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In clinical care, obtaining a correct diagnosis is the first step towards successful treatment and, ultimately, recovery. Depending on the complexity of the case, the diagnostic phase can be lengthy and ridden with errors and delays. Such errors have a high likelihood to cause patients severe harm or even lead to their death and are estimated to cost the U.S. healthcare system several hundred billion dollars each year. To avoid diagnostic errors, physicians increasingly rely on diagnostic decision support systems drawing from heuristics, historic cases, textbooks, clinical guidelines and scholarly biomedical literature. The evaluation of such systems, however, is often conducted in an ad-hoc fashion, using non-transparent methodology, and proprietary data. This paper presents DC3, a collection of 31 extremely difficult diagnostic case challenges, manually compiled and solved by clinical experts. For each case, we present a number of temporally ordered physician-generated observations alongside the eventually confirmed true diagnosis. We additionally provide inferred dense relevance judgments for these cases among the PubMed collection of 27 million scholarly biomedical articles.
In this paper, we investigate how semantic relations between concepts extracted from medical documents can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement lear
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which sh
Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This
The COVID-19 crisis has brought about new clinical questions, new workflows, and accelerated distributed healthcare needs. While artificial intelligence (AI)-based clinical decision support seemed to have matured, the application of AI-based tools fo