A Decision Support System with Intelligent Recommendation for Multi-disciplinary Medical Treatment

Abstract

Recent years have witnessed an emerging trend for improving disease treatment by forming multi-disciplinary medical teams. The collaboration among specialists from multiple medical domains has been shown to be significantly helpful for designing comprehensive and reliable regimens, especially for incurable diseases. Although this kind of multi-disciplinary treatment has been increasingly adopted by healthcare providers, a new challenge has been introduced to the decision-making process—how to efficiently and effectively develop final regimens by searching for candidate treatments and considering inputs from every expert. In this article, we present a sophisticated decision support system called MdtDSS (a decision support system (DSS) for multi-disciplinary treatment (Mdt)), which is particularly developed to guide the collaborative decision-making in multi-disciplinary treatment scenarios. The system integrates a recommender system that aims to search for personalized candidates from a large-scale high-quality regimen pool and a voting system that helps collect feedback from multiple specialists without potential bias. Our decision support system optimally combines machine intelligence and human experience and helps medical practitioners make informed and accountable regimen decisions. We deployed the proposed system in a large hospital in Shanghai, China, and collected real-world data on large-scale patient cases. The evaluation shows that the proposed system achieves outstanding results in terms of high-quality multi-disciplinary treatment.

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