Table 4.
Number and proportion of papers according to outcome predicted and measure of predictive accuracy reported (for studies that validated predictions)
| Measure of predictive accuracy reporteda | ||||||
|---|---|---|---|---|---|---|
| Outcome predicted | Total papers | AUC and accuracy/sensitivity/specificity | AUC only | Accuracy/sensitivity/specificity only | R 2 | Otherb |
| Complication | 73 (45.3%) | 24 (32.9%) | 17 (23.3%) | 28 (38.4%) | 4 (5.5%) | |
| Mortality | 68 (42.2%) | 16 (23.5%) | 31 (45.6%) | 18 (26.5%) | 3 (4.4%) | |
| Length of stay | 18 (11.1%) | 2 (11.8%) | 3 (16.7%) | 5 (27.8%) | 8 (44.4%) | 1 (5.6%) |
| Health improvement | 16 (10%) | 1 (6.3%) | 3 (18.8%) | 11 (68.8%) | 1 (6.3%) | |
| Total | 161 | 43 (26.7%) | 54 (33.5%) | 62 (38.5%) | 8 (5.0%) | 9 (5.6%) |
aPapers can have more than one approach, so percentages may total more than 100. The total of these columns does not account for duplicates as papers can fluctuate how they discuss different results
b“Other” measures of predictive accuracy (number): congruence of ML and clinician’s decisions (1), Matthews correlation coefficient (1), mean absolute differences between observed and predicted (1), mean error rate (1), MSE as loss function (1), Pearson correlation between estimate and actual (1), ratio of wins vs loses against logistic regression (1), rules developed by ML (1)