start-ver=1.4
cd-journal=joma
no-vol=10
cd-vols=
no-issue=5
article-no=
start-page=892
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20220512
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=No prediction models using use conventional logistic models and machine learning exist for medical litigation outcomes involving medical doctors. Using a logistic model and three machine learning models, such as decision tree, random forest, and light-gradient boosting machine (LightGBM), we evaluated the prediction ability for litigation outcomes among medical litigation in Japan. The prediction model with LightGBM had a good predictive ability, with an area under the curve of 0.894 (95% CI; 0.893-0.895) in all patients' data. When evaluating the feature importance using the SHApley Additive exPlanation (SHAP) value, the system error was the most significant predictive factor in all clinical settings for medical doctors' loss in lawsuits. The other predictive factors were diagnostic error in outpatient settings, facility size in inpatients, and procedures or surgery settings. Our prediction model is useful for estimating medical litigation outcomes.
en-copyright=
kn-copyright=

en-aut-name=YamamotoNorio
en-aut-sei=Yamamoto
en-aut-mei=Norio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=SukegawaShintaro
en-aut-sei=Sukegawa
en-aut-mei=Shintaro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=WatariTakashi
en-aut-sei=Watari
en-aut-mei=Takashi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=2
en-affil=Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
kn-affil=

affil-num=3
en-affil=General Medicine Center, Shimane University Hospital
kn-affil=
en-keyword=medical malpractice claims
kn-keyword=medical malpractice claims
en-keyword=litigation
kn-keyword=litigation
en-keyword=diagnostic error
kn-keyword=diagnostic error
en-keyword=medical error
kn-keyword=medical error
en-keyword=system error
kn-keyword=system error
en-keyword=machine learning
kn-keyword=machine learning
en-keyword=prediction model
kn-keyword=prediction model
END