Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury.
Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different s...
| Publicado en: | Sports Biomechanics Vol. 22; no. 1; pp. 91 - 102 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160565298&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160565298 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14763141 I7F jtl: Sports Biomechanics issn: 14763141 maglogo: N pubinfo: dt: Jan2023 vid: 22 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 160565298 151655578 160565298 160565298 10.1080/14763141.2021.1947358 160565298 ppf: 91 ppct: 11 formats: tig: atl: Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury. aug: au: Richter, Chris Petushek, Erich Grindem, Hege Franklyn-Miller, Andrew Bahr, Roald Krosshaug, Tron affil: Sports Medicine Department, Sports Surgery Clinic, Santry Demesne, Ireland sug: subj: Machine Learning Algorithms Anterior Cruciate Ligament Injuries Rehabilitation Anterior Cruciate Ligament Injuries Diagnosis Predictive Validity Instrument Validation Human Probability Comparative Studies Incidence ab: Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was 'healthy-limb' class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61<AUC<0.62) and FACL group (0.57<AUC<0.59). The ACL injury incidence proportion was highest in athletes with probabilities below 0.20 (9.4%; +2.7% to baseline), while athletes with probabilities above 0.80 had an incidence proportion of 4.1% (−2.6%). While findings that a low probability might represent an increase in injury risk on a group level, it is not sensitive enough for injury screening to predict a future injury on the individual level. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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