Machine Learning Models for Predicting Unplanned…

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Results

From 4105 identified records, 8 studies met the inclusion criteria. Because pooling AUC-ROC across studies confounds architectural differences with differences between cohorts, outcome definitions, and validation designs, this review prioritizes three paired within-study comparisons, each free of that confounding. First, when each study’s own proposed architecture was compared with that same study’s logistic-regression baseline on the identical cohort and outcome, the pooled advantage was +0.027 AUC-ROC (95% CI: +0.010 to +0.044; k=4; I2 = 0.0%)—a small, consistent gain, far more homogeneous than any cross-study pooled estimate. Second, within a single study evaluating eight identical architectures on two databases (MIMIC-III and eICU-CRD), changing the database moved AUC-ROC by 0.068 on average (range 0.041–0.101)—2.5 times the pooled architecture effect—while the ranking of architectures was preserved. Third, in the three studies reporting both an internal and an external validation cohort, external validation reduced AUC-ROC by 0.055–0.073 relative to internal validation. Against this background, cross-study pooled estimates are reported as exploratory context only. AUC-ROC standard errors were reconstructed at the estimate level wherever possible (reported confidence interval, or the Hanley–McNeil sampling-variance formula, corrected for each study’s actual evaluation-set size where a train/test split was used) rather than assumed to be uniform; one study (Sun M. et al., which does not report event counts and shows independent evidence of an atypically high evaluation-cohort prevalence) was excluded from pooling. Pooled on the logit scale to keep confidence intervals within the mathematically admissible [0,1] range, the primary analysis (one prespecified representative estimate per study, k=7) yielded AUC-ROC = 0.814 (95% CI: 0.746–0.868; I2 = 98.5%), and an exploratory analysis of the remaining 68 model/outcome estimates yielded a similar point estimate (0.807, 95% CI: 0.786–0.826; I2 = 98.8%). This heterogeneity is not an artifact of understated standard errors: inflating every Hanley–McNeil standard error by a factor of five reduced I2 only to 71–75%. Three studies were judged to be at low risk of bias, five raised some concerns, and none was at high risk.

Machine Learning Models for Predicting Unplanned Readmission and Mortality in the ICU: A Systematic Review and Meta-Analysis

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