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Deep learning model estimates cancer patient health from records

Paper Authors:

Aya Nakamura,

Ryosuke Kojima,

Yuji Okamoto,

Eiichiro Uchino,

Yohei Mineharu,

Yohei Harada,

Mayumi Kamada,

Manabu Muto,

Motoko Yanagita,

Yasushi Okuno

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Key Details

Developed deep learning model to estimate latent disease states from EHRs over time

Applied model to 12,695 cancer patients undergoing chemotherapy

Revealed latent states correlating with prognosis and mortality

Visualized interpretable transitions in disease state over time

Identified key clinical factors during critical state transitions

AI generated summary

Deep learning model estimates cancer patient health from records

Researchers developed a deep learning model to analyze electronic health records over time and estimate latent disease states in cancer patients. This could help understand disease progression and guide treatment. The model was applied to records of 12,695 cancer patients, revealing interpretable latent states related to prognosis. Visualizing state transitions showed progression from stable to unstable to death. Analyzing by drug identified important clinical factors during critical transitions.

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