Table 2 Positive predictive values of the natural language processing algorithms.

	Pre-pandemic positive predictive value (95%CI, No. annotated records)^a	Post-pandemic positive predictive value (95%CI, No. annotated records)^a
Main algorithm used to detect hospitalisations caused by suicide attempts (hybrid machine learning and rule-based approach)	0.85 (0.76–0.91, 85)	0.86 (0.76–0.92, 77)
Alternative algorithm used to detect hospitalisations caused by suicide attempts (rule-based approach)	0.51^b	0.52^b
Algorithm used to detect social isolation risk factor	1.00^c (0.89–1.00, 30)	0.96^c (0.79–0.99, 23)
Algorithm used to detect domestic violence risk factor	0.96^c (0.79–0.99, 23)	0.96^c (0.80–0.99, 24)
Algorithm used to detect sexual violence risk factor	0.84^c (0.70–0.93, 38)	0.96^c (0.82–0.99, 28)
Algorithm used to detect physical violence risk factor	0.86^c (0.69–0.95, 29)	0.96^c (0.82–0.99, 28)
Algorithm used to detect suicide attempt history risk factor	0.93^c (0.78–0.98, 29)	0.83^c (0.66–0.93, 30)

^aWilson score intervals were used to compute the 95%CIs.
^bFor the estimation of the performances of the alternative rule-based SA-classification algorithm, additional stays were drawn randomly among the stays that were classified as SA-caused by the rule-based algorithm but not by the hybrid algorithm—see Supplement. The Wilson score intervals could not be computed using this method.
^cFor the estimation of the performances of the risk factors algorithms, stays were drawn randomly among those that were labelled positive both by the main SA-classification algorithm and by the risk factors-classification algorithms.

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