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Minimal algorithms for knowledge representation in clinical decision support systems research: a theoretical and empirical analysis

Eduardo, A.; Loureiro, R. M.; Tachibana, A.; Netto, P. V.; Almeida, T. F. d.; Monteiro, L. H. A.; Santos, A. P. d.

2022-04-21 health informatics
10.1101/2022.04.20.22274099 medRxiv
Show abstract

Clinical decision support systems (CDSS) figures out as one of the most promising technologies for data-centered and AI-prompted healthcare. Its current developments are mainly guided by two disparate mindsets, namely a machine learning-centered framework and a classical rule-based framework. These respective approaches presents contrastive pros and cons. In the present study we provide an analysis showing that these two mindsets are actually related to each other, and straightforward algorithms are feasible by combining current standards for machine learning and classic decision tables algorithms. A theoretical analysis are provided, as well a computational implementation (in python). A real case scenario on radiological immaging exam prescription is used to ilustrate the successfully application of our results. Future work on benchmarking the proposed algorithms embodied in a fully operational clinical decision support system could extend our findings towards daily used systems.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.