Real-time risk prediction of colorectal surgery-related post-surgical complications using GRU-D model.

Ruan, Xiaoyang; Fu, Sunyang; Storlie, Curtis B; Mathis, Kellie L; Larson, David W; Liu, Hongfang

Ruan, Xiaoyang; Fu, Sunyang; Storlie, Curtis B; Mathis, Kellie L; Larson, David W; Liu, Hongfang.

Affiliation

Ruan X; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
Fu S; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
Storlie CB; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Mathis KL; Department of Surgery, Mayo Clinic, Rochester, MN, United States.
Larson DW; Department of Surgery, Mayo Clinic, Rochester, MN, United States.
Liu H; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States. Electronic address: liu.hongfang@mayo.edu.

J Biomed Inform ; 135: 104202, 2022 11.

Article in En | MEDLINE | ID: mdl-36162805

Subject(s)
Key words

Colorectal surgery (CRS); Machine learning; Post-surgical complication (PSC); Real-time risk prediction

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colorectal Neoplasms / Colorectal Surgery Type of study: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Aged / Humans Country/Region as subject: America do norte Language: En Journal: J Biomed Inform Journal subject: INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

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