Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

Autor: Katsuki, Takayuki, Miyaguchi, Kohei, Koseki, Akira, Iwamori, Toshiya, Yanagiya, Ryosuke, Suzuki, Atsushi

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions

Externí odkaz: http://arxiv.org/abs/2204.13451

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1040-P: Survival Models of Diabetes Complications Applied to Different Cohorts.

Autor: KOSEKI, AKIRA, TOKUMASU, REITARO, CHAKRABORTY, PRITHWISH, GHALWASH, MOHAMED, IWAMORI, TOSHIYA, KUDO, MICHIHARU, SOW, DABY, YANAGISAWA, HIROKI, YANAGIYA, RYOSUKE, MAKINO, MASAKI, SUZUKI, ATSUSHI

Publikováno v: Diabetes; 2021 Supplement 1, Vol. 70, pN.PAG-N.PAG, 1p

Risk Prediction of Diabetic Nephropathy via Interpretable Feature Extraction from EHR Using Convolutional Autoencoder.

Autor: Katsuki T; IBM Research - Tokyo, Japan., Ono M; IBM Research - Tokyo, Japan., Koseki A; IBM Research - Tokyo, Japan., Kudo M; IBM Research - Tokyo, Japan., Haida K; Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Japan., Kuroda J; IT Business Process Planning Department, The Dai-ichi Life Insurance Company, Limited, Japan., Makino M; Division of Endocrinology and Metabolism, Department of Internal Medicine, Fujita Health University, Japan., Yanagiya R; Division of Medical Information Systems, Fujita Health University, Japan., Suzuki A; Division of Endocrinology and Metabolism, Department of Internal Medicine, Fujita Health University, Japan.

Publikováno v: Studies in health technology and informatics [Stud Health Technol Inform] 2018; Vol. 247, pp. 106-110.