Patient Clustering via Integrated Profiling of Clinical and Digital Data
Authors: 
Dongjin Choi, Andy Xiang, Ozgur Ozturk, Deep Shrestha, Barry Drake, Hamid Haidarian, Faizan Javed, Haesun ParkAuthors Info & Claims
Pages 3818 - 3822
Abstract
We introduce a novel profile-based patient clustering model designed for healthcare clinical data. By utilizing a method grounded on constrained low-rank approximation, our model takes advantage of patients' clinical data and digital interaction data, including browsing and search, to construct patient profiles. As a result of the method, nonnegative embedding vectors are generated, serving as a low-dimensional representation of the patients. Our model was assessed using real-world patient data from a healthcare web portal, with a comprehensive evaluation approach which considered clustering and recommendation capabilities. In comparison to other baselines, our approach demonstrated superior performance in terms of clustering coherence and recommendation accuracy.
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Index Terms
- Patient Clustering via Integrated Profiling of Clinical and Digital Data
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October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
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- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
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Published: 21 October 2023
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
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