Search results for: PREDICT score

Authors: Hamed Alizadeh Dashagholi, Hossein Yousefi-Banaem, Mina Naeimi

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Current study established for EEG signal analysis in patients with language disorder. Language disorder can be defined as meaningful delay in the use or understanding of spoken or written language. The disorder can include the content or meaning of language, its form, or its use. Here we applied Z-score, power spectrum, and coherence methods to discriminate the language disorder data from healthy ones. Power spectrum of each channel in alpha, beta, gamma, delta, and theta frequency bands was measured. In addition, intra hemispheric Z-score obtained by scoring algorithm. Obtained results showed high Z-score and power spectrum in posterior regions. Therefore, we can conclude that peoples with language disorder have high brain activity in frontal region of brain in comparison with healthy peoples. Results showed that high coherence correlates with irregularities in the ERP and is often found during complex task, whereas low coherence is often found in pathological conditions. The results of the Z-score analysis of the brain dynamics showed higher Z-score peak frequency in delta, theta and beta sub bands of Language Disorder patients. In this analysis there were activity signs in both hemispheres and the left-dominant hemisphere was more active than the right.

Keywords: EEG, electroencephalography, coherence methods, language disorder, power spectrum, z-score

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Mahmoud Tarek, Alaa Obeida, Mai Magdy, Khalid Hussein, Aly Shalaby

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Background: More doctors are complaining of burnout than before, Burnout is a state of physical and mental exhaustion caused by the doctor’s lifestyle, unfortunately, Medical errors are also more likely in those suffering from burnout and these may result in malpractice suits. Methodology: It is a retrospective audit of burnout response on all neonatologists over a 9 months period. We gathered data using burnout questionnaire, it was obtained from 23 physicians, the physicians divided into 5 categories according to the final score of the 28 questions in the questionnaire. Category 1 with score from 28-38 with almost no work stress, category 2 with score (38-50) who express a low amount of job related stress, category 3 with score (51-70) with moderate amount of stress, category 4 with score (71-90) those express a high amount of job stress and begun to burnout, category 5 with score (91 and above) who are under a dangerous amount of stress and advanced stage of burnout. Results: 33 neonatologists have received the questionnaire, 23 responses were sent back with a response rate of 69.6%. The results showed that 61% of physicians fall in category 4, 31% of the physician in category 5, while 8% of physicians equally distributed between category 2 and 3 (4% each of them). On the other hand, there is no physician present in category 1. Conclusion: Burnout is prevalent in SNICUs, So interventions to minimize burnout prevalence may be of greater importance as this may be reflected indirectly on medical conditions of the patients and physicians, efforts should be done to decrease this high rate of burnout.

Keywords: Cairo, work overload, exhaustion, surgery, neonatal ICU

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Authors: Christel Elisabeth Bonin

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The score of the 2 ½ hour ‘magnum opus’ named ‘Epitaph’ was reconstructed 10 years after Charles Mingus’ death in 1979. Most of the movements were probably composed in the late 1950s. As the finale was missing, Gunther Schuller, the conductor of the world premiere in 1989, decided to improvise one with the orchestra, using Mingus as a guide. The aim of this paper is to analyze ‘Main Score Part I ‘ and ‘Main Score Part II’ and to look into the score of Mingus’ reconstructed compositions under particular observation of the new finale, ‘Main Score Reprise’. There, Mingus left instructions for a return to the opening section of ‘Epitaph’. By examining ‘Epitaph’ in the historical context of Jazz between 1955 to 1967 and the 1980s and comparing the finale of ‘Epitaph’, created - or better said: improvised - by the musicians of the 1989 world premiere with the opening section, at first it will be interesting to discover at which point Gunther Schuller followed Mingus creative process and brought it to life in 1989. Finally, it will be speculated if Charles Mingus composition still represents a foresight of Jazz nearly 30 years after its creation.

Keywords: epitaph, Charles Mingus, Gunter Schuller, jazz reception, bebop, hardbop, Duke Ellington, black, brown and beige, African-American music, free-jazz

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Authors: Rim Sghiri, Samia Al Shouli, Hana Benhassine, Nejla Elamri, Zahid Shakoor, Foued Slama, Adel Almogren, Hala Zeglaoui, Elyes Bouajina, Ramzi Zemni

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Objectives: Little is known about genes predisposing to systemic bone loss (SBL) in rheumatoid arthritis (RA). Therefore, we examined the association between SBL and a variant of CD40 gene, which is known to play a critical role in both immune response and bone homeostasis among patients with RA. Methods: CD40 rs48104850 was genotyped in 176 adult RA patients. Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA). Results: Low BMD was observed in 116 (65.9%) patients. Among them, 60 (34.1%) had low femoral neck (FN) Z score, 72 (40.9%) had low total femur (TF) Z score, and 105 (59.6%) had low lumbar spine (LS) Z score. CD40 rs4810485 was found to be associated with reduced TF Z score with the CD40 rs4810485 T allele protecting against reduced TF Z score (OR = 0.40, 95% CI = 0.23-0.68, p = 0.0005). This association was confirmed in the multivariate logistic regression analysis (OR=0.31, 95% CI= 0.16-0.59, p=3.84 x 10₋₄). Moreover, median FN BMD was reduced among RA patients with CD40 rs4810485 GG genotype compared to RA patients harbouring CD40 rs4810485 TT and GT genotypes (0.788± 0.136 versus 0.826± 0.146g/cm², p=0.001). Conclusion: This study, for the first time ever, demonstrated an association between a CD40 genetic variant and SBL among patients with RA.

Keywords: rheumatoid arthritis, CD40 gene, bone mineral density, systemic bone loss, rs48104850

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Ashi Gautam, Isha Shukla, Akhil Seghal

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Biosensors are analytical devices that use a biological sensing element to detect and measure a specific chemical substance or biomolecule in a sample. These devices are widely used in various fields, including medical diagnostics, environmental monitoring, and food analysis, due to their high specificity, sensitivity, and selectivity. In this research paper, a machine learning model is proposed for predicting the suitability of skin care products based on biosensor readings. The proposed model takes in features extracted from biosensor readings, such as biomarker concentration, skin hydration level, inflammation presence, sensitivity, and free radicals, and outputs the most appropriate skin care product for an individual. This model is trained on a dataset of biosensor readings and corresponding skin care product information. The model's performance is evaluated using several metrics, including accuracy, precision, recall, and F1 score. The aim of this research is to develop a personalised skin care product recommendation system using biosensor data. By leveraging the power of machine learning, the proposed model can accurately predict the most suitable skin care product for an individual based on their biosensor readings. This is particularly useful in the skin care industry, where personalised recommendations can lead to better outcomes for consumers. The developed model is based on supervised learning, which means that it is trained on a labeled dataset of biosensor readings and corresponding skin care product information. The model uses these labeled data to learn patterns and relationships between the biosensor readings and skin care products. Once trained, the model can predict the most suitable skin care product for an individual based on their biosensor readings. The results of this study show that the proposed machine learning model can accurately predict the most appropriate skin care product for an individual based on their biosensor readings. The evaluation metrics used in this study demonstrate the effectiveness of the model in predicting skin care products. This model has significant potential for practical use in the skin care industry for personalised skin care product recommendations. The proposed machine learning model for predicting the suitability of skin care products based on biosensor readings is a promising development in the skin care industry. The model's ability to accurately predict the most appropriate skin care product for an individual based on their biosensor readings can lead to better outcomes for consumers. Further research can be done to improve the model's accuracy and effectiveness.

Keywords: biosensors, data model, machine learning, skin care

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Authors: Hao-Hsi Tseng, Hsin-Yun Lee

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The Most Advantageous Tender (MAT) has been criticized for its susceptibility to dictatorial situations and for its processing of same score, same rank issues. This study applies the four criteria from Arrow's Impossibility Theorem to construct a mechanism for revealing illegitimate scores in scoring methods. While commonly be used to improve on problems resulting from extreme scores, ranking methods hide significant defects, adversely affecting selection fairness. To address these shortcomings, this study relies mainly on the overall evaluated score method, using standardized scores plus normal cumulative distribution function conversion to calculate the evaluation of vender preference. This allows for free score evaluations, which reduces the influence of dictatorial behavior and avoiding same score, same rank issues. Large-scale simulations confirm that this method outperforms currently used methods using the Impossibility Theorem.

Keywords: Arrow’s impossibility theorem, cumulative normal distribution function, most advantageous tender, scoring method

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Authors: Suphaphon Udomluck, Pannathorn Chachvarat

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Information Communication Technology (ICT) using management is essential for effective decision making in organization. The Concerns Based Adoption Model (CBAM) was employed as the conceptual framework. The purposes of the study were to assess the situation of Information Communication Technology (ICT) using management in College of Nursing under the Praboromarajchanok Institute. The samples were multi – stage sampling of 10 colleges of nursing that participated include directors, vice directors, head of learning groups, teachers, system administrator and responsible for ICT. The total participants were 280; the instrument used were questionnaires that include 4 parts, general information, Information Communication Technology (ICT) using management, the Stage of concern Questionnaires (SoC), and the Levels of Use (LoU) ICT Questionnaires respectively. Reliability coefficients were tested; alpha coefficients were 0.967for Information Communication Technology (ICT) using management, 0.884 for SoC and 0.945 for LoU. The data were analyzed by frequency, percentage, mean, standard deviation, Pearson Product Moment Correlation and Multiple Regression. They were founded as follows: The high level overall score of Information Communication Technology (ICT) using management and issue were administration, hardware, software, and people. The overall score of the Stage of concern (SoC)ICTis at high level and the overall score of the Levels of Use (LoU) ICTis at moderate. The Information Communication Technology (ICT) using management had the positive relationship with the Stage of concern (SoC)ICTand the Levels of Use (LoU) ICT(p < .01). The results of Multiple Regression revealed that administration hardwear, software and people ware could predict SoC of ICT (18.5%) and LoU of ICT (20.8%).The factors that were significantly influenced by SoCs were people ware. The factors that were significantly influenced by LoU of ICT were administration hardware and people ware.

Keywords: information communication technology (ICT), management, the concerns-based adoption model (CBAM), stage of concern(SoC), the levels of use(LoU)

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Authors: Yu-Chuan Huang

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Introduction: There is a higher mortality rate for unplanned transfer to intensive care units. It also needs a longer length of stay and makes the intensive care unit beds cannot be effectively used. It affects the immediate medical treatment of critically ill patients, resulting in a drop in the quality of medical care. Purpose: The purpose of this study was using SOFA and APACHEII score to analyze the mortality rate of the cases transferred from ED to ICU. According to the score that should be provide an appropriate care as early as possible. Methods: This study was a descriptive experimental design. The sample size was estimated at 220 to reach a power of 0.8 for detecting a medium effect size of 0.30, with a 0.05 significance level, using G-power. Considering an estimated follow-up loss, the required sample size was estimated as 242 participants. Data were calculated by medical system of SOFA and APACHEII score that cases transferred from ED to ICU in 2016. Results: There were 233 participants meet the study. The medical records showed 33 participants’ mortality. Age and sex with QSOFA , SOFA and sex with APACHEII showed p>0.05. Age with APCHHII in ED and ICU showed r=0.150, 0,268 (p < 0.001**). The score with mortality risk showed: ED QSOFA is r=0.235 (p < 0.001**), exp(B)=1.685(p = 0.007); ICU SOFA 0.78 (p < 0.001**), exp(B)=1.205(p < 0.001). APACHII in ED and ICU showed r= 0.253, 0.286 (p < 0.001**), exp(B) = 1.041,1.073(p = 0.017,0.001). For SOFA, a cutoff score of above 15 points was identified as a predictor of the 95% mortality risk. Conclusions: The SOFA and APACHE II were calculated based on initial laboratory data in the Emergency Department, and during the first 24 hours of ICU admission. In conclusion, the SOFA and APACHII score is significantly associated with mortality and strongly predicting mortality. Early predictors of morbidity and mortality, which we can according the predicting score, and provide patients with a detail assessment and proper care, thereby reducing mortality and length of stay.

Keywords: SOFA, APACHEII, mortality, ICU

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Authors: Raghav Lakhotia, Chandra Kanth Nagesh, Krishna Madgula

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A lot has been said and discussed regarding the rationale and significance of the Bechdel Score. It became a digital sensation in 2013, when Swedish cinemas began to showcase the Bechdel test score of a film alongside its rating. The test has drawn criticism from experts and the film fraternity regarding its use to rate the female presence in a movie. The pundits believe that the score is too simplified and the underlying criteria of a film to pass the test must include 1) at least two women, 2) who have at least one dialogue, 3) about something other than a man, is egregious. In this research, we have considered a few more parameters which highlight how we represent females in film, like the number of female dialogues in a movie, dialogue genre, and part of speech tags in the dialogue. The parameters were missing in the existing criteria to calculate the Bechdel score. The research aims to analyze 342 movies scripts to test a hypothesis if these extra parameters, above with the current Bechdel criteria, are significant in calculating the female representation score. The result of the Principal Component Analysis method concludes that the female dialogue content is a key component and should be considered while measuring the representation of women in a work of fiction.

Keywords: Bechdel test, dialogue genre, parts of speech tags, principal component analysis

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Authors: G. Shiha, S. Seif, W. Samir, K. Zalata

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Prospective Validation of the FibroTest Score in assessing Liver Fibrosis in Hepatitis C Infection with Genotype 4 FibroTest (FT) is non-invasive score of liver fibrosis that combines the quantitative results of 5 serum biochemical markers (alpha-2-macroglobulin, haptoglobin, apolipoprotein A1, gamma glutamyl transpeptidase (GGT) and bilirubin) and adjusted with the patient's age and sex in a patented algorithm to generate a measure of fibrosis. FT has been validated in patients with chronic hepatitis C (CHC) (Halfon et al., Gastroenterol. Clin Biol.( 2008), 32 6suppl 1, 22-39). The validation of fibro test ( FT) in genotype IV is not well studied. Our aim was to evaluate the performance of FibroTest in an independent prospective cohort of hepatitis C patients with genotype 4. Subject was 122 patients with CHC. All liver biopsies were scored using METAVIR system. Our fibrosis score(FT) were measured, and the performance of the cut-off score were done using ROC curve. Among patients with advanced fibrosis, the FT was identically matched with the liver biopsy in 18.6%, overestimated the stage of fibrosis in 44.2% and underestimated the stage of fibrosis in 37.7% of cases. Also in patients with no/mild fibrosis, identical matching was detected in 39.2% of cases with overestimation in 48.1% and underestimation in 12.7%. So, the overall results of the test were identical matching, overestimation and underestimation in 32%, 46.7% and 21.3% respectively. Using ROC curve it was found that (FT) at the cut-off point of 0.555 could discriminate early from advanced stages of fibrosis with an area under ROC curve (AUC) of 0.72, sensitivity of 65%, specificity of 69%, PPV of 68%, NPV of 66% and accuracy of 67%. As FibroTest Score overestimates the stage of advanced fibrosis, it should not be considered as a reliable surrogate for liver biopsy in hepatitis C infection with genotype 4.

Keywords: fibrotest, chronic Hepatitis C, genotype 4, liver biopsy

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Authors: Surjeet Bakshi, Surabhi Sharma

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Objective: To assess the impact of educational sessions by reputed regional faculties on knowledge of primary care physicians on evidence based diabetes management methods and practice. Study Design: Retrospective pre-post intervention study. Methodology: Nine cities in Kerala from August to October, 2012 were selected for the study. 125 MBBS doctors participated in the study. 11 regional faculties provided six educational sessions throughout the period. Validated questionnaires were used to evaluate the knowledge of the participants on evidence based diabetes management methods before and after the intervention. Results: The mean score on pre-test was 8 and the mean score on post-test was 9. A paired t-test was conducted on participant’s pre- and post test score and the results were statistically significant (p<0.001). Conclusion: Even though the general attitude to and level of knowledge of diabetes management is good among the primary care physicians in India, there do exist some knowledge gaps which might influence their future practices when it comes to counselling and information on diabetes management methods. In the present study, the performance and awareness level of the participants have expressively improved among primary care physicians. There is a significant improvement in the test score and the training conducted. It seems that if such study programmes are included in the students study programme, it will give higher score in the knowledge and attitude towards diabetes management.

Keywords: diabetes, management, primary care physicians, evidence base, improvement score, knowledge

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Authors: Farzad Khajavi

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Recognition of the interaction between active pharmaceutical ingredients (API) and excipients is a pivotal factor in the development of all pharmaceutical dosage forms. By predicting the interaction between API and excipients, we will be able to prevent the advent of impurities or at least lessen their amount. In this study, we used principle component analysis (PCA) to predict the interaction between Gliclazide as a secondary amine with Lactose in pharmaceutical solid dosage forms. The infrared spectra of binary mixtures of Gliclazide with Lactose at different mole ratios were recorded, and the obtained matrix was analyzed with PCA. By plotting score columns of the analyzed matrix, the incompatibility between Gliclazide and Lactose was observed. This incompatibility was seen experimentally. We observed the appearance of the impurity originated from the Maillard reaction between Gliclazide and Lactose at the chromatogram of the manufactured tablets in room temperature and under accelerated stability conditions. This impurity increases at the stability months. By changing Lactose to Mannitol and using Calcium Dibasic Phosphate in the tablet formulation, the amount of the impurity decreased and was in the acceptance range defined by British pharmacopeia for Gliclazide Tablets. This method is a fast and simple way to predict the existence of incompatibility between excipients and active pharmaceutical ingredients.

Keywords: PCA, gliclazide, impurity, infrared spectroscopy, interaction

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Authors: Ammar Ali Saleh Jaber, Amer Hayat Khan, Syed Azhar Syed Sulaiman

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Tuberculosis (TB) is considered as a major disease that affects daily activities and impairs health-related quality of life (HRQoL). The impact of TB on HRQoL can affect treatment outcome and may lead to treatment defaulting. Therefore, this study aims to evaluate the HRQoL of TB treatment lost to follow-up during and after treatment in Yemen. For this aim, this prospective study enrolled a total of 399 TB lost to follow-up patients between January 2011 and December 2015. By applying HRQoL criteria, only 136 fill the survey during treatment. Moreover, 96 were traced and fill out the HRQoL survey. All eight HRQol domains were categorized into the physical component score (PCS) and mental component score (MCS), which were calculated using QM scoring software. Results show that all lost to follow-up TB patients reported a score less than 47 for all eight domains, except general health (67.3) during their treatment period. Low scores of 27.9 and 29.8 were reported for emotional role limitation (RE) and mental health (MH), respectively. Moreover, the mental component score (MCS) was found to be only 28.9. The trace lost follow-up shows a significant improvement in all eight domains and a mental component score of 43.1. The low scores of 27.9 and 29.8 for role emotion and mental health, respectively, in addition to the MCS score of 28.9, show that severe emotional condition and reflect the higher depression during treatment period that can result to lost to follow-up. The low MH, RE, and MCS can be used as a clue for predicting future TB treatment lost to follow-up.

Keywords: Yemen, tuberculosis, health-related quality of life, Khat

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Authors: S. J. Yune, S. Y. Lee, S. J. Im, B. S. Kam, S. Y. Baek

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This study aimed to determine the reliability of clinical performance assessments, having been emphasized by ability-based education, and professors overall assessment methods. We addressed the following problems: First, we try to find out whether there is a difference in what we consider to be the main variables affecting the clinical performance test according to the evaluator’s working period and the number of evaluation experience. Second, we examined the relationship among the global rating score (G), analytic global rating score (Gc), and the sum of the analytical checklists (C). What are the main factors affecting clinical performance assessments in relation to the numbers of times the evaluator had administered evaluations and the length of their working period service? What is the relationship between overall assessment score and analytic checklist score? How does analytic global rating with 6 components in OSCE and 4 components in sub-domains (Gc) CPX: aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude overall assessment score and task-specific analytic checklist score sum (C) affect the professor’s overall global rating assessment score (G)? We studied 75 professors who attended a 2016 Bugyeoung Consortium clinical skills performances test evaluating third and fourth year medical students at the Pusan National University Medical school in South Korea (39 prof. in OSCE, 36 prof. in CPX; all consented to participate in our study). Each evaluator used 3 forms; a task-specific analytic checklist, subsequent analytic global rating scale with sub-6 domains, and overall global scale. After the evaluation, the professors responded to the questionnaire on the important factors of clinical performance assessment. The data were analyzed by frequency analysis, correlation analysis, and hierarchical regression analysis using SPSS 21.0. Their understanding of overall assessment was analyzed by dividing the subjects into groups based on experiences. As a result, they considered ‘precision’ most important in overall OSCE assessment, and ‘precise accuracy physical examination’, ‘systemic approaches to taking patient history’, and ‘diagnostic skill capability’ in overall CPX assessment. For OSCE, there was no clear difference of opinion about the main factors, but there was for CPX. Analytic global rating scale score, overall rating scale score, and analytic checklist score had meaningful mutual correlations. According to the regression analysis results, task-specific checklist score sum had the greatest effect on overall global rating. professors regarded task-specific analytic checklist total score sum as best reflecting overall OSCE test score, followed by aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude on a subsequent analytic global rating scale. For CPX, subsequent analytic global rating scale score, overall global rating scale score, and task-specific checklist score had meaningful mutual correlations. These findings support explanations for validity of professors’ global rating in clinical performance assessment.

Keywords: global rating, clinical performance assessment, medical education, analytic checklist

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Authors: Katleho Daniel Makatjane, Diteboho Lawrence Xaba, Ntebogang Dinah Moroke

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The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.

Keywords: generalized autoregressive score model, South Africa, stock returns, time-varying

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Authors: Hongmeng Su, Luyu Zhao, Yanyan Qian, Hong Fan

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Aim: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors that endanger human health seriously. RNA methylation, especially N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C), a crucial epigenetic transcriptional regulatory mechanism, plays an important role in tumorigenesis, progression and prognosis. This research aims to systematically evaluate the prognostic value of m⁶A and m⁵C modulators in HCC patients. Methods: Twenty-four modulators of m⁶A and m⁵C were candidates to analyze their expression level and their contribution to predict the prognosis of HCC. Consensus clustering analysis was applied to classify HCC patients. Cox and LASSO regression were used to construct the risk model. According to the risk score, HCC patients were divided into high-risk and low/medium-risk groups. The clinical pathology factors of HCC patients were analyzed by univariate and multivariate Cox regression analysis. Results: The HCC patients were classified into 2 clusters with significant differences in overall survival and clinical characteristics. Nine-gene risk model was constructed including METTL3, VIRMA, YTHDF1, YTHDF2, NOP2, NSUN4, NSUN5, DNMT3A and ALYREF. It was indicated that the risk score could serve as an independent prognostic factor for patients with HCC. Conclusion: This study constructed a Nine-gene risk model by modulators of m⁶A and m⁵C and investigated its effect on the clinical prognosis of HCC. This model may provide important consideration for the therapeutic strategy and prognosis evaluation analysis of patients with HCC.

Keywords: hepatocellular carcinoma, m⁶A, m⁵C, prognosis, RNA methylation

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Authors: Mohamed Abo El Nasr, Mohamed Shoeib, Abdelhamid Abdelkhalik, Amro Serag

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Background: Early assessment of severity of chest trauma, either blunt or penetrating is of critical importance in prediction of patient outcome. Different trauma scoring systems are widely available and are based on anatomical or physiological parameters to expect patient morbidity or mortality. Up till now, there is no ideal, universally accepted trauma score that could be applied in all trauma centers and is suitable for assessment of severity of chest trauma patients. Aim: Our aim was to compare various trauma scoring systems regarding their predictability of morbidity and mortality in chest trauma patients. Patients and Methods: This study was a prospective study including 400 patients with chest trauma who were managed at Tanta University Emergency Hospital, Egypt during a period of 2 years (March 2014 until March 2016). The patients were divided into 2 groups according to the mode of trauma: blunt or penetrating. The collected data included age, sex, hemodynamic status on admission, intrathoracic injuries, and associated extra-thoracic injuries. The patients outcome including mortality, need of thoracotomy, need for ICU admission, need for mechanical ventilation, length of hospital stay and the development of acute respiratory distress syndrome were also recorded. The relevant data were used to calculate the following trauma scores: 1. Anatomical scores including abbreviated injury scale (AIS), Injury severity score (ISS), New injury severity score (NISS) and Chest wall injury scale (CWIS). 2. Physiological scores including revised trauma score (RTS), Acute physiology and chronic health evaluation II (APACHE II) score. 3. Combined score including Trauma and injury severity score (TRISS ) and 4. Chest-Specific score Thoracic trauma severity score (TTSS). All these scores were analyzed statistically to detect their sensitivity, specificity and compared regarding their predictive power of mortality and morbidity in blunt and penetrating chest trauma patients. Results: The incidence of mortality was 3.75% (15/400). Eleven patients (11/230) died in blunt chest trauma group, while (4/170) patients died in penetrating trauma group. The mortality rate increased more than three folds to reach 13% (13/100) in patients with severe chest trauma (ISS of >16). The physiological scores APACHE II and RTS had the highest predictive value for mortality in both blunt and penetrating chest injuries. The physiological score APACHE II followed by the combined score TRISS were more predictive for intensive care admission in penetrating injuries while RTS was more predictive in blunt trauma. Also, RTS had a higher predictive value for expectation of need for mechanical ventilation followed by the combined score TRISS. APACHE II score was more predictive for the need of thoracotomy in penetrating injuries and the Chest-Specific score TTSS was higher in blunt injuries. The anatomical score ISS and TTSS score were more predictive for prolonged hospital stay in penetrating and blunt injuries respectively. Conclusion: Trauma scores including physiological parameters have a higher predictive power for mortality in both blunt and penetrating chest trauma. They are more suitable for assessment of injury severity and prediction of patients outcome.

Keywords: chest trauma, trauma scores, blunt injuries, penetrating injuries

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Authors: Nicolas Koslover, Tamara Levene

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Aims: Tonsilitis is one of the most common presentations to the A+E department. We aimed to assess whether patients presenting with tonsilitis are being managed in-line with current guidance. We then set out to educate A+E staff about tonsilitis management and then assessed for improvement in management. Methods: All patients presenting to A+E in one fortnight with a documented diagnosis of tonsilitis were included. We reviewed the notes to assess the choice of treatment in each case and whether a clinical score (CENTOR or FEVERPain score) was used to guide choice of treatment (in accordance with NICE guideline [NG84]). We designed and delivered an educational intervention for A+E staff covering tonsilitis guidelines. The audit was repeated two weeks later. Results: Over the study period, 49 patients were included; only 35% (n=17) had either a clinical score documented or had all components of a score recorded. In total, 39% (n=19) were treated with antibiotics. Of these, 63% (n=12) should not have been prescribed an antibiotic and 37% (n=7) were prescribed an inappropriate antibiotic. At re-audit, (n=50 cases), 58% (n=29) had a clinical score documented and 28% (n=14) were treated with antibiotics. Of these, 29% (n=4) should not have been prescribed antibiotics and 21% (n=3) were prescribed an inappropriate antibiotic. Thus, after this teaching session, there was a significant improvement in antibiotic prescribing practices (63% vs. 29%, p=0.026). Conclusions: A+E assessment and management of tonsilitis frequently deviated from guidelines, but a single teaching session vastly improved clinical scoring and antibiotic prescribing practices.

Keywords: tonsilitis, education, emergency medicine, ENT

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Authors: Venkatesh Pulletikurthi, Karthik B. Ariyur, Luciano Castillo

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The system identification from the turbulence wakes will lead to the tactical advantage to prepare and also, to predict the trajectory of the opponents’ movements. A low-order modeling technique, POD, is used to predict the object based on the wake pattern and compared with pre-trained image recognition neural network (NN) to classify the wake patterns into objects. It is demonstrated that low-order modeling, POD, is able to predict the objects better compared to pretrained NN by ~30%.

Keywords: the bluff body wakes, low-order modeling, neural network, system identification

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Authors: Phang Moon Leng

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Automated Early Warning Scores is a newly developed clinical decision tool that is used to streamline and improve the process of obtaining a patient’s vital signs so a clinical decision can be made at an earlier stage to prevent the patient from further deterioration. This technology provides immediate update on the score and clinical decision to be taken based on the outcome. This paper aims to study the use of an automated early warning score system on whether the technology has assisted the hospital in early detection and escalation of clinical condition and improve patient outcome. The hospital adopted the Modified Early Warning Scores (MEWS) Scoring System and MEWS Clinical Response into Philips IntelliVue Guardian Automated Early Warning Score equipment and studied whether the process has been leaned, whether the use of technology improved the usage & experience of the nurses, and whether the technology has improved patient care and outcome. It was found the steps required to obtain vital signs has been significantly reduced and is used more frequently to obtain patient vital signs. The number of deaths, and length of stay has significantly decreased as clinical decisions can be made and escalated more quickly with the Automated EWS. The automated early warning score equipment has helped improve work efficiency by removing the need for documenting into patient’s EMR. The technology streamlines clinical decision-making and allows faster care and intervention to be carried out and improves overall patient outcome which translates to better care for patient.

Keywords: automated early warning score, clinical quality and safety, patient safety, medical technology

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Jocelyn Li, Xiangyang Li

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We have employed THINC-it® to study the acute effects of Spravato on the cognitive function of patients with severe major depression disorder (MDD). The scores of the four tasks (Spotter, Symbol Check, Code Breaker, Trails) found in THINC-it® were used to measure cognitive function throughout treatment. The patients who participated in this study have tried more than 3 antidepressants without significant improvement before they began Spravato treatment. All patients received 3 doses of 28 mg Spravato 5 minutes apart (84 mg total per treatment) during this study with THINC-it®. The data were collected before the first Spravato administration (T0), 1 hour after the first Spravato administration (T1), and 2 hours after the first Spravato administration (T2) during each treatment. The following data were from 13 patients, with a total of 226 trials in a 2-3 month period. Spravato at 84 mg reduced the scores of Trails, Code Breaker, Symbol Check, and Spotter at T1 by 10-20% in all patients with one exception for a minority of patients in Spotter. At T2, the scores of Trails, Symbol Check, and Spotter were back to 97% of T0 while the score of Code Breaker was back to 92%. Interestingly, we found that the score of Spotter was consistently increased by 17% at T1 in the same 30% of patients in each treatment. We called this change reverse response while the pattern of the other patients, a decline (T1) and then recovery (T2), was called non-reverse response. We also compared the scores at T0 between the first visit and the fifth visit. The T0 scores of all four tasks were improved at visit 5 when compared to visit 1. The scores of Trails, Code Breaker, and Symbol Check at T0 were increased by 14%, 33%, and 14% respectively at visit 5. The score of Code Breaker, which had two trends, improved by 9% in reverse response patients compared to a 27% improvement in non-reverse response patients. To our knowledge, this is the first study done on the impact of Spravato on cognitive function change in major depression patients at this time frame. Whether we can predict future responses to Spravato with THINC-it® merits further study.

Keywords: Spravato, THINC-it, major depressive disorder, cognitive function

Procedia PDF Downloads 88

Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Umesh Kumar Singh, Chanchala Joshi

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With increasing dependency on IT infrastructure, the main objective of a system administrator is to maintain a stable and secure network, with ensuring that the network is robust enough against malicious network users like attackers and intruders. Security risk management provides a way to manage the growing threats to infrastructures or system. This paper proposes a framework for risk level estimation which uses vulnerability database National Institute of Standards and Technology (NIST) National Vulnerability Database (NVD) and the Common Vulnerability Scoring System (CVSS). The proposed framework measures the frequency of vulnerability exploitation; converges this measured frequency with standard CVSS score and estimates the security risk level which helps in automated and reasonable security management. In this paper equation for the Temporal score calculation with respect to availability of remediation plan is derived and further, frequency of exploitation is calculated with determined temporal score. The frequency of exploitation along with CVSS score is used to calculate the security risk level of the system. The proposed framework uses the CVSS vectors for risk level estimation and measures the security level of specific network environment, which assists system administrator for assessment of security risks and making decision related to mitigation of security risks.

Keywords: CVSS score, risk level, security measurement, vulnerability category

Procedia PDF Downloads 295

Authors: Hiroaki Hirose, Hiroshi Ikeda, Isao Takeda

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The Japanese version of the SF-36 has been employed to assess individuals’ health-related QOL (HRQOL). This study aimed to clarify the HRQOL of motorists with a spinal cord injury, in order to compare these individuals' SF-36 scores and national standard values. A total of 100 motorists with a spinal cord injury participated in this study. Participants’ HRQOL was evaluated using the Japanese version of the SF-36 (second edition). The score for each subscale was standardized based on data on the Japanese population. The average scores for NPF, NRP, NBP, NGH, NVT, NSF, NRE, and NMH were 10.9, 41.8, 45.9, 47.1, 46.1, 46.7, 46.0, and 47.4 points, respectively. Subjects showed significantly lower scores for NPF and NRP compared with national standard values, which were both ≤ 45.0 points, but relatively normal scores for the other items: NBP, NGH, NVT, NSF, NRE and NMH (> 45.0 points). The average scores for PCS, MCS and RCS were 21.9, 56.0, and 50.0 points, respectively. Subjects showed a significantly lower PCS score (≤ 20.0 points); however, the MCS score was higher (> 55.0 points) along with a relatively normal RCS score in these individuals (= 50.0 points).

Keywords: health-related QOL, HRQOL, SF-36, motorist, spinal cord injury, Japan

Procedia PDF Downloads 311

Authors: Pongsakorn Komutpol, Pravej Serichetaphongse, Soontra Panmekiate, Atiphan Pimkhaokham

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Statement of Problem: The important parameter of esthetic assessment in anterior maxillary implant include pink esthetic of gingiva and white esthetic of restoration. While the 3 dimensional (3D) implant position are recently concerned as a key for succeeding in implant treatment. However, to our knowledge, the authors did not come across any publication that demonstrated the relations of esthetic outcome and 3D implant position. Objectives: To investigate the correlation between positional accuracy of single-tooth implant restoration (STIR) in all 3 dimensions and their esthetic outcomes. Materials and Methods: 17 patients’ data who had a STIR at central incisor with pristine contralateral tooth were included in this study. Intraoral photographs, dental models, and cone beam computed tomography (CBCT) images were retrieved. The esthetic outcome was assessed in accordance with pink esthetic score and white esthetic score (PES/WES). While the number of correct position in each dimension (mesiodistal, labiolingual, apicocoronal) of the implant were evaluated and defined as 'right' or 'wrong' according to ITI consensus conference by one investigator using CBCT data. The different mean score between right and wrong position in all dimensions was analyzed by Mann-Whitney U test with 0.05 was the significant level of the study. Results: The average score of PES/WES was 15.88 ± 1.65 which was considered as clinically acceptable. The average PES/WES score in 1, 2 and 3 right dimension of the implant position were 16.71, 15.75 and 15.17 respectively. None of the implants placed wrongly in all three dimensions. Statistically significant difference of the PES/WES score was found between the implants that placed right in 3 dimensions and 1 dimension (p = 0.041). Conclusion: This study supported the principle of 3D position of implant. The more properly implant was placed, the higher esthetic outcome was found.

Keywords: accuracy, dental implant, esthetic, 3D implant position

Procedia PDF Downloads 147

Authors: Abdelrasoul, Khalid Ahmed Elrabie

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The study was conducted on Sahiwal cattle bulls maintained at the Artificial Breeding Complex, NDRI, Karnal, Hayana, India, to determine the effect of exercise on the sexual behavior and semen quality. Fourteen Sahiwal bulls were classified into two groups of seven each. Group-1, bulls were exercised by walking in a bull exerciser once a week one hour before semen collection, whereas bulls in group-2 were exercised daily. Sexual behavior and semen quality traits studied were: Reaction time (RT), Dismounting time (DMT), Total time taken in mounts (TTTM), Flehmen response (FR), Erection Score (ES), Protrusion Score (PS), Intensity of thrust (ITS), Temperament Score (TS), Libido Score (LS), Semen volume, Physical appearance, Mass activity, Initial progressive motility, Non-eosinophilic spermatozoa count (NESC) and post thaw motility percent. Data were analyzed by least squares technique. Group-2 showed significantly (p < 0.01) higher value in RT (sec), DMT (sec), TTTM (sec), ES, PS, ITS, LS, semen volume, semen color density and mass activity.

Keywords: exercise, Sahiwal bulls, semen quality, sexual behavior

Procedia PDF Downloads 306