Search results for: Patient record data

Authors: Jun-ichi Ochiai, Itsuki Noda, Ryo Kanamori, Keiji Hirata, Hitoshi Matsubara, Hideyuki Nakashima

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We conducted exhaustive simulations for data assimilation and evaluation of service quality for various setting in a new shared transportation system, called SAVS. Computational social simulation is a key technology to design recent social services like SAVS as new transportation service. One open issue in SAVS was to determine the service scale through the social simulation. Using our exhaustive simulation framework, OACIS, we did data-assimilation and evaluation of effects of SAVS based on actual tax-use data at Tajimi city, Japan. Finally, we get the conditions to realize the new service in a reasonable service quality.

Keywords: on-demand bus sytem, social simulation, data assimilation, exhaustive simulation

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Authors: Amirul Adlan, Motaz AlAqeel, Scott Evans, Vaiyapuri sumathi, Mark Davies, Rajesh Botchu

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Background & Objectives: Osteoid osteoma (OO) is a benign tumor of the bone commonly found in childhood and adolescence, causing bone pain, especially during the night. CT-guided radiofrequency ablation (RFA) is currently the mainstay treatment for OO. There is currently no literature reporting the outcomes of OO following RFA based on the histological presence of a nidus seen on a biopsy taken at the time of RFA. The primary aim of this study was to compare the clinical outcomes of OO between the group of patients with the presence of nidus on biopsy samples from RFA with those without nidus. Secondly, we aimed to examine other factors that may affect the outcomes of OO, reflecting our experience as a tertiary orthopedic oncology center. Methods: We retrospectively reviewed 88 consecutive patients diagnosed with osteoid osteoma treated with RFA between November 2005 and March 2015, consisting of 63 males (72%) and 25 females (28%). Sixty-six patients (75%) had nidus present in their biopsy samples. Patients’ mean age was 17.6 years (4-53). The median duration of follow-up was 12.5 months (6-20.8). Lesions were located in the appendicular skeleton in seventy-nine patients (90%), while nine patients (10%) had an OO in the axial skeleton. Outcomes assessed were based on patients’ pain alleviation (partial, complete, or no pain improvement) and the need for further interventions. Results: Pain improvement in the patient group with nidus in the histology sample was significantly better than in the group without nidus (OR 7.4, CI 1.35-41.4, p=0.021). The patient group with nidus on biopsy demonstrated less likelihood of having a repeat procedure compared to the group without nidus(OR 0.092, CI 0.016-0.542, p=0.008). Our study showed significantly better outcomes in pain improvement in appendicular lesions compared to the axially located lesions (p = 0.005). Patients with spinal lesions tend to have relatively poor pain relief than those with appendicular or pelvic lesions (p=0.007). Conclusions: Patients with nidus on histology had better pain alleviation compared to patients without nidus. The histological presence of nidus significantly reduces the chance of repeat interventions. The pain alleviation of osteoid osteoma following RFA is better in patients with appendicular lesions than spinal or axially located lesions.

Keywords: osteoid osteoma, benign tumour, radiofrequency ablation, oncology

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Maria Malliarou, Efrossini Lyraraki, Pavlos Sarafis, Theodosios Paralikas, Styliani Kotrotsiou, Evangelia Kotrotsiou, Mairy Gouva

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Women with breast cancer have to adapt to physical malformations, side effects of chemotherapy, emotional insecurity, and changes in social roles. Inability to recognize the co-morbidity of psychiatric conditions can have an aggravating effect on patient compliance in therapeutic interventions, resulting in treatment delays and an impact on overall survival. The purpose of this study was to identify the quality of life of breast cancer patients undergoing external radiation therapy and to correlate it with depression and anxiety. Patients were asked to respond to an anonymous questionnaire with general demographic and clinical questions, followed by the EORTCQLQ-C30 questionnaire for assessing the quality of life of patients with breast cancer. Hospital Anxiety and Depression Scale (HADS) as well as the Depression, Anxiety and Stress Scale (DASS-21) was also administered. The statistical analysis of the data was done in IBM SPSS. Results indicated that the incidence of anxiety and depression in breast cancer patients is high both in HADS (37.5 % with mild to moderate depression and 62.5 % with significant to severe depression) and DASS - 21 (39.2 % mild to moderate depression and 60.8 % significant to severe) scales. The correlation of anxiety and depression with life quality was negative for HADS (r = -, 810, p = .000) as well as for DASS-21 (r = -, 682, p = .000). The psychological impact of breast cancer on patients is important. Its correlation with the quality of life may lead to better tolerance to treatment and better effectiveness of the therapeutic approach.

Keywords: anxiety, breast cancer, depression, quality of life

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Authors: Oluwole Enoch Adeniran

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Peace studies and conflict resolution; though a relatively new discipline had attracted scholars from far and near. It had enhanced a purposeful training of mind of young adult among other categories of learners. It provides a platform through which university under-graduates and post-graduates students are exposed to the rudiments of peace building, peacemaking and peace keeping towards a successful conflict resolution. The paper historicizes peace education as most desirable in any human society that desired development. It aims at educating children and young adults in the dynamics of peaceful conflicts resolution at home, in school and communities (states) throughout the world for a purposeful technological development. It also aims at exposing students to the nature of conflict and how to manage and resolve conflicts in order to promote national unity for meaningful development. The paper argues that, for a state to record any meaningful socio-economic, political and technological development; a conducive and peaceful atmosphere must be put in place. This theoretical paper emerged in the context of historical specificities of conflict resolution from a general conceptual framework. It then concludes with suggestions on the modes of conflict prevention, conflict management and conflict resolution for an ideal technologically advanced society.

Keywords: history, education, peace, unity, technology and development

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Authors: Antonio Montes, Antonio Cozar, Clara Pereyra, Diego Valor, Enrique Martinez de la Ossa

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Tissues and organs can be damaged because of traumatism, congenital illnesses, or cancer and the traditional therapeutic alternatives, such as surgery, cannot usually completely repair the damaged tissues. Tissue engineering allows regeneration of the patient's tissues, reducing the problems caused by the traditional methods. Scaffolds, polymeric structures with interconnected porosity, can be promoted the proliferation and adhesion of the patient’s cells in the damaged area. Furthermore, by means of impregnation of the scaffold with beneficial active substances, tissue regeneration can be induced through a drug delivery process. The objective of the work is the fabrication of a PVA scaffold coated with Gallic Acid and polypyrrole through a one-step foaming and impregnation process using the SSI technique (Supercritical Solvent Impregnation). In this technique, supercritical CO₂ penetrates into the polymer chains producing the plasticization of the polymer. In the depressurization step a CO₂ cellular nucleation and growing to take place to an interconnected porous structure of the polymer. The foaming process using supercritical CO₂ as solvent and expansion agent presents advantages compared to the traditional scaffolds’ fabrication methods, such as the polymer’s high solubility in the solvent or the possibility of carrying out the process at a low temperature, avoiding the inactivation of the active substance. In this sense, the supercritical CO₂ avoids the use of organic solvents and reduces the solvent residues in the final product. Moreover, this process does not require long processing time that could cause the stratification of substance inside the scaffold reducing the therapeutic efficiency of the formulation. An experimental design has been carried out to optimize the SSI technique operating conditions, as well as a study of the morphological characteristics of the scaffold for its use in tissue engineerings, such as porosity, conductivity or the release profiles of the active substance. It has been proved that the obtained scaffolds are partially porous, conductors of electricity and are able to release Gallic Acid in the long term.

Keywords: scaffold, foaming, supercritical, PVA, polypyrrole, gallic acid

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Authors: Nafiseh Mirabdolhosseini, Jerry Zhou, Vincent Ho

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The dramatic global rise in acid reflux has also led to oesophageal adenocarcinoma (OAC) becoming the fastest-growing cancer in developed countries. While gastroscopy with biopsy is used to diagnose OAC patients, this labour-intensive and expensive process is not suitable for population screening. This study aims to design, develop, and implement a minimally invasive system to capture optical data of the distal oesophagus for rapid screening of potential abnormalities. To develop the system and understand user requirements, a user-centric approach was employed by utilising co-design strategies. Target users’ segments were identified, and 38 patients and 14 health providers were interviewed. Next, the technical requirements were developed based on consultations with the industry. A minimally invasive optical system was designed and developed considering patient comfort. This system consists of the sensing catheter, controller unit, and analysis program. Its procedure only takes 10 minutes to perform and does not require cleaning afterward since it has a single-use catheter. A prototype system was evaluated for safety and efficacy for both laboratory and clinical performance. This prototype performed successfully when submerged in simulated gastric fluid without showing evidence of erosion after 24 hours. The system effectively recorded a video of the mid-distal oesophagus of a healthy volunteer (34-year-old male). The recorded images were used to develop an automated program to identify abnormalities in the distal oesophagus. Further data from a larger clinical study will be used to train the automated program. This system allows for quick visual assessment of the lower oesophagus in primary care settings and can serve as a screening tool for oesophageal adenocarcinoma. In addition, this system is able to be coupled with 24hr ambulatory pH monitoring to better correlate oesophageal physiological changes with reflux symptoms. It also can provide additional information on lower oesophageal sphincter functions such as opening times and bolus retention.

Keywords: endoscopy, MedTech, oesophageal adenocarcinoma, optical system, screening tool

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Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

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Authors: Rajesh Kumar G

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A challenging aspect of any clinical trial is to carefully plan the study design to meet the study objective in optimum way and to validate the assumptions made during protocol designing. And when it is a pediatric study, there is the added challenge of stringent guidelines and difficulty in recruiting the necessary subjects. Unlike adult trials, there is not much historical data available for pediatrics, which is required to validate assumptions for planning pediatric trials. Typically, pediatric studies are initiated as soon as approval is obtained for a drug to be marketed for adults, so with the adult study historical information and with the available pediatric pilot study data or simulated pediatric data, the pediatric study can be well planned. Generalizing the historical adult study for new pediatric study is a tedious task; however, it is possible by integrating various statistical techniques and utilizing the advantage of hybrid study design, which will help to achieve the study objective in a smoother way even with the presence of many constraints. This research paper will explain how well the hybrid study design can be planned along with integrated technique (SEV) to plan the pediatric study; In brief the SEV technique (Simulation, Estimation (using borrowed adult data and applying Bayesian methods)) incorporates the use of simulating the planned study data and getting the desired estimates to Validate the assumptions.This method of validation can be used to improve the accuracy of data analysis, ensuring that results are as valid and reliable as possible, which allow us to make informed decisions well ahead of study initiation. With professional precision, this technique based on the collected data allows to gain insight into best practices when using data from historical study and simulated data alike.

Keywords: adaptive design, simulation, borrowing data, bayesian model

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Authors: Arslan Ellahi, Syed Amjad Hussain

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Software Testing is a vital process in software development life cycle. We can attain the quality of software after passing it through software testing phase. We have tried to find out automatic test data generation techniques that are a key research area of software testing to achieve test automation that can eventually decrease testing time. In this paper, we review some of the approaches presented in the literature which use evolutionary search based algorithms like Genetic Algorithm, Particle Swarm Optimization (PSO), etc. to validate the test data generation process. We also look into the quality of test data generation which increases or decreases the efficiency of testing. We have proposed test data generation techniques for model-based testing. We have worked on tuning and fitness function of PSO algorithm.

Keywords: search based, evolutionary algorithm, particle swarm optimization, genetic algorithm, test data generation

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Authors: Claudineia Brazil, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Rafael Haag

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Renewable energy sources are dependent on climatic variability, so for adequate energy planning, observations of the meteorological variables are required, preferably representing long-period series. Despite the scientific and technological advances that meteorological measurement systems have undergone in the last decades, there is still a considerable lack of meteorological observations that form series of long periods. The reanalysis is a system of assimilation of data prepared using general atmospheric circulation models, based on the combination of data collected at surface stations, ocean buoys, satellites and radiosondes, allowing the production of long period data, for a wide gamma. The third generation of reanalysis data emerged in 2010, among them is the Climate Forecast System Reanalysis (CFSR) developed by the National Centers for Environmental Prediction (NCEP), these data have a spatial resolution of 0.50 x 0.50. In order to overcome these difficulties, it aims to evaluate the performance of solar radiation estimation through alternative data bases, such as data from Reanalysis and from meteorological satellites that satisfactorily meet the absence of observations of solar radiation at global and/or regional level. The results of the analysis of the solar radiation data indicated that the reanalysis data of the CFSR model presented a good performance in relation to the observed data, with determination coefficient around 0.90. Therefore, it is concluded that these data have the potential to be used as an alternative source in locations with no seasons or long series of solar radiation, important for the evaluation of solar energy potential.

Keywords: climate, reanalysis, renewable energy, solar radiation

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Authors: Ruairi J. McMillan

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Introduction: This critical analysis aims to ascertain how well neuroanatomical aetiologies are communicated within 20 case reports of aphasia. Neuroanatomical visualisations based on dissected brain specimens were produced and combined with white matter tract and vascular taxonomies of function in order to address the most consistently underreported features found within the aphasic case study reports. Together, these approaches are intended to integrate aphasiological knowledge from the past 20 years with aphasiological diagnostics, and to act as prototypal resources for both researchers and clinical professionals. The medico-legal precedent for aphasia diagnostics under Canadian, US and UK case law and the neuroimaging/neurological diagnostics relative to the functional capacity of aphasic patients are discussed in relation to the major findings of the literary analysis, neuroimaging protocols in clinical use today, and the neuroanatomical aetiologies of different aphasias. Basic Methodology: Literature searches of relevant scientific databases (e.g, OVID medline) were carried out using search terms such as aphasia case study (year) & stroke induced aphasia case study. A series of 7 diagnostic reporting criteria were formulated, and the resulting case studies were scored / 7 alongside clinical stroke criteria. In order to focus on the diagnostic assessment of the patient’s condition, only the case report proper (not the discussion) was used to quantify results. Statistical testing established if specific reporting criteria were associated with higher overall scores and potentially inferable increases in quality of reporting. Statistical testing of whether criteria scores were associated with an unclear/adjusted diagnosis were also tested, as well as the probability of a given criterion deviating from an expected estimate. Major Findings: The quantitative analysis of neuroanatomically driven diagnostics in case studies of aphasia revealed particularly low scores in the connection of neuroanatomical functions to aphasiological assessment (10%), and in the inclusion of white matter tracts within neuroimaging or assessment diagnostics (30%). Case studies which included clinical mention of white matter tracts within the report itself were distributed among higher scoring cases, as were case studies which (as clinically indicated) related the affected vascular region to the brain parenchyma of the language network. Concluding Statement: These findings indicate that certain neuroanatomical functions are integrated less often within the patient report than others, despite a precedent for well-integrated neuroanatomical aphasiology also being found among the case studies sampled, and despite these functions being clinically essential in diagnostic neuroimaging and aphasiological assessment. Therefore, ultimately the integration and specificity of aetiological neuroanatomy may contribute positively to the capacity and autonomy of aphasic patients as well as their clinicians. The integration of a full aetiological neuroanatomy within the reporting of aphasias may improve patient outcomes and sustain autonomy in the event of medico-ethical investigation.

Keywords: aphasia, language network, functional neuroanatomy, aphasiological diagnostics, medico-legal ethics

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Authors: A. Fordham, A. Hudson

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Background: Studies have suggested 16-25% of normotensive trauma patients with no clinical signs of shock have abnormal lactate and BD readings evidencing shock; a phenomenon known as occult hypoperfusion (OH). In light of the scarce quantity of evidence currently documenting OH, this study aimed to identify the prevalence of OH in the pre-hospital setting and explore ways to improve its identification and management. Methods: A quantitative retrospective data analysis was carried out on 75 sets of patient records for trauma patients treated by Kent Surrey Sussex Air Ambulance Trust between November 2013 and October 2014. The KSS HEMS notes and subsequent ED notes were collected. Trends between patients’ SBP on the scene, whether or not they received PRBCs on the scene as well as lactate and BD readings in the ED were assessed. Patients’ KSS HEMS notes written by the HEMS crew were also reviewed and recorded. Results: -Suspected OH was identified in 7% of the patients who did not receive PRBCs in the pre-hospital phase. -SBP heavily influences the physicians’ decision of whether or not to transfuse PRBCs in the pre-hospital phase. Preliminary conclusions: OH is an under-studied and underestimated phenomenon. We suggest a prospective trial is carried out to evaluate whether detecting trauma patients’ tissue perfusion status in the pre-hospital phase using portable devices capable of measuring serum BD and/or lactate could aid more accurate detection and management of all haemorrhaging trauma patients, including patients with OH.

Keywords: occult hypoperfusion, PRBC transfusion, point of care testing, pre-hospital emergency medicine, trauma

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Andrew Anis Fakhrey Mosaad

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Objectives: Physiotherapy in the intensive care unit (ICU) improves patient outcomes. We aimed to determine the characteristics of physiotherapy practice and critical barriers to applying physiotherapy in ICUs. Materials and Methods: A 54-item survey for determining the characteristics physiotherapists and physiotherapy applications in the ICU was developed. The survey was electronically sent to potential participants through the Turkish Physiotherapy Association network. Sixty-five physiotherapists (47F and 18M; 23–52 years; ICU experience: 6.0±6.2 years) completed the survey. The data were analyzed using quantitative and qualitative methods. Results: The duration of ICU practice was 3.51±2.10 h/day. Positioning (90.8%), active exercises (90.8%), breathing exercises (89.2%), passive exercises (87.7%), and percussion (87.7%) were the most commonly used applications. The barriers were related to physiotherapists (low level of employment and practice, lack of shift); patients (unwillingness, instability, participation restriction); teamwork (lack of awareness and communication); equipment (inadequacy, non-priority to purchase); and legal (reimbursement, lack of direct physiotherapy access, non-recognition of autonomy) procedures. Conclusion: The most common interventions were positioning, active, passive, breathing exercises, and percussion. Critical barriers toward physiotherapy are multifactorial and related to physiotherapists, patients, teams, equipment, and legal procedures. Physiotherapist employment, service maintenance, and multidisciplinary teamwork should be considered for physiotherapy effectiveness in ICUs.

Keywords: intensive care units, physical therapy, physiotherapy, exercises

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Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Jayesh M. Patel, Bharat P. Modi

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The availability of on mobile devices that can invoke the demonstrated that the total data demand from users is far higher than previously articulated by measurements based solely on a cellular-centric view of smart-phone usage. The ratio of Wi-Fi to cellular traffic varies significantly between countries, This paper is shown the compression between the cellular data usage and Wi-Fi data usage by the user. This strategy helps operators to understand the growing importance and application of yield management strategies designed to squeeze maximum returns from their investments into the networks and devices that enable the mobile data ecosystem. The transition from unlimited data plans towards tiered pricing and, in the future, towards more value-centric pricing offers significant revenue upside potential for mobile operators, but, without a complete insight into all aspects of smartphone customer behavior, operators will unlikely be able to capture the maximum return from this billion-dollar market opportunity.

Keywords: cellular, Wi-Fi, mobile, smart phone

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Authors: Breanna Wright, Peter Bragge

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Diagnostic error rates in healthcare are approximately 10% of cases. Diagnostic errors can cause patient harm due to inappropriate, inadequate or delayed treatment, and such errors contribute heavily to medical liability claims globally. Therefore, addressing diagnostic error is a high priority. In most cases, diagnostic errors are the result of faulty information synthesis rather than lack of knowledge. Specifically, the majority of diagnostic errors involve cognitive factors, and in particular, cognitive biases. Emergency Departments are an environment with heightened risk of diagnostic error due to time and resource pressures, a frequently chaotic environment, and patients arriving undifferentiated and with minimal context. This project aimed to develop a behavioural, evidence-informed intervention to reduce diagnostic error in Emergency Departments through co-design with emergency physicians, insurers, researchers, hospital managers, citizens and consumer representatives. The Forum Process was utilised to address this aim. This involves convening a small (4 – 6 member) expert panel to guide a focused literature and practice review; convening of a 10 – 12 person citizens panel to gather perspectives of laypeople, including those affected by misdiagnoses; and a 18 – 22 person structured stakeholder dialogue bringing together representatives of the aforementioned stakeholder groups. The process not only provides in-depth analysis of the problem and associated behaviours, but brings together expertise and insight to facilitate identification of a behaviour change intervention. Informed by the literature and practice review, the Citizens Panel focused on eliciting the values and concerns of those affected or potentially affected by diagnostic error. Citizens were comfortable with diagnostic uncertainty if doctors were honest with them. They also emphasised the importance of open communication between doctors and patients and their families. Citizens expect more consistent standards across the state and better access for both patients and their doctors to patient health information to avoid time-consuming re-taking of long patient histories and medication regimes when re-presenting at Emergency Departments and to reduce the risk of unintentional omissions. The structured Stakeholder Dialogue focused on identifying a feasible behavioural intervention to review diagnoses in Emergency Departments. This needed to consider the role of cognitive bias in medical decision-making; contextual factors (in Victoria, there is a legislated 4-hour maximum time between ED triage and discharge / hospital admission); resource availability; and the need to ensure the intervention could work in large metropolitan as well as small rural and regional ED settings across Victoria. The identified behavioural intervention will be piloted in approximately ten hospital EDs across Victoria, Australia. This presentation will detail the findings of all review and consultation activities, describe the behavioural intervention developed and present results of the pilot trial.

Keywords: behavioural intervention, cognitive bias, decision-making, diagnostic error

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Isha Saxena, Behzad Kazemtabrizi, Matthias C. M. Troffaes, Christopher Crabtree

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The calculations done at the beginning of the life of a wind farm are rarely reliable, which makes it important to conduct research and study the failure and repair rates of the wind turbines under various conditions. This miscalculation happens because the current models make a simplifying assumption that the failure/repair rate remains constant over time. This means that the reliability function is exponential in nature. This research aims to create a more accurate model using sensory data and a data-driven approach. The data cleaning and data processing is done by comparing the Power Curve data of the wind turbines with SCADA data. This is then converted to times to repair and times to failure timeseries data. Several different mathematical functions are fitted to the times to failure and times to repair data of the wind turbine components using Maximum Likelihood Estimation and the Posterior expectation method for Bayesian Parameter Estimation. Initial results indicate that two parameter Weibull function and exponential function produce almost identical results. Further analysis is being done using the complex system analysis considering the failures of each electrical and mechanical component of the wind turbine. The aim of this project is to perform a more accurate reliability analysis that can be helpful for the engineers to schedule maintenance and repairs to decrease the downtime of the turbine.

Keywords: reliability, bayesian parameter inference, maximum likelihood estimation, weibull function, SCADA data

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Authors: Madeleine Cox

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Objective; analyse patient choices in management of first trimester miscarriage, rates of complications including repeat procedure. Design: all first trimester miscarriages from a tertiary institution on the Gold Coast in a 6 month time frame (July to December 2021) were reviewed, including choice of management, histopathology, any representations or admissions, and potential complications. Results: a total of 224 first trimester miscarriages were identified. Of these, 183 (81%) opted to have surgical management in the first instance. Of the remaining patients, 18 (8%) opted to have medical management, and 28 (12.5%) opted to have expectant management. In total, 33(15%) patients required a repeat treatment for retained products. 1 had medical management for a small volume PROC post suction curette. A significant number of these patients initially opted for medical management but then elected to have shorter follow up than usual and went on to have retained products noted. 5 women who had small volumes of RPOC post medical or surgical management had repeat suction curette, however, had very small volumes of products on scan and on curette and may have had a good result with repeated misoprostol administration. It is important to note that whilst a common procedure, suction curettes are not without risk. 2 women had significant blood loss of 1L and 1.5L. A third women had a uterine perforation, a rare but recognised complication, she went on to require a laparoscopy which identified a small serosal bowel injury which was closed by the colorectal team. Conclusion: Management of first trimester miscarriage should be guided by patient preference. It is important to be able to provide patients with their choice of management, however, it is also important to have a good understanding of the risks of each management choice, chances of repeated procedure, appropriate time frame for follow up. Women who choose to undertake medical or expectant management should be supported through this time, with appropriate time frame between taking misoprostol and repeat scan so that the true effects can be evaluated. Patients returning for scans within 2-3 days are more likely to be booked for further surgery, however, may reflect patients who did not have adequate counselling or simply changed their mind on their preferred management options.

Keywords: miscarriage, gynaecology, obstetrics, first trimester

Procedia PDF Downloads 95

Authors: Moon Jung Kim, Guil Rhim

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The clinical sensitivity of the “VEUPLEXTM TBI assay”, a clinical trial medical device, in mild traumatic brain injury was 28.6% (95% CI, 19.7%-37.5%), and the clinical specificity was 94.0% (95% CI, 89.3%). -98.7%). In addition, when the results analyzed by marker were put together, the sensitivity was higher when interpreting the two tests together than the two tests, UCHL1 and GFAP alone. Additionally, when sensitivity and specificity were analyzed based on CT results for the mild traumatic brain injury patient group, the clinical sensitivity for 2 CT-positive cases was 50.0% (95% CI: 1.3%-98.7%), and 19 CT-negative cases. The clinical specificity for cases was 68.4% (95% CI: 43.5% - 87.4%). Since the low clinical sensitivity for the two CT-positive cases was not statistically significant due to the small number of samples analyzed, it was judged necessary to secure and analyze more samples in the future. Regarding the clinical specificity analysis results for 19 CT-negative cases, there were a large number of patients who were actually clinically diagnosed with mild traumatic brain injury but actually received a CT-negative result, and about 31.6% of them showed abnormal results on VEUPLEXTM TBI assay. Although traumatic brain injury could not be detected in 31.6% of the CT scans, the possibility of actually suffering a mild brain injury could not be ruled out, so it was judged that this could be confirmed through follow-up observation of the patient. In addition, among patients with mild traumatic brain injury, CT examinations were not performed in many cases because the symptoms were very mild, but among these patients, about 25% or more showed abnormal results in the VEUPLEXTM TBI assay. In fact, no damage is observed with the naked eye immediately after traumatic brain injury, and traumatic brain injury is not observed even on CT. But in some cases, brain hemorrhage may occur (delayed cerebral hemorrhage) after a certain period of time, so the patients who did show abnormal results on VEUPLEXTM TBI assay should be followed up for the delayed cerebral hemorrhage. In conclusion, it was judged that it was difficult to judge mild traumatic brain injury with the VEUPLEXTM TBI assay only through clinical findings without CT results, that is, based on the GCS value. Even in the case of CT, it does not detect all mild traumatic brain injury, so it is difficult to necessarily judge that there is no traumatic brain injury, even if there is no evidence of traumatic brain injury in CT. And in the long term, more patients should be included to evaluate the usefulness of the VEUPLEXTM TBI assay in the detection of microscopic traumatic brain injuries without using CT.

Keywords: brain injury, traumatic brain injury, GFAP, UCHL1

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Authors: Muhammad Shafiq

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In accelerated life testing approaches life time data is obtained under various conditions which are considered more severe than usual condition. Classical techniques are based on obtained precise measurements, and used to model variation among the observations. In fact, there are two types of uncertainty in data: variation among the observations and the fuzziness. Analysis techniques, which do not consider fuzziness and are only based on precise life time observations, lead to pseudo results. This study was aimed to examine the behavior of empirical acceleration functions using fuzzy lifetimes data. The results showed an increased fuzziness in the transformed life times as compare to the input data.

Keywords: acceleration function, accelerated life testing, fuzzy number, non-precise data

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Authors: Cherif Y., Ghariani R., Derbal S., Farhati S., Ben Dahmen F., Abdallah M.

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Introduction: Tuberculosis (TBC) is a frequent infection and is still a major public health problem in Tunisia. The aim of this work is to focus on diagnostic and therapeutic characteristics of TBC in patients referred to our internal medicine department. Patients and Methods: The study was retrospective and descriptive of a cohort of consecutive cases treated from January 2016 to December 2019, collecting patients with latent or patent TBC. Twenty-eight medical records of adults diagnosed with TBC were reviewed. Results: Twenty-eight patients, including 18 women and 10 men, were diagnosed with TBC. Their mean age is 48 years (range: 22-78 years). Five patients have a medical history of diabetes mellitus, 1 patient was followed for systemic lupus erythematosus treated with corticosteroids and immunosuppressant drugs, and another was treated with corticosteroids for Mac Duffy syndrome. The TBC is latent in 12 cases and patent in 16 cases. The most common symptoms were fever and weight loss and were found in 10 cases, a cough in 2 cases, sputum in 3 cases, lymph nodes in 4 cases, erythema nodosum in 2 cases, and neurological signs in 3 cases. Lymphopenia is noticed in 3 cases and a biological inflammatory syndrome in 18 of the cases. The purified protein derivate reaction was positive in 17 cases, anergic in 3 cases, negative in 5 cases, and not done in 3 cases. The acid-fast bacilli stain culture was strongly positive in one patient. The histopathological study was conclusive in 11 patients and showed granulomatosis with caseous necrosis. TBC was pulmonary in 7 patients, lymph node in 7 cases, peritoneal in 7 cases, digestive in 1 case, neuromeningeal in 3 cases, and thyroïd in 1 case. Seven patients had multifocal TBC. All the patients received anti-tuberculosis treatment with a mean duration of 8 months with no failure or relapse with an average follow-up time of 10.58 months. Conclusion: Diagnosis and management of TBC remain essential to avoid serious complications. The survey is necessary to ensure timely detection and treatment of infected adults to decrease its incidence. The best treatment remains preventive through vaccination and improving social and economic conditions.

Keywords: tuberculosis, infection, autoimmune disease, granulomatosis

Procedia PDF Downloads 184

Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

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Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

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Authors: Feras Hanandeh, Izzat Alsmadi, Muhammad M. Kwafha

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Prefix trees or tries are data structures that are used to store data or index of data. The goal is to be able to store and retrieve data by executing queries in quick and reliable manners. In principle, the structure of the trie depends on having letters in nodes at the different levels to point to the actual words in the leafs. However, the exact structure of the trie may vary based on several aspects. In this paper, we evaluated different structures for building tries. Using datasets of words of different sizes, we evaluated the different forms of trie structures. Results showed that some characteristics may impact significantly, positively or negatively, the size and the performance of the trie. We investigated different forms and structures for the trie. Results showed that using an array of pointers in each level to represent the different alphabet letters is the best choice.

Keywords: data structures, indexing, tree structure, trie, information retrieval

Procedia PDF Downloads 449

Authors: Elizaveta Petelina, Anton Sizo

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Environmental remediation projects deal with a wide spectrum of data, including data collected during site assessment, execution of remediation activities, and environmental monitoring. Therefore, an appropriate data management is required as a key factor for well-grounded decision making. The Environmental Data Management System (EDMS) was developed to address all necessary data management aspects, including efficient data handling and data interoperability, access to historical and current data, spatial and temporal analysis, 2D and 3D data visualization, mapping, and data sharing. The system focuses on support of well-grounded decision making in relation to required mitigation measures and assessment of remediation success. The EDMS is a combination of enterprise and desktop level data management and Geographic Information System (GIS) tools assembled to assist to environmental remediation, project planning, and evaluation, and environmental monitoring of mine sites. EDMS consists of seven main components: a Geodatabase that contains spatial database to store and query spatially distributed data; a GIS and Web GIS component that combines desktop and server-based GIS solutions; a Field Data Collection component that contains tools for field work; a Quality Assurance (QA)/Quality Control (QC) component that combines operational procedures for QA and measures for QC; Data Import and Export component that includes tools and templates to support project data flow; a Lab Data component that provides connection between EDMS and laboratory information management systems; and a Reporting component that includes server-based services for real-time report generation. The EDMS has been successfully implemented for the Project CLEANS (Clean-up of Abandoned Northern Mines). Project CLEANS is a multi-year, multimillion-dollar project aimed at assessing and reclaiming 37 uranium mine sites in northern Saskatchewan, Canada. The EDMS has effectively facilitated integrated decision-making for CLEANS project managers and transparency amongst stakeholders.

Keywords: data management, environmental remediation, geographic information system, GIS, decision making

Procedia PDF Downloads 156

Authors: Arup Dutta, Badrul Alam, Sayed M. Wazed, Taslima Newaz, Srobonti Dutta

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Background: In today's world, with modern science and contemporary technology, a lot of diseases may be quickly identified and ruled out, but children's fever of unknown origin (FUO) still presents diagnostic difficulties in clinical settings. Any fever that reaches 38 °C and lasts for more than seven days without a known cause is now classified as a fever of unknown origin (FUO). Despite tremendous progress in the medical sector, fever of unknown origin, or FOU, persists as a major health issue and a major contributor to morbidity and mortality, particularly in children, and its spectrum is sometimes unpredictable. The etiology is influenced by geographic location, age, socioeconomic level, frequency of antibiotic resistance, and genetic vulnerability. Since there are currently no known diagnostic algorithms, doctors are forced to evaluate each patient one at a time with extreme caution. A persistent fever poses difficulties for both the patient and the doctor. This prospective observational study was carried out in a Bangladeshi tertiary care hospital from June 2018 to May 2019 with the goal of identifying the epidemiological patterns of fever of unknown origin in pediatric patients. Methods: It was a hospital-based prospective observational study carried out on 106 children (between 2 months and 12 years) with prolonged fever of >38.0 °C lasting for more than 7 days without a clear source. Children with additional chronic diseases or known immunodeficiency problems were not allowed. Clinical practices that helped determine the definitive etiology were assessed. Initial testing included a complete blood count, a routine urine examination, PBF, a chest X-ray, CRP measurement, blood cultures, serology, and additional pertinent investigations. The analysis focused mostly on the etiological results. The standard program SPSS 21 was used to analyze all of the study data. Findings: A total of 106 patients identified as having FUO were assessed, with over half (57.5%) being female and the majority (40.6%) falling within the 1 to 3-year age range. The study categorized the etiological outcomes into five groups: infections, malignancies, connective tissue conditions, miscellaneous, and undiagnosed. In the group that was being studied, infections were found to be the main cause in 44.3% of cases. Undiagnosed cases came in at 31.1%, cancers at 10.4%, other causes at 8.5%, and connective tissue disorders at 4.7%. Hepato-splenomegaly was seen in people with enteric fever, malaria, acute lymphoid leukemia, lymphoma, and hepatic abscesses, either by itself or in combination with other conditions. About 53% of people who were not diagnosed also had hepato-splenomegaly at the same time. Conclusion: Infections are the primary cause of PUO (pyrexia of unknown origin) in children, with undiagnosed cases being the second most common cause. An incremental approach is beneficial in the process of diagnosing a condition. Non-invasive examinations are used to diagnose infections and connective tissue disorders, while invasive investigations are used to diagnose cancer and other ailments. According to this study, the prevalence of undiagnosed diseases is still remarkable, so extensive historical analysis and physical examinations are necessary in order to provide a precise diagnosis.

Keywords: children, diagnostic challenges, fever of unknown origin, pediatric fever, undiagnosed diseases

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Authors: Bharat Singh Om Prakash Vyas

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Now a day’s applications deal with High Dimensional Data have tremendously used in the popular areas. To tackle with such kind of data various approached has been developed by researchers in the last few decades. To tackle with such kind of data various approached has been developed by researchers in the last few decades. One of the problems with the NMF approaches, its randomized valued could not provide absolute optimization in limited iteration, but having local optimization. Due to this, we have proposed a new approach that considers the initial values of the decomposition to tackle the issues of computationally expensive. We have devised an algorithm for initializing the values of the decomposed matrix based on the PSO (Particle Swarm Optimization). Through the experimental result, we will show the proposed method converse very fast in comparison to other row rank approximation like simple NMF multiplicative, and ACLS techniques.

Keywords: ALS, NMF, high dimensional data, RMSE

Procedia PDF Downloads 339

Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

Procedia PDF Downloads 118