Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

Autor: Paul Thottakkara, Tezcan Ozrazgat-Baslanti, Bradley B Hupf, Parisa Rashidi, Panos Pardalos, Petar Momcilovic, Azra Bihorac

Publikováno v: PLoS ONE, Vol 11, Iss 5, p e0155705 (2016)

OBJECTIVE:To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. DESIGN:Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. PATIENTS:50,318 adult

Externí odkaz: https://doaj.org/article/477d5ef23b9c49179aff8f870a9f3fd6

Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data.

Autor: Hupf B; Takeda Pharmaceuticals, Cambridge, MA, United States., Yang Y; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States., Gryder R; Takeda Pharmaceuticals, Cambridge, MA, United States., Bunn V; Takeda Pharmaceuticals, Cambridge, MA, United States., Lin J; Takeda Pharmaceuticals, Cambridge, MA, United States.

Publikováno v: Journal of biopharmaceutical statistics [J Biopharm Stat] 2024 Apr 01, pp. 1-9. Date of Electronic Publication: 2024 Apr 01.

Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data.

Autor: Liu M; Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA., Bunn V; Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA., Hupf B; Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA., Lin J; Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA., Lin J; Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA.

Publikováno v: Statistics in medicine [Stat Med] 2021 Sep 30; Vol. 40 (22), pp. 4794-4808. Date of Electronic Publication: 2021 Jun 14.