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تسجيل مستخدم جديدRisk factor identification for incident heart failure using neural network distillation and variable selection
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Recent evidence shows that deep learning models trained on electronic health records from millions of patients can deliver substantially more accurate predictions of risk compared to their statistical counterparts. While this provides an important opportunity for improving clinical decision-making, the lack of interpretability is a major barrier to the incorporation of these black-box models in routine care, limiting their trustworthiness and preventing further hypothesis-testing investigations. In this study, we propose two methods, namely, model distillation and variable selection, to untangle hidden patterns learned by an established deep learning model (BEHRT) for risk association identification. Due to the clinical importance and diversity of heart failure as a phenotype, it was used to showcase the merits of the proposed methods. A cohort with 788,880 (8.3% incident heart failure) patients was considered for the study. Model distillation identified 598 and 379 diseases that were associated and dissociated with heart failure at the population level, respectively. While the associations were broadly consistent with prior knowledge, our method also highlighted several less appreciated links that are worth further investigation. In addition to these important population-level insights, we developed an approach to individual-level interpretation to take account of varying manifestation of heart failure in clinical practice. This was achieved through variable selection by detecting a minimal set of encounters that can maximally preserve the accuracy of prediction for individuals. Our proposed work provides a discovery-enabling tool to identify risk factors in both population and individual levels from a data-driven perspective. This helps to generate new hypotheses and guides further investigations on causal links.
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Predicting the incidence of complex chronic conditions such as heart failure is challenging. Deep learning models applied to rich electronic health records may improve prediction but remain unexplainable hampering their wider use in medical practice.
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Mistura Muibideen
, Rajesh Prasad (Department of Computer Sciencen African University of Science
, Technology
2020
Cardiovascular disease is the number one cause of death all over the world. Data mining can help to retrieve valuable knowledge from available data from the health sector. It helps to train a model to predict patients health which will be faster as c
ompared to clinical experimentation. Various implementation of machine learning algorithms such as Logistic Regression, K-Nearest Neighbor, Naive Bayes (NB), Support Vector Machine, etc. have been applied on Cleveland heart datasets but there has been a limit to modeling using Bayesian Network (BN). This research applied BN modeling to discover the relationship between 14 relevant attributes of the Cleveland heart data collected from The UCI repository. The aim is to check how the dependency between attributes affects the performance of the classifier. The BN produces a reliable and transparent graphical representation between the attributes with the ability to predict new scenarios. The model has an accuracy of 85%. It was concluded that the model outperformed the NB classifier which has an accuracy of 80%.
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