Search results for: predictive medicine

Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

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A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: S. H. Atef, N. H. Mahmoud, S. Abdrahman, A. Fattoh

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Background: The aim of the study is to assess the diagnostic value of fibrotest and hyaluronic acid in discriminate between insignificant and significant fibrosis. Also, to find out if these parameters could replace liver biopsy which is currently used for selection of chronic hepatitis C patients eligible for antiviral therapy. Study design: This study was conducted on 52 patients with HCV RNA detected by polymerase chain reaction (PCR) who had undergone liver biopsy and attending the internal medicine clinic at Ain Shams University Hospital. Liver fibrosis was evaluated according to the METAVIR scoring system on a scale of F0 to F4. Biochemical markers assessed were: alpha-2 macroglobulin (α2-MG), apolipoprotein A1 (Apo-A1), haptoglobin, gamma-glutamyl transferase (GGT), total bilirubin (TB) and hyaluronic acid (HA). The fibrotest score was computed after adjusting for age and gender. Predictive values and ROC curves were used to assess the accuracy of fibrotest and HA results. Results: For fibrotest, the observed area under curve for the discrimination between minimal or no fibrosis (F0-F1) and significant fibrosis (F2-F4) was 0.6736 for cutoff value 0.19 with sensitivity of 84.2% and specificity of 85.7%. For HA, the sensitivity was 89.5% and specificity was 85.7% and area under curve was 0.540 at the best cutoff value 71 mg/dL. Multi-use of both parameters, HA at 71 mg/dL with fibrotest score at 0.22 give a sensitivity 89.5%, specificity 100 and efficacy 92.3% (AUC 0.895). Conclusion: The use of both fibrotest score and HA could be as alternative to biopsy in most patients with chronic hepaitis C putting in consideration some limitations of the proposed markers in evaluating liver fibrosis.

Keywords: fibrotest, liver fibrosis, HCV RNA, biochemical markers

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Authors: Prashant Verma

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Commercial banks are facing immense pressure, including financial disintermediation, interest rate volatility and digital ways of finance. Retaining an existing customer is 5 to 25 less expensive than acquiring a new one. This paper explores customer churn prediction, based on various statistical & machine learning models and uses under-sampling, to improve the predictive power of these models. The results show that out of the various machine learning models, Random Forest which predicts the churn with 78% accuracy, has been found to be the most powerful model for the scenario. Customer vintage, customer’s age, average balance, occupation code, population code, average withdrawal amount, and an average number of transactions were found to be the variables with high predictive power for the churn prediction model. The model can be deployed by the commercial banks in order to avoid the customer churn so that they may retain the funds, which are kept by savings bank (SB) customers. The article suggests a customized campaign to be initiated by commercial banks to avoid SB customer churn. Hence, by giving better customer satisfaction and experience, the commercial banks can limit the customer churn and maintain their deposits.

Keywords: savings bank, customer churn, customer retention, random forests, machine learning, under-sampling

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Authors: Saurabh Kumar

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In the fiercely competitive landscape of e-commerce, understanding and mitigating customer churn has become paramount for sustainable business growth. This paper presents a thorough investigation into the application of machine learning techniques for churn prediction in e-commerce, aiming to provide actionable insights for businesses seeking to enhance customer retention strategies. We conduct a comparative study of various machine learning algorithms, including traditional statistical methods and ensemble techniques, leveraging a rich dataset sourced from Kaggle. Through rigorous evaluation, we assess the predictive performance, interpretability, and scalability of each method, elucidating their respective strengths and limitations in capturing the intricate dynamics of customer churn. We identified the XGBoost classifier to be the best performing. Our findings not only offer practical guidelines for selecting suitable modeling approaches but also contribute to the broader understanding of customer behavior in the e-commerce domain. Ultimately, this research equips businesses with the knowledge and tools necessary to proactively identify and address churn, thereby fostering long-term customer relationships and sustaining competitive advantage.

Keywords: customer churn, e-commerce, machine learning techniques, predictive performance, sustainable business growth

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Authors: Mani Venkatesh, Catarina Delgado, Purvishkumar Patel

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The sustainable supply chain is gaining popularity among practitioners because of increased environmental degradation and stakeholder awareness. On the other hand supply chain, risk management is very crucial for the practitioners as it potentially disrupts supply chain operations. Prediction and addressing the risk caused by social issues in the supply chain is paramount importance to the sustainable enterprise. More recently, the usage of Big data analytics for forecasting business trends has been gaining momentum among professionals. The aim of the research is to explore the application of big data, predictive analytics in successfully mitigating supply chain social risk and demonstrate how such mitigation can help in achieving sustainability (environmental, economic & social). The method involves the identification and validation of social issues in the supply chain by an expert panel and survey. Later, we used a case study to illustrate the application of big data in the successful identification and mitigation of social issues in the supply chain. Our result shows that the company can predict various social issues through big data, predictive analytics and mitigate the social risk. We also discuss the implication of this research to the body of knowledge and practice.

Keywords: big data, sustainability, supply chain social sustainability, social risk, case study

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Authors: Amil Suleimanov, Aigul Saduakassova, Denis Vinnikov

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Objective: To assess functional visceral fat (VAT) activity evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) as a predictor of metastases in colorectal cancer (CRC). Materials and methods: We assessed 60 patients with histologically confirmed CRC who underwent 18F-FDG PET/CT after a surgical treatment and courses of chemotherapy. Age, histology, stage, and tumor grade were recorded. Functional VAT activity was measured by maximum standardized uptake value (SUVmax) using ¹⁸F-FDG PET/CT and tested as a predictor of later metastases in eight abdominal locations (RE – Epigastric Region, RLH – Left Hypochondriac Region, RRL – Right Lumbar Region, RU – Umbilical Region, RLL – Left Lumbar Region, RRI – Right Inguinal Region, RP – Hypogastric (Pubic) Region, RLI – Left Inguinal Region) and pelvic cavity (P) in the adjusted regression models. We also report the best areas under the curve (AUC) for SUVmax with the corresponding sensitivity (Se) and specificity (Sp). Results: In both adjusted for age regression models and ROC analysis, 18F-FDG accumulation in RLH (cutoff SUVmax 0.74; Se 75%; Sp 61%; AUC 0.668; p = 0.049), RU (cutoff SUVmax 0.78; Se 69%; Sp 61%; AUC 0.679; p = 0.035), RRL (cutoff SUVmax 1.05; Se 69%; Sp 77%; AUC 0.682; p = 0.032) and RRI (cutoff SUVmax 0.85; Se 63%; Sp 61%; AUC 0.672; p = 0.043) could predict later metastases in CRC patients, as opposed to age, sex, primary tumor location, tumor grade and histology. Conclusions: VAT SUVmax is significantly associated with later metastases in CRC patients and can be used as their predictor.

Keywords: ¹⁸F-FDG, PET/CT, colorectal cancer, predictive value

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Mohammadreza Mohebbi, Masoumeh Sanagou

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The ability to predict clinical outcomes is of great importance to physicians and clinicians. A number of different methods have been used in an effort to accurately predict these outcomes. These methods include the development of scoring systems based on multivariate statistical modelling, and models involving the use of classification and regression trees. The process usually consists of two consecutive phases, namely model development and external validation. The model development phase consists of building a multivariate model and evaluating its predictive performance by examining calibration and discrimination, and internal validation. External validation tests the predictive performance of a model by assessing its calibration and discrimination in different but plausibly related patients. A motivate example focuses on prediction modeling using a sample of patients undergone coronary artery bypass graft (CABG) has been used for illustrative purpose and a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study has been proposed.

Keywords: clinical prediction models, clinical decision rule, prognosis, external validation, model calibration, biostatistics

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Authors: Fatemeh Nokhodchi Bonab

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Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities. The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems.

Keywords: methods, applications, transcriptional regulatory systems, techniques

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Authors: Prateek Rastogi

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Medical teaching in present era concentrates not only on teaching but on effective teaching. For effective teaching a combination of effective carefully designed curriculum, an educated educator, competent learner and fool proof evaluation system is required. Keeping these parameters in mind and study was undertaken at Kasturba Medical College, Mangalore among medical students. In this study, evaluation of Forensic Medicine syllabus along with its teaching and evaluation methodology was done using 20 different parameters. This questionnaire based study was done over a period of two years i.e. 2013 and 2014. Batch of students who just passed the forensic medicine subject was included for study. Carefully designed questionnaire contained questions related to course content, teaching methodology and evaluation system along with provisions to mention merits and demerits of subject. The feedbacks in first round were analyzed and suggestions were implemented before conducting the second round of study. Overall evaluation of course was done as well as it was compared with other subjects of second MBBS. It was noted that Scores improved in 2nd survey thus stressing the importance of course evaluation and student feedback in teaching improvement.

Keywords: teaching methodology, system of evaluation, course content, bioinformatics, biomedicine

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Authors: Muhammad Waqas, Shahan Waheed, Mohsin Qadeer, Ehsan Bari, Salman Ahmed, Iqra Patoli

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Objective: To determine the value of aPTT, platelets and INR as the predictor of unfavorable outcomes in patients with blunt isolated traumatic brain injury. Methods: This was an observational cohort study conducted in a tertiary care facility from 1st January 2008 to 31st December 2012. All the patients with isolated traumatic brain injury presenting within 24 hours of injury were included in the study. Coagulation parameters at presentation were recorded and Glasgow Outcome Scale calculated on last follow up. Outcomes were dichotomized into favorable and unfavorable outcomes. Relationship of coagulopathy with GOS and unfavorable outcomes was calculated using Spearman`s correlation and area under curve ROC analysis. Results: 121 patients were included in the study. The incidence of coagulopathy was found to be 6 %. aPTT was found to a significantly associated with unfavorable outcomes with an AUC = 0.702 (95%CI = 0.602-0.802). Predictive value of platelets and INR was not found to be significant. Conclusion: Incidence of coagulopathy was found to be low in current population compared to data from the West. aPTT was found to be a good predictor of unfavorable outcomes compared with other parameters of coagulation.

Keywords: aPTT, coagulopathy, unfavorable outcomes, parameters

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Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

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Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: dynamic time warping, glottal area waveform, linear predictive coding, high-speed laryngeal images, Hilbert transform

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Shahryar Eslamitabar

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Currently, the internet plays an important role in the various scientific, commercial and service practices. Thanks to information and communication technology, the healthcare industry via the internet, generally known as cyber-medicine, can offer professional medical service in a wider geographical area. Having some appealing benefits such as convenience in offering healthcare services, improved accessibility to the services, enhanced information exchange, cost-effectiveness, time-saving, etc. Tele-health has increasingly developed innovative models of healthcare delivery. However, it presents many potential hazards to cyber-patients, inherent in the use of the system. First, there are legal issues associated with the communication and transfer of information on the internet. These include licensure, malpractice, liabilities and jurisdictions as well as privacy, confidentiality and security of personal data as the most important challenge brought about by this system. Additional items of concern are technological and ethical. Although, there are some rules to deal with pitfalls associated with cyber-medicine practices in the USA and some European countries, yet for all developments, it is being practiced in a legal vacuum in many countries. In addition to the domestic legislations to deal with potential problems arisen from the system, it is also imperative that some international or regional agreement should be developed to achieve the harmonization of laws among countries and states. This article discusses some implications posed by the practice of cyber-medicine in the healthcare system according to the experience of some developed countries using a comparative study of laws. It will also review the status of tele-health laws in Iran. Finally, it is intended to pave the way to outline a plan for countries like Iran, with newly-established judicial system for health laws, to develop appropriate regulations through providing some recommendations.

Keywords: tele-health, cyber-medicine, telemedicine, criminal laws, legislations, time-saving

Procedia PDF Downloads 659

Authors: Gopal Lamichhane, Biswash Sapkota, Grinsun Sharma, Mahendra Adhikari

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Urolithiasis is a condition in which insoluble or less soluble salts like oxalate, phosphate etc. precipitate in urinary tract and causes obstruction in ureter resulting renal colic or sometimes haematuria. It is the third most common disorder of urinary tract affecting nearly 2% of world’s population. Poor urinary drainage, microbial infection, oxalate and calcium containing diet, calciferol, hyperparathyroidism, cysteine in urine, gout, dysfunction of intestine, drought environment, lifestyle, exercise, stress etc. are risk factors for urolithiasis. Wide ranges of treatments are available in allopathic system of medicine but reoccurrence is unpreventable even with the surgical removal of stone or lithotripsy. So, people prefer alternative medicinal systems such as Unani, homeopathic, ayurvedic etc. systems of medicine due to their fewer side effects over allopathic counterpart. Different plants based ethnomedicines are being well established by their continuous effective use in human since long time in treatment of urinary problem. Many studies have scientifically proved those ethnomedicines for antiurolithiatic effect in animal and in vitro model. Plant-based remedies were found to be therapeutically effective for both prevention as well as cure of calcium oxalate urolithiasis. Plants were known to show these effects through a combination of many effects such as antioxidant, diuretic, hypocalciuric, urine alkalinizing effect in them. Berberine, triterpenoids, lupeol are the phytochemicals established for antiurolithiatic effect. Hence, plant-based medicine can be the effective herbal alternative as well as means of discovery of novel drug molecule for curing urolithiatic disorder and should be focused on further research to discover their value in coming future.

Keywords: urolithiasis, herbal medicine, ethnomedicine, kidney stone, calcium oxalate

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Authors: A. F. Suleimanov, A. B. Saduakassova, V. S. Pokrovsky, D. V. Vinnikov

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Relevance: Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy, with relapse occurring in about 70% of advanced cases with poor prognoses. The aim of the study was to evaluate functional visceral fat activity (VAT) evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) as a predictor of metastases in epithelial ovarian cancer (EOC). Materials and methods: We assessed 53 patients with histologically confirmed EOC who underwent ¹⁸F-FDG PET/CT after a surgical treatment and courses of chemotherapy. Age, histology, stage, and tumor grade were recorded. Functional VAT activity was measured by maximum standardized uptake value (SUVₘₐₓ) using ¹⁸F-FDG PET/CT and tested as a predictor of later metastases in eight abdominal locations (RE – Epigastric Region, RLH – Left Hypochondriac Region, RRL – Right Lumbar Region, RU – Umbilical Region, RLL – Left Lumbar Region, RRI – Right Inguinal Region, RP – Hypogastric (Pubic) Region, RLI – Left Inguinal Region) and pelvic cavity (P) in the adjusted regression models. We also identified the best areas under the curve (AUC) for SUVₘₐₓ with the corresponding sensitivity (Se) and specificity (Sp). Results: In both adjusted-for regression models and ROC analysis, ¹⁸F-FDG accumulation in RE (cut-off SUVₘₐₓ 1.18; Se 64%; Sp 64%; AUC 0.669; p = 0.035) could predict later metastases in EOC patients, as opposed to age, sex, primary tumor location, tumor grade, and histology. Conclusions: VAT SUVₘₐₓ is significantly associated with later metastases in EOC patients and can be used as their predictor.

Keywords: ¹⁸F-FDG, PET/CT, EOC, predictive value

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Eun-Seop Kim, Eun-Ha Jang, Rana R. Kim, Sae-Byul Jang

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Objective: Preterm labor is the most common antecedent of preterm birth(PTB), which is characterized by regular uterine contraction before 37 weeks of pregnancy and cervical change. In acute preterm labor, tocolytics are administered as the first-line medication to suppress uterine contractions but rarely delay pregnancy to 37 weeks of gestation. On the other hand, according to the Korean Traditional Medicine, PTB is caused by the deficiency of Qi and unnecessary energy in the body of the mother. The aim of this study was to demonstrate the benefit of Traditional Korean Medicine as an adjuvant therapy in management of early uterine contractions and the prevention of PTB. Methods: It is a case report of a 38-year-old woman (0-0-6-0) hospitalized for irregular uterine contractions and cervical change at 33+3/7 weeks of gestation. Past history includes chemical pregnancies achieved by Artificial Rroductive Technology(ART), one stillbirth (at 7 weeks) and a laparoscopic surgery for endometriosis. After seven trials of IVF and articificial insemination, she had succeeded in conception via in-vitro fertilization (IVF) with help of Traditional Korean Medicine (TKM) treatments. Due to irregular uterine contractions and cervical changes, 2 TKM were prescribed: Gami-Dangguisan, and Antae-eum, known to nourish blood and clear away heat. 120ml of Gami-Dangguisan was given twice a day monring and evening along with same amount of Antae-eum once a day from 31 August 2013 to 28 November 2013. Tocolytics (Ritodrine) was administered as a first aid for maintenance of pregnancy. Information regarding progress until the delivery was collected during the patient’s visit. Results: On admission, the cervix of 15mm in length and cervical os with 0.5cm-dilated were observed via ultrasonography. 50% cervical effacement was also detected in physical examination. Tocolysis had been temporarily maintained. As a supportive therapy, TKM herbal preparations(gami-dangguisan and Antae-eum) were concomitantly given. As of 34+2/7 weeks of gestation, however intermittent uterine contractions appeared (5-12min) on cardiotocography and vaginal bleeding was also smeared at 34+3/7 weeks. However, enhanced tocolytics and continuous administration of herbal medicine sustained the pregnancy to term. At 37+2/7 weeks, no sign of labor with restored cervical length was confirmed. The woman gave a term birth to a healthy infant via vaginal delivery at 39+3/7 gestational weeks. Conclusions: This is the first successful case report about a preter labor patient administered with conventional tocolytic agents as well as TKM herbal decoctions, delaying delivery to term. This case deserves attention considering it is rare to maintain gestation to term only with tocolytic intervention. Our report implies the potential of herbal medicine as an adjuvant therapy for preterm labor treatment. Further studies are needed to assess the safety and efficacy of TKM herbal medicine as a therapeutic alternative for curing preterm birth.

Keywords: preterm labor, traditional Korean medicine, herbal medicine, integrative treatment, complementary and alternative medicine

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Authors: Peyman Rezaei Hachesu, Vahideh Zareh Gavgani, Zahra Salahzadeh

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To survey the attitudes, awareness, and practice of Evidence Based Medicine (EBM), and to determine the barriers that influence apply’ EBM in therapeutic process among clinical residents in Iran.We conducted a cross sectional survey during September to December 2012 at the teaching hospitals of Tehran University of Medical Sciences among 79 clinical residents from different medical specialties. A valid and reliable questionnaire consisted of five sections and 27 statements were used in this research. We applied Spearman and Mann Whitney test for correlation between variables. Findings showed that the knowledge of residents about EBM is low. Their attitude towards EBM was positive but their knowledge and skills in regard with the evidence based medical information resources were mostly limited to PubMed and Google scholar. The main barrier was the lack of enough time to practicing EBM. There was no significant correlation between residency grade and familiarity and use of electronic EBM resources (Spearman, P = 0.138). Integration of training approaches like journal clubs or workshops with clinical practice is suggested.

Keywords: evidence-based medicine, clinical residents, decision-making, attitude, questionnaire

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Authors: Mazharuddin Syed Ahmed

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This research presents a framework used to develop The Ara Polytechnic College of Architecture Studies building “Kahukura” which is Green Building certified. This framework integrates the development of a smart building digital twin by utilizing Building Information Modelling (BIM) and its BIM maturity levels, including Levels of Development (LOD), eight dimensions of BIM, Heritage-BIM (H-BIM) and Facility Management BIM (FM BIM). The research also outlines a structured approach to building performance analysis and integration with the circular economy, encapsulated within a five-level digital twin framework. Starting with Level 1, the Descriptive Twin provides a live, editable visual replica of the built asset, allowing for specific data inclusion and extraction. Advancing to Level 2, the Informative Twin integrates operational and sensory data, enhancing data verification and system integration. At Level 3, the Predictive Twin utilizes operational data to generate insights and proactive management suggestions. Progressing to Level 4, the Comprehensive Twin simulates future scenarios, enabling robust “what-if” analyses. Finally, Level 5, the Autonomous Twin, represents the pinnacle of digital twin evolution, capable of learning and autonomously acting on behalf of users.

Keywords: building information modelling, circular economy integration, digital twin, predictive analytics

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Authors: Anucha Taiwong, Nirobol Kanogsunthornrat

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This predictive research aimed to investigate the symptom clusters that influence the quality of life among patients with chronic kidney disease, as indicated in the Theory of Unpleasant Symptoms. The purposive sample consisted of 150 patients with stage 3-4 chronic kidney disease who received care at an outpatient chronic kidney disease clinic of a tertiary hospital in Roi-Et province. Data were collected from January to March 2016 by using a patient general information form, unpleasant symptom form, and quality of life (SF-36) and were analyzed by using descriptive statistics, factor analysis, and multiple regression analysis. Findings revealed six core symptom clusters including symptom cluster of the mental and emotional conditions, peripheral nerves abnormality, fatigue, gastro-intestinal tract, pain and, waste congestion. Significant predictors for quality of life were the two symptom clusters of pain (Beta = -.220; p < .05) and the mental and emotional conditions (Beta=-.204; p<.05) which had predictive value of 19.10% (R2=.191, p<.05). This study indicated that the symptom cluster of pain and the mental and emotional conditions would worsen the patients’ quality of life. Nurses should be attentive in managing the two symptom clusters to facilitate the quality of life among patients with chronic kidney disease.

Keywords: chronic kidney disease, symptom clusters, predictors of quality of life, pre-dialysis

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Authors: Janis E. Campbell

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Most of what is known about complementary, alternative or traditional medicine (CATM) in the United States today is known from either the National Health Interview Survey a cross-sectional survey with a few questions or from small cross-sectional or cohort studies with specific populations. The broad geographical distribution in CATM use or providers is not known. For this project, we used geospatial cluster analysis to determine if there were clusters of CATM provider by county in the US. In this analysis, we used the National Provider Index to determine the geographic distribution of providers in the US. Of the 215,769 CAMT providers 211,603 resided in the contiguous US: Acupuncturist (26,563); Art, Poetry, Music and Dance Therapist (2,752); Chiropractor (89,514); Doula/Midwife (3,535); Exercise (507); Homeopath (380); Massage Therapist (36,540); Mechanotherapist (1,888); Naprapath (146); Naturopath (4,782); Nutrition (42,036); Reflexologist (522); Religious (2,438). ESRI® spatial autocorrelation was used to determine if the geographic location of CATM providers were random or clustered. For global analysis, we used Getis-Ord General G and for Local Indicators of Spatial Associations with an Optimized Hot Spot Analysis using an alpha of 0.05. Overall, CATM providers were clustered with both low and high. With Chiropractors, focusing in the Midwest, religious providers having very small clusters in the central US, and other types of CAMT focused in the northwest and west coast, Colorado and New Mexico, the great lakes areas and Florida. We will discuss some of the implications of this study, including associations with health, economic, social, and political systems.

Keywords: complementary medicine, alternative medicine, geospatial, United States

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: diesel engine, machine learning, NOₓ emission, semi-empirical

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Authors: V. Pourmostaghimi, M. Zadshakoyan

Abstract:

Efficiency and productivity of the finish hard turning can be enhanced impressively by utilizing accurate predictive models for cutting tool wear. However, the ability of genetic programming in presenting an accurate analytical model is a notable characteristic which makes it more applicable than other predictive modeling methods. In this paper, the genetic equation for modeling of tool flank wear is developed with the use of the experimentally measured flank wear values and genetic programming during finish turning of hardened AISI D2. Series of tests were conducted over a range of cutting parameters and the values of tool flank wear were measured. On the basis of obtained results, genetic model presenting connection between cutting parameters and tool flank wear were extracted. The accuracy of the genetically obtained model was assessed by using two statistical measures, which were root mean square error (RMSE) and coefficient of determination (R²). Evaluation results revealed that presented genetic model predicted flank wear over the study area accurately (R² = 0.9902 and RMSE = 0.0102). These results allow concluding that the proposed genetic equation corresponds well with experimental data and can be implemented in real industrial applications.

Keywords: cutting parameters, flank wear, genetic programming, hard turning

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Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

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Authors: A. Saimen, E. Armstrong, C. Manitshana

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Intimate partner violence (IPV) has been recognised as a global human rights violation. It is universally under diagnosed and the institution of timeous multi-faceted interventions has been noted to benefit IPV victims. Currently, the concept of using a screening tool to detect IPV has not been widely explored in a primary healthcare setting in South Africa, and it was for this reason that this study has been undertaken. A systematic random sampling of 1 in 8 women over a period of 3 months was conducted prospectively at the OPD of a Level 1 Hospital. Participants were asked about their experience of IPV during the past 12 months. The WAST-short, a two-question tool, was used to screen patients for IPV. To verify the result of the screening, women were also asked the remaining questions from the WAST. Data was collected from 400 participants, with a response rate of 99.3%. The prevalence of IPV in the sample was 32%. The WAST-short was shown to have the following operating characteristics: sensitivity 45.2%, specificity 98%,positive predictive value 98%, negative predictive value 79%. The WAST-short lacks sufficient sensitivity and therefore is not an ideal screening tool for this setting. Improvement in the sensitivity of the WAST-short in this setting may be achieved by lowering the threshold for a positive result for IPV screening, and modification of the screening questions to better reflect IPV as understood by the local population.

Keywords: domestic violence, intimate partner violence, screening, screening tools

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Authors: S. MacDonald, A. Manuel, R. Law, N. Bandruak, A. Dubrowski, V. Curran, J. Smith-Young, K. Simmons, A. Warren

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High fidelity human patient simulation has been used for many years by health sciences education programs to foster critical thinking, engage learners, improve confidence, improve communication, and enhance psychomotor skills. Unfortunately, there is a paucity of research on the use of high fidelity human patient simulation to foster teamwork among nursing, medicine and pharmacy undergraduate students. This study compared the impact of high fidelity and low fidelity simulation education on teamwork among nursing, medicine and pharmacy students. For the purpose of this study, two innovative teaching scenarios were developed based on the care of an adult patient experiencing acute anaphylaxis: one high fidelity using a human patient simulator and one low fidelity using case based discussions. A within subjects, pretest-posttest, repeated measures design was used with two-treatment levels and random assignment of individual subjects to teams of two or more professions. A convenience sample of twenty-four (n=24) undergraduate students participated, including: nursing (n=11), medicine (n=9), and pharmacy (n=4). The Interprofessional Teamwork Questionnaire was used to assess for changes in students’ perception of their functionality within the team, importance of interprofessional collaboration, comprehension of roles, and confidence in communication and collaboration. Student satisfaction was also assessed. Students reported significant improvements in their understanding of the importance of interprofessional teamwork and of the roles of nursing and medicine on the team after participation in both the high fidelity and the low fidelity simulation. However, only participants in the high fidelity simulation reported a significant improvement in their ability to function effectively as a member of the team. All students reported that both simulations were a meaningful learning experience and all students would recommend both experiences to other students. These findings suggest there is merit in both high fidelity and low fidelity simulation as a teaching and learning approach to foster teamwork among undergraduate nursing, medicine and pharmacy students. However, participation in high fidelity simulation may provide a more realistic opportunity to practice and function as an effective member of the interprofessional health care team.

Keywords: acute anaphylaxis, high fidelity human patient simulation, low fidelity simulation, interprofessional education

Procedia PDF Downloads 229

Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

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In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

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Authors: Renée Fregosi

Abstract:

The contribution presently proposed will report on the beginnings of a Franco-Chilean study to be launched in 2015 by a multidisciplinary team of Renée Fregosi Political Science University Paris 3 / CECIEC, Norma Muñoz Public Policies University of Santiago of Chile, Jean-Daniel Lelievre, Medicine Paris 11 University, Marcelo WOLFF Medicine University of Chile, Cecilia Blatrix Political Science University Paris-Tech, Ernesto OTTONE, Political Science University of Chile, Paul DENY Medicine Paris 13 University, Rafael Bugueno Medicine Hospital Urgencia Pública of Santiago, Eduardo CARRASCO Political Science Paris 3 University. The problem of hidden populations challenges some criteria and concepts to re-examine: in particular the concept of target population, sampling methods to "snowball" and the cost-effectiveness criterion that shows the connection of political and scientific fields. Furthermore, if the pattern of homosexual transmission still makes up the highest percentage of the modes of infection with HIV, there is a continuous increase in the number of people infected through heterosexual sex, including women and persons aged 50 years and older. This group can be described as " unknown risk people." Access to these populations is a major challenge and raises methodological, ethical and political issues of prevention, particularly on the issue of screening. This paper proposes an inventory of these types of problems and their articulation, to define a new phase in the prevention against HIV refocused on women.

Keywords: HIV testing, hidden populations, difficult to reach PLWHA, women, unknown risk people

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Authors: Upasana Sharma, Jayanti Dutta, Amarjeet Singh

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Background: Ayurveda is a comprehensive natural health care system. It is widely used in India as a system of primary health care, and interest in it is growing worldwide. Objectives: 1) To assess the extent and pattern of use of Ayurvedic medicines/ products by the people of Chandigarh. 2) To assess the perceived impact of use of Ayurvedic medicines/ products among the users. Methods: A cross-sectional community based study was conducted in a city of North India. Overall 371 households were covered from rural, urban and slum areas from December 2010 to April 2011. Respondents were interviewed regarding practices about Ayurveda products. Results: Around 160 (43%; 95% CI= 38.15, 47.85) of the respondents were using Ayurvedic products in one form or the other. Out of them, 91 (57%) had used Ayurvedic medicines in combination with some other system of medicine rather than as a standalone therapy. Most of them (81%) preferred Ayurveda products for chronic digestive system related problems. Conclusion: The present study revealed that respondents had keen interest in Ayurveda. A section of population was taking Ayurvedic treatment for their health ailments. There was a great level of satisfaction among the users but high cost bothered them at times.

Keywords: ayurveda, alternative medicine, chronic diseases, complimentary medicine

Procedia PDF Downloads 308