Additional file 1: of Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor

Supplementary methods: Physician's predictions. Figure S1. Performance of AKIpredictor for prediction of AKI-23 by serum creatinine. Figure S2. Performance of binary predictions by physicians. Figure S3. Performance of clinicians split by seniority level. Figure S4. Performance of clinicians split by confidence level. Figure S5. Comparison of performance of AKIpredictor, physicians and their combination. Figure S6. Comparison of performance of junior physicians and the combination of junior physicians with AKIpredictor. Figure S7. Comparison of performance of physicians with low-medium confidence in their predictions and the combination of their predictions with AKIpredictor. Table S1. Patient characteristics and clinical outcomes for patients with predictions by physicians and AKIpredictor. Table S2. Physicians’ generalities. Table S3. Description of physicians’ predictions. Table S4. Description of physicians’ predictions per seniority and confidence levels. Appendix A. prediction questionnaire. Appendix B. Physician questionnaire Tables S1, S2, S3, and S4 and Figures S1, S2, S3, S4, S5, S6, and S7. (DOCX 2070 kb)