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Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.PMID:33965930 | DOI:10.3233/SHTI210100
Source: Studies in Health Technology and Informatics - May 9, 2021 Category: Information Technology Authors: Michael Schrempf Diether Kramer Stefanie Jauk Sai P K Veeranki Werner Leodolter Peter P Rainer Source Type: research

CBN: Constructing a Clinical Bayesian Network based on Data from the Electronic Medical Record
Publication date: Available online 3 November 2018Source: Journal of Biomedical InformaticsAuthor(s): Ying Shen, Lizhu Zhang, Jin Zhang, Min Yang, Buzhou Tang, Yaliang Li, Kai LeiAbstractThe process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise stat...
Source: Journal of Biomedical Informatics - November 4, 2018 Category: Information Technology Source Type: research

Validation of an algorithm that determines stroke diagnostic code accuracy in a Japanese hospital-based cancer registry using electronic medical records
This study aimed to validate an algorithm that determines stroke diagnostic code accuracy, in a hospital-based cancer registry, using electronic medical records (EMRs) in Japan.
Source: BMC Medical Informatics and Decision Making - December 4, 2017 Category: Information Technology Authors: Yasufumi Gon, Daijiro Kabata, Keichi Yamamoto, Ayumi Shintani, Kenichi Todo, Hideki Mochizuki and Manabu Sakaguchi Tags: Research article Source Type: research