Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017.

Autor:	Karnuta JM; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Luu BC; Department of Orthopedic Surgery, Baylor College of Medicine, Houston, Texas, USA., Haeberle HS; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA.; Department of Orthopedic Surgery, Baylor College of Medicine, Houston, Texas, USA., Saluan PM; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Frangiamore SJ; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Stearns KL; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Farrow LD; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Nwachukwu BU; Hospital for Special Surgery, New York, New York, USA., Verma NN; Rush University Medical Center, Chicago, Illinois, USA., Makhni EC; Department of Orthopedics, Henry Ford Health System, West Bloomfield, Michigan, USA., Schickendantz MS; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA., Ramkumar PN; Orthopaedic Machine Learning Laboratory, Cleveland Clinic, Cleveland, Ohio, USA.
Jazyk:	angličtina
Zdroj:	Orthopaedic journal of sports medicine [Orthop J Sports Med] 2020 Nov 11; Vol. 8 (11), pp. 2325967120963046. Date of Electronic Publication: 2020 Nov 11 (Print Publication: 2020).
DOI:	10.1177/2325967120963046
Abstrakt:	Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player's future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury's anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers. Competing Interests: One or more of the authors has declared the following potential conflicts of interest or source of funding: P.M.S. has received educational support from Arthrex, consulting fees from DJO and DePuy, nonconsulting fees from Arthrex, and hospitality payments from the Musculoskeletal Transplant Foundation. S.J.F. has received grant payments from Arthrex and DJO and educational support from Arthrex and Rock Medical. K.L.S. has received educational support from Arthrex; consulting fees from Molnlycke Health Care; nonconsulting fees from Horizon Pharma; honoraria from Fidia Pharma; and hospitality payments from Biomet Orthopedics, the Musculoskeletal Transplant Foundation, Ramsay Medical, and Stryker. L.D.F. has received consulting fees from Zimmer Biomet and hospitality payments from the Musculoskeletal Transplant Foundation. B.U.N. has received educational support from Smith & Nephew and hospitality payments from Stryker, Wright Medical, and Zimmer Biomet. N.N.V. has received educational support from Medwest; consulting fees from Arthrex, Medacta, and Smith & Nephew; nonconsulting fees from Arthrex and Smith & Nephew; and royalties from Smith & Nephew. E.C.M. has received educational support from Pinnacle (Arthrex), consulting fees from Smith & Nephew, and hospitality payments from Stryker. M.S.S. has received consulting fees and nonconsulting fees from Arthrex. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto. (© The Author(s) 2020.)
Databáze:	MEDLINE
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