Search results for: clinical prediction score

Authors: Hatem A. El-Mezayen, Hossam Darwesh

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Background/Aim: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stage where effective therapies are lacking. Identification of new scoring system is needed to discriminate HCC patients from those with chronic liver disease. Based on the link between vascular endothelial growth factor (VEGF) and HCC progression, we aimed to develop a novel score based on combination of VEGF and routine laboratory tests for early prediction of HCC. Methods: VEGF was assayed for HCC group (123), liver cirrhosis group (210) and control group (50) by Enzyme Linked Immunosorbent Assay (ELISA). Data from all groups were retrospectively analyzed including α feto protein (AFP), international normalized ratio (INR), albumin and platelet count, transaminases, and age. Areas under ROC curve were used to develop the score. Results: A novel index named hepatocellular carcinoma-vascular endothelial growth factor score (HCC-VEGF score)=1.26 (numerical constant) + 0.05 ×AFP (U L-1)+0.038 × VEGF(ng ml-1)+0.004× INR –1.02 × Albumin (g l-1)–0.002 × Platelet count × 109 l-1 was developed. HCC-VEGF score produce area under ROC curve of 0.98 for discriminating HCC patients from liver cirrhosis with sensitivity of 91% and specificity of 82% at cut-off 4.4 (ie less than 4.4 considered cirrhosis and greater than 4.4 considered HCC). Conclusion: Hepatocellular carcinoma-VEGF score could replace AFP in HCC screening and follow up of cirrhotic patients.

Keywords: Hepatocellular carcinoma, cirrhosis, HCV, diagnosis, tumor markers

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Authors: Wafaa M. El- Kotb, Soheir Mohamed Weheida, Manal E. Fareed

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The aim of this study was to determine the effect of implementing a teaching module about diet and exercises on clinical outcomes of patients with gout. Subjects: A purposive sample of 60 adult gouty patients was selected and randomly and alternatively divided into two equal groups 30 patients in each. Setting: The study was conducted in orthopedic out patient's clinic of Menoufia University. Tools of the study: Three tools were utilized for data collection: Knowledge assessment structured interview questionnaire, Clinical manifestation assessment tools and Nutritional assessment sheet. Results: All patients of both groups (100 %) had poor total knowledge score pre teaching, while 90 % of the study group had good total knowledge score post teaching by three months compared to 3.3 % of the control group. Moreover the recovery outcomes were significantly improved among study group compared to control group post teaching. Conclusion: Teaching study group about diet and exercises significantly improved their clinical outcomes. Recommendation: Patient's education about diet and exercises should be ongoing process for patients with gout.

Keywords: clinical outcomes, diet, exercises, teaching module

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Authors: Devina Aprilia Gunawan, Tasya Aspiranti, Inugrah Ratia Pratiwi

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This research aims to classify the mining sector companies based on Altman’s Z-score model, and providing an analysis based on the Altman’s Z-score model’s financial ratios to provide a picture about the financial condition in mining sector companies in Indonesia and their viability in the future, and to find out the partial and simultaneous impact of each of the financial ratio variables in the Altman’s Z-score model, namely (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), toward the financial condition represented by the Z-score itself. Among 38 mining sector companies listed in Indonesia Stock Exchange (IDX), 28 companies are selected as research sample according to the purposive sampling criteria.The results of this research showed that during 3 years research period at 2010-2012, the amount of the companies that was predicted to be healthy in each year was less than half of the total sample companies and not even reach up to 50%. The multiple regression analysis result showed that all of the research hypotheses are accepted, which means that (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), both partially and simultaneously had an impact towards company’s financial condition.

Keywords: Altman’s Z-score model, financial condition, mining companies, Indonesia

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: Christelle Navarro, Sébastien Viaud, Carole Gard, Bruno Jahier

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Background: Idiopathic or familial nasal hyperkeratosis (NHK) may be considered a cosmetic issue in its uncomplicated form. Nevertheless, prevention of secondary lesions such as fissures or infections could be advised by proper management. The objective of this open-field study is to evaluate the benefits of a moisturizing balm in privately owned dogs with NHK, using an original validation grid for both investigator and owner assessments. Methods: Dogs with idiopathic or familial NHK received a vegetable-based ointment (Sensiderm® Balm, MP Labo, France) BID for 60 days. A global dermatological score (GDS) was defined using the sum of 4 criteria (“dryness,” “lichenification”, “crusts,” and “affected area”) on a 0 (no) to 3 (severe or > 2/3 extension) scale. Evaluation of this GDS (0-12) on D0, D30, and D60, by owners and investigators was the main outcome. The score’s percentage decrease versus D0, the evolution of each individual score, the correlation between observers, and the evaluation of clinical improvement and animal discomfort on VAS (0-10) during follow-up were analysed. Results: The global dermatological score significantly decreased over time (p<0.0001) for all observers. The decrease reached 44.9% and 54.3% at D30 and 54.5% and 62.3% at D60, for investigators and owners, respectively. “Dryness”, “Lichenification,” and “Affected area scores” decreased significantly and steadily over time compared to Day 0 for both investigators and owners (p < 0.001 and p = 0.001 for investigator assessment of dryness). All but one score (lichenification) were correlated at all times between observers (only at D60 for crusts). Whoever the observer, clinical improvement was always above 7. At D30 and until D60, “animal discomfort” was more than halved. Owner satisfaction was high as soon as D30 (8.1/10). No adverse effects were reported. Conclusion and clinical importance: The positive results confirm the benefits and safety of a moisturizing balm when used in dogs with uncomplicated NHK.

Keywords: hyperkeratosis, nose, dog, moisturizer

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Swati Srivastava, Ashish Kumar Upadhyay

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Using longitudinal data from three waves of Young Lives Study and Ordinary Least Square methods, study has investigated the effect of birth order and arrival of younger siblings on child development in India. Study used child’s height for age z-score, weight for age z-score, BMI for age z-score, Peabody Picture Vocabulary Test (PPVT-Score)c, maths score, Early Grade Reading Assessment Test (ERGA) score, and memory score to measure the physical and cognitive development of child during wave-3. Findings suggest that having a high birth order is detrimental for child development and the gap between adjacent siblings is larger for children late in the birth sequences than early in the birth sequences. Study also reported that not only older siblings but arrival of younger siblings before assessment of test also reduces the development of a child. The effects become stronger in case of female children than male children.

Keywords: height for age z-score, weight for age z-score, BMI for z-score, PPVT score, math score, EGRA score, memory score, birth order, siblings, Young Lives Study, India

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Authors: Kamalpreet Kaur, Renu Dhir

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Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%.

Keywords: climate, satellite images, prediction, classification

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Authors: Stefaan Carpentier, Aya Al Masri, Fabrice Leroy, Thibault Julien, Safoin Aktaou, Malorie Martin, Fouad Maaloul

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Introduction: During an 'Interventional Radiology (IR)' procedure, the patient's skin-dose may become very high for a burn, necrosis, and ulceration to appear. In order to prevent these deterministic effects, a prediction of the peak skin-dose for the patient is important in order to improve the post-operative care to be given to the patient. The objective of this study is to estimate, before the intervention, the patient dose for ‘Chronic Total Occlusion (CTO)’ procedures by selecting relevant clinical indicators. Materials and methods: 103 procedures were performed in the ‘Interventional Cardiology (IC)’ department using a Siemens Artis Zee image intensifier that provides the Air Kerma of each IC exam. Peak Skin Dose (PSD) was measured for each procedure using radiochromic films. Patient parameters such as sex, age, weight, and height were recorded. The complexity index J-CTO score, specific to each intervention, was determined by the cardiologist. A correlation method applied to these indicators allowed to specify their influence on the dose. A predictive model of the dose was created using multiple linear regressions. Results: Out of 103 patients involved in the study, 5 were excluded for clinical reasons and 2 for placement of radiochromic films outside the exposure field. 96 2D-dose maps were finally used. The influencing factors having the highest correlation with the PSD are the patient's diameter and the J-CTO score. The predictive model is based on these parameters. The comparison between estimated and measured skin doses shows an average difference of 0.85 ± 0.55 Gy for doses of less than 6 Gy. The mean difference between air-Kerma and PSD is 1.66 Gy ± 1.16 Gy. Conclusion: Using our developed method, a first estimate of the dose to the skin of the patient is available before the start of the procedure, which helps the cardiologist in carrying out its intervention. This estimation is more accurate than that provided by the Air-Kerma.

Keywords: chronic total occlusion procedures, clinical experimentation, interventional radiology, patient's peak skin dose

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Authors: Aml M. El-Sharkawy, Hossam M. Darwesh

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Abstract— Background/Aim: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stage where effective therapies are lacking. Identification of new scoring system is needed to discriminate HCC patients from those with chronic liver disease. Based on the link between tumor associated trypsin inhibitor (TATI) and HCC progression, we aimed to develop a novel score based on combination of TATI and routine laboratory tests for early prediction of HCC. Methods: TATI was assayed for HCC group (123), liver cirrhosis group (210) and control group (50) by Enzyme Linked Immunosorbent Assay (ELISA). Data from all groups were retrospectively analyzed including α feto protein (AFP), international normalized ratio (INR), albumin and platelet count, transaminases, and age. Areas under ROC curve were used to develop the score. Results: A novel index named hepatocellular carcinoma-vascular endothelial growth factor score (HCC-TATI score) = 3.1 (numerical constant) + 0.09 ×AFP (U L-1) + 0.067 × TATI (ng ml-1) + 0.16 × INR – 1.17 × Albumin (g l-1) – 0.032 × Platelet count × 109 l-1 was developed. HCC-TATI score produce area under ROC curve of 0.98 for discriminating HCC patients from liver cirrhosis with sensitivity of 91% and specificity of 82% at cut-off 6.5 (ie less than 6.5 considered cirrhosis and greater than 4.4 considered HCC). Conclusion: Hepatocellular carcinoma-TATI score could replace AFP in HCC screening and follow up of cirrhotic patients.

Keywords: Hepatocellular carcinoma, cirrhosis, HCV, diagnosis, TATI

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Osama Moustafa Zayed

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Aim of the work: To evaluate the effectiveness of venous glucose, levels of serum lactate and base deficit in polytraumatized patients as simple parameters to predict the mortality in these patients. Compared to the predictive value of Trauma and injury severity (TRISS) and Acute Physiology And Chronic Health Evaluation IV (APACHE IV). Introduction: Trauma is a serious global health problem, accounting for approximately one in 10 deaths worldwide. Trauma accounts for 5 million deaths per year. Prediction of mortality in trauma patients is an important part of trauma care. Several trauma scores have been devised to predict injury severity and risk of mortality. The trauma and injury severity score (TRISS) was most common used. Regardless of the accuracy of trauma scores, is based on an anatomical description of every injury and cannot be assigned to the patients until a full diagnostic procedure has been performed. So we hypothesized that alterations in admission glucose, lactate levels and base deficit would be an early and easy rapid predictor of mortality. Patient and Method: a comparative cross-sectional study. 282 Polytraumatized patients attended to the Emergency Department(ED) of the Suez Canal university Hospital constituted. The period from 1/1/2012 to 1/4/2013 was included. Results: We found that the best cut off value of TRISS probability of survival score for prediction of mortality among poly-traumatized patients is = 90, with 77% sensitivity and 89% specificity using area under the ROC curve (0.89) at (95%CI). APACHE IV demonstrated 67% sensitivity and 95% specificity at 95% CI at cut off point 99. The best cutoff value of Random Blood Sugar (RBS) for prediction of mortality was>140 mg/dl, with 89%, sensitivity, 49% specificity. The best cut off value of base deficit for prediction of mortality was less than -5.6 with 64% sensitivity, 93% specificity. The best cutoff point of lactate for prediction of mortality was > 2.6 mmol/L with 92%, sensitivity, 42% specificity. Conclusion: According to our results from all evaluated predictors of mortality (laboratory and scores) and mortality based on the estimated cutoff values using ROC curves analysis, the highest risk of mortality was found using a cutoff value of 90 in TRISS score while with laboratory parameters the highest risk of mortality was with serum lactate > 2.6 . Although that all of the three parameter are accurate in predicting mortality in poly-traumatized patients and near with each other, as in serum lactate the area under the curve 0.82, in BD 0.79 and 0.77 in RBS.

Keywords: APACHE IV, emergency department, polytraumatized patients, serum lactate

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Authors: Wangxu Xia

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BACKGROUND BI-RADS 4 lesions raise the possibility of malignancy that warrant further clinical and radiologic work-up. This study aimed to evaluate the predictive performance of diffusion-weighted imaging(DWI) and microcalcifications on mammography for predicting malignancy of BI-RADS 4 lesions. In addition, the predictive performance of DWI combined with microcalcifications was alsocompared with the Kaiser score. METHODS During January 2021 and June 2023, 144 patients with 178 BI-RADS 4 lesions underwent conventional MRI, DWI, and mammography were included. The lesions were dichotomized intobenign or malignant according to the pathological results from core needle biopsy or surgical mastectomy. DWI was performed with a b value of 0 and 800s/mm2 and analyzed using theapparent diffusion coefficient, and a Kaiser score > 4 was considered to suggest malignancy. Thediagnostic performances for various diagnostic tests were evaluated with the receiver-operatingcharacteristic (ROC) curve. RESULTS The area under the curve (AUC) for DWI was significantly higher than that of the of mammography (0.86 vs 0.71, P<0.001), but was comparable with that of the Kaiser score (0.86 vs 0.84, P=0.58). However, the AUC for DWI combined with mammography was significantly highthan that of the Kaiser score (0.93 vs 0.84, P=0.007). The sensitivity for discriminating malignant from benign BI-RADS 4 lesions was highest at 89% for Kaiser score, but the highest specificity of 83% can be achieved with DWI combined with mammography. CONCLUSION DWI combined with microcalcifications on mammography could discriminate malignant BI-RADS4 lesions from benign ones with a high AUC and specificity. However, Kaiser score had a better sensitivity for discrimination.

Keywords: MRI, DWI, mammography, breast disease

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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: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

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Authors: Lubena Achmad, Herman Kristanto, Julian Dewantiningrum

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Background: The Bishop score is a standard method used to predict the success of induction. This examination tends to be subjective with high inter and intraobserver variability, so it was presumed to have a low predictive value in terms of the outcome of labor induction. Cervical length measurement using transvaginal ultrasound is considered to be more objective to assess the cervical length. Meanwhile, this examination is not a complicated procedure and less invasive than vaginal touché. Objective: To compare transvaginal ultrasound and Bishop score in predicting successful induction. Methods: This study was a prospective cohort study. One hundred and twenty women with singleton pregnancies undergoing induction of labor at 37 – 42 weeks and met inclusion and exclusion criteria were enrolled in this study. Cervical assessment by both transvaginal ultrasound and Bishop score were conducted prior induction. The success of labor induction was defined as an ability to achieve active phase ≤ 12 hours after induction. To figure out the best cut-off point of cervical length and Bishop score, receiver operating characteristic (ROC) curves were plotted. Logistic regression analysis was used to determine which factors best-predicted induction success. Results: This study showed significant differences in terms of age, premature rupture of the membrane, the Bishop score, cervical length and funneling as significant predictors of successful induction. Using ROC curves found that the best cut-off point for prediction of successful induction was 25.45 mm for cervical length and 3 for Bishop score. Logistic regression was performed and showed only premature rupture of membranes and cervical length ≤ 25.45 that significantly predicted the success of labor induction. By excluding premature rupture of the membrane as the indication of induction, cervical length less than 25.3 mm was a better predictor of successful induction. Conclusion: Compared to Bishop score, cervical length using transvaginal ultrasound was a better predictor of successful induction.

Keywords: Bishop Score, cervical length, induction, successful induction, transvaginal sonography

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Authors: Phornchanok Motantasut, Torkamol Hunsawong, Lugkana Mato, Wanida Donpunha

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Identifying abnormal foot posture is important for prescribing appropriate management in patients with lower limb disorders and chronic non-specific low back pain. The normalized navicular height truncated (NNHt) and the foot posture index-6 (FPI-6) have been recommended as the common, simple, valid, and reliable static measures for clinical application. The NNHt is a single plane measure while the FPI-6 is a triple plane measure. At present, there is inadequate information about the correlation between the NNHt and the FPI-6 for categorizing foot posture that leads to a difficulty of choosing the appropriate assessment. Therefore, the present study aimed to determine the correlation between the NNHt and the FPI-6 measures in adult participants with asymptomatic feet. Methods: A cross-sectional descriptive study was conducted in 47 asymptomatic individuals (23 males and 24 females) aged 28.89 ± 7.67 years with body mass index 21.73 ± 1.76 kg/m². The right foot was measured twice by the experienced rater using the NNHt and the FPI-6. A sequence of the measures was randomly arranged for each participant with a 10-minute rest between the tests. The Pearson’s correlation coefficient (r) was used to determine the relationship between the measures. Results: The mean NNHt score was 0.23 ± 0.04 (ranged from 0.15 to 0.36) and the mean FPI-6 score was 4.42 ± 4.36 (ranged from -6 to +11). The Pearson’s correlation coefficient among the NNHt score and the FPI-6 score was -0.872 (p < 0.01). Conclusion: The present finding demonstrates the strong correlation between the NNHt and FPI-6 in adult feet and implies that both measures could be substituted for each other in identifying foot posture.

Keywords: foot posture index, foot type, measurement of foot posture, navicular height

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Authors: Sanela Slavkovic, Congor Nad, Spela Golubovic

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Background: The diagnosis of multiple sclerosis (MS) is a major life challenge and has repercussions on all aspects of the daily functioning of those attained by it – personal activities, social participation, and quality of life. Regular follow-up of only the neurological status is not informative enough so that it could provide data on the sort of support and rehabilitation that is required. Objective: The aim of this study was to establish the current level of functioning of persons attained by MS and the factors that influence it. Methods: The study was conducted in Serbia, on a sample of 108 persons with relapse-remitting form of MS, aged 20 to 53 (mean 39.86 years; SD 8.20 years). All participants were fully ambulatory. Methods applied in the study include Expanded Disability Status Scale-EDSS and World Health Organization Disability Assessment Schedule, WHODAS 2.0 (36-item version, self-administered). Results: Participants were found to experience the most problems in the domains of Participation, Mobility, Life activities and Cognition. The least difficulties were found in the domain of Self-care. Symptom duration was the only control variable with a significant partial contribution to the prediction of the WHODAS scale score (β=0.30, p < 0.05). The total EDSS score correlated with the total WHODAS 2.0 score (r=0.34, p=0.00). Statistically significant differences in the domain of EDSS 0-5.5 were found within categories (0-1.5; 2-3.5; 4-5.5). The more pronounced a participant’s EDSS score was, although not indicative of large changes in the neurological status, the more apparent the changes in the functional domain, i.e. in all areas covered by WHODAS 2.0. Pyramidal (β=0.34, p < 0.05) and Bowel and bladder (β=0.24, p < 0.05) functional systems were found to have a significant partial contribution to the prediction of the WHODAS score. Conclusion: Measuring functioning and disability is important in the follow-up of persons suffering from MS in order to plan rehabilitation and define areas in which additional support is needed.

Keywords: disability, functionality, multiple sclerosis, rehabilitation

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Authors: Philippe Dillien, Patrice Forget, Harald Engel, Olivier Cornu, Marc De Kock, Jean Cyr Yombi

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Introduction: Hip fracture precedes commonly death in elderly people. Identification of high-risk patients may contribute to target patients in whom optimal management, resource allocation and trials efficiency is needed. The aim of this study is to construct a predictive score of mortality after hip fracture on the basis of the objective prognostic factors available: Neutrophil-to-lymphocyte ratio (NLR), age, and sex. C-Reactive Protein (CRP), is also considered as an alternative to the NLR. Patients and methods: After the IRB approval, we analyzed our prospective database including 286 consecutive patients with hip fracture. A score was constructed combining age (1 point per decade above 74 years), sex (1 point for males), and NLR at postoperative day+5 (1 point if >5). A receiver-operating curve (ROC) curve analysis was performed. Results: From the 286 patients included, 235 were analyzed (72 males and 163 females, 30.6%/69.4%), with a median age of 84 (range: 65 to 102) years, mean NLR values of 6.47+/-6.07. At one year, 82/280 patients died (29.3%). Graphical analysis and log-rank test confirm a highly statistically significant difference (P<0.001). Performance analysis shows an AUC of 0.72 [95%CI 0.65-0.79]. CRP shows no advantage on NLR. Conclusion: We have developed a score based on age, sex and the NLR to predict the risk of mortality at one year in elderly patients after surgery for a hip fracture. After external validation, it may be included in clinical practice as in clinical research to stratify the risk of postoperative mortality.

Keywords: neutrophil-to-lymphocyte ratio, hip fracture, postoperative mortality, medical and health sciences

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Authors: N. H. Adenan, M. S. M. Noorani

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River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources.

Keywords: river flow, nonlinear prediction method, phase space, local linear approximation

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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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: Khader A. Almhdawi, Saddam F. Kanaan

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Background: Allied health (AH) students, including rehabilitation sciences students, are subjected to significant levels of physical and mental stressors, which could affect their education. This study aimed to study physical and mental of Health-related Quality of Life (HR-QoL) levels along with their predictors among students of nine AH majors. Methods: Students filled validated anonymous surveys covering demographics and life style, Nordic Musculoskeletal Questionnaire, 12-item Short-Form Health Survey (SF-12), and Depression Anxiety Stress Scale (DASS- 42). SF-12 Mental (MCS) and Physical (PCS) summary scores were compared between academic majors and gender. Multiple linear regression models were conducted to examine potential predictors of PCS and MCS scores. Results: 838 students (77.4% females) participated in this study. Participants’ PCS mean score was 45.64±7.93 and found statistically different between the nine academic majors (P < 0.001). Additionally, participants’’ MCS mean score was 39.45±10.86 and significantly greater in males (P < 0.001). Significant PCS scores predictors included hip and upper back musculoskeletal pain, anxiety score, diet self-evaluation, and GPA. Finally, MCS scores were statistically associated with neck musculoskeletal pain, stress score, depression score, number of weekly clinical training hours, gender, university year, GPA, sleep quality self-evaluation, and diet self-evaluation. Conclusion: Clinical educators of AH need to account for students’ low levels of HR-QoL and their academic-related, health-related, and lifestyle-related associated factors. More studies are recommended to investigate the progression of HR-QoL throughout university years and to create effective interventions to improve HR-QoL among healthcare students.

Keywords: medical education, quality of life, stress, anxiety, depression

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Authors: Ramy Shaaban

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The purpose of this study was to examine the difference in clinical reasoning skills of students when using virtual audiology cases with and without collaborative assistance from major learning approaches important to clinical reasoning skills and computer-based learning models: Situated Learning Theory, Social Development Theory, Scaffolding, and Collaborative Learning. A quasi-experimental design was conducted at two United States universities to examine whether there is a significant difference in clinical reasoning skills between three treatment groups using IUP Audiosim software. Two computer-based audiology case simulations were developed, and participants were randomly placed into the three groups: no collaboration, virtual collaboration, and live collaboration. The clinical reasoning data were analyzed using One-Way ANOVA and Tukey posthoc analyses. The results show that there was a significant difference in clinical reasoning skills between the three treatment groups. The score obtained by the no collaboration group was significantly less than the scores obtained by the virtual and live collaboration groups. Collaboration, whether virtual or in person, has a positive effect on students’ clinical reasoning. These results with audiology students indicate that combining collaboration models with scaffolding and embedding situated learning and social development theories into the design of future virtual patients has the potential to improve students’ clinical reasoning skills.

Keywords: clinical reasoning, virtual patients, collaborative learning, scaffolding

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

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Authors: Bliss Singhal

Abstract:

Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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

Abstract:

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: Ravija Gunawardana, Banuka Athuraliya

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Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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