Patient Disease Prediction and Medical Feature Extraction using Matrix Factorization
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- SUMIYA Yuki
- Tokyo Institute of Technology
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- MATSUDA Atsuyoshi
- Logbii, Inc.
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- ARAKI Kenji
- University of Miyazaki Hospital
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- NAKATA Kazuhide
- Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- 行列因子分解を使用した個別患者ごとの疾病予測および医療事象の特徴表現抽出
Abstract
<p>It is expected to extract and apply useful information from the big EMR data. In particular, "prediction / prevention of diseases that patients may develop" and "analysis of features and relationships of medical events" will help solve problems caused by the shortage of doctors. In order to achieve these goals, we used Matrix factorization-based methods to "A: calculate the risk of developing each disease for each patient" and "B: obtain and analyze the feature representations of patients, diseases, and patient characteristics". However, it was difficult to apply the existing methods. In this study, we developed a new method called PCMF to solve concerns about these methods. Then, by applying PCMF to the EMR data, we aimed to achieve the above A and B simultaneously. Our experiments showed PCMF predicts future diseases more accurately than other methods. We also analyzed the obtained feature representations, and showed PCMF can extract useful information.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2021 (0), 2G3GS2e03-2G3GS2e03, 2021
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390288370504095616
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- NII Article ID
- 130008051596
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed