  Objectives To develop and evaluate machine learning models to detect
suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for
diagnostic screening and clinical management.
  Methods In this retrospective observational noninterventional study using
administrative medical claims data from 1,463,089 patients, gradient-boosted
decision trees were trained to detect likely NASH patients from an at-risk
patient population with a history of obesity, type 2 diabetes mellitus (T2DM),
metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to
detect likely NASH in all at-risk patients or in the subset without a prior
NAFL diagnosis (non-NAFL at-risk patients). Models were trained and validated
using retrospective medical claims data and assessed using area under precision
recall and receiver operating characteristic curves (AUPRCs, AUROCs).
  Results The 6-month incidence of NASH in claims data was 1 per 1,437 at-risk
patients and 1 per 2,127 non-NAFL at-risk patients. The model trained to detect
NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 - 0.011) and
an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60x above
NASH incidence. The model trained to detect NASH in non-NAFL patients had an
AUPRC of 0.003 (95% CI 0.0029 - 0.0031) and an AUROC of 0.78. At 10% recall,
model precision was 1%, which is 20x above NASH incidence.
  Conclusion The low incidence of NASH in medical claims data corroborates the
pattern of NASH underdiagnosis in clinical practice. Claims-based machine
learning could facilitate the detection of probable NASH patients for
diagnostic testing and disease management.
