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Mining and Model Understanding on Medical Data
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery (ACM), 2019, p. 3223-3224Conference paper, Published paper (Refereed)
Abstract [en]

What are the basic forms of healthcare data? How are Electronic Health Records and Cohorts structured? How can we identify the key variables in such data and how important are temporal abstractions? What are the main challenges in knowledge extraction from medical data sources? What are the key machine algorithms used for this purpose? What are the main questions that clinicians and medical experts pose to machine learning researchers?

In this tutorial, we provide answers to these questions by presenting state-of-the-art methods, workflows, and tools for mining and understanding medical data. Particular emphasis is given on temporal abstractions, knowledge extraction from cohorts, machine learning model interpretability, and mHealth.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2019. p. 3223-3224
Keywords [en]
cohorts, deep learning, electronic health records, interpretability, medical mining
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177166DOI: 10.1145/3292500.3332274ISBN: 978-1-4503-6201-6 (electronic)OAI: oai:DiVA.org:su-177166DiVA, id: diva2:1379886
Conference
KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage AK USA, August 4 - 8, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-07Bibliographically approved

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More languages
Output format
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