The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.
Freddy Gabbay received his B.Sc., M.Sc. and Ph.D. in Electrical Engineering from the Technion – Israel Institute of Technology, Haifa, Israel. In 1998, he worked as a researcher at Intel’s Microprocessor Research Lab. In 1999 he joined Mellanox Technologies and held various positions in leading switch product line architecture and ASIC design. In 2003, he joined Freescale Semiconductor as a senior design manager and led the design of baseband ASIC products. In 2012 he rejoined Mellanox Technologies where he served as Vice President of Chip Design. Today he is an associate professor and the head of the Computer and Information Sciences Department at the Ruppin Academic Center, Emek Hefer, Israel. His research interests include VLSI design, computer architecture, machine learning and domain-specific accelerators. Prof. Gabbay holds 19 patents and is a senior member of IEEE and IEEE Computer Society.
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