Search

Background: The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19., Methods: Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization., Results: Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747., Conclusion: C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Family Medicine and Primary Care.)

Background: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population., Methods: The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019-2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas., Results: LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used., Conclusion: Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

The aim of this paper was to review the literature on suicide ideation and intent in Malaysia. PsyINFO, PubMed, Medline databases from 1845 to 2012 and detailed manual search of local official reports from Ministry of Health, Malaysian Psychiatric Association and unpublished dissertations from 3 local universities providing postgraduate psychiatric training, were included in the current review. A total of 13 studies on suicide ideation and intent in Malaysia were found and reviewed. The review showed that research on suicide ideation and intent in Malaysia was fragmented and limited, at best. Approximately 50% of existing research on suicide ideation and intent simply focused on sociodemographic data. Fifty-four percent of the data were obtained from hospitals. No study has been conducted on treatment and interventions for suicide ideation and intent. None of the studies used validated suicide scales. The impact of culture was rarely considered. It was clear from the review that for researchers, clinicians and public health policy makers to gain a better understanding of suicide behavior especially suicide ideation and intent in Malaysia, more systematic and empirically stringent methodologies and research frameworks need to be used., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The Reasons For Living Inventory has been shown to have good psychometric properties in Western populations for the past three decades. The present study examined the psychometric properties and factor structure of English and Malay version of the Reasons For Living (RFL) Inventory in a sample of clinical outpatients in Malaysia. The RFL is designed to assess an individual's various reasons for not committing suicide. A total of 483 participants (283 with psychiatric illnesses and 200 with non-psychiatric medical illnesses) completed the RFL and other self-report instruments. Results of the EFA (exploratory factor analysis) and CFA (confirmatory factor analysis) supported the fit for the six-factor oblique model as the best-fitting model. The internal consistency of the RFL was α=.94 and it was found to be high with good concurrent, criterion and discriminative validities. Thus, the RFL is a reliable and valid instrument to measure the various reasons for not committing suicide among psychiatry and medical outpatients in Malaysia., (© 2014.)

The present study evaluated the psychometric properties and factor structure of the Satisfaction With Life Scale (SWLS) in a sample of clinical outpatients in Malaysia. The SWLS is a measure designed to assess subjective life satisfaction. Four hundred eighty-three participants (283 with psychiatric illnesses and 200 with other medical illnesses) completed the SWLS and other self-report instruments. Results of the EFA and CFA supported the fit for the one-factor model as the best-fitting model. The internal consistency of the SWLS (α=0.86) was found to be high. Correlational analyses showed that SWLS had adequate concurrent validity. Scores on SWLS, which differentiated psychiatric patients and medical patients, supported criterion validity. The logistic regression analyses showed good discriminative validity of SWLS. The SWLS is a reliable and valid instrument to measure the satisfaction with life among psychiatry and clinical outpatients in Malaysia., (Copyright © 2014 Elsevier Inc. All rights reserved.)