Table 2.

Characteristics of supervised learning models compared to dichotomized risk categories, by data source

Supervised Learning Models	Dichotomized Risk Categorya						Model Characteristicsb
	High	Not High	Subtotal	Undeterminedc	Totalc
Briggs & Freeman (N = 1,141 medications)
SVMd
High	155	30	185	15	200	SEN	51%	NPV	84%
Not high	147	746	893	48	941	SPEC	96%	Accur	84%
Total	302	776	1078	63	1141	PPV	84%
Sentiment
High	173	24	197	1	198	SEN	57%	NPV	85%
Not high	129	752	881	62	943	SPEC	97%	Accur	86%
Total	302	776	1078	63	1141	PPV	88%
TERIS (N = 1,703 medications)
SVMd
High	47	2	49	29	78	SEN	57%	NPV	90%
Not high	35	308	343	1282	1625	SPEC	99%	Accur	91%
Total	82	310	392	1311	1703	PPV	96%
Sentiment
High	51	7	58	6	64	SEN	62%	NPV	91%
Not high	31	303	334	1305	1639	SPEC	98%	Accur	90%
Total	82	310	392	1311	1703	PPV	88%
Drug Labels (N = 2,106 medications)
SVMd
High	310	13	323	327	650	SEN	99%	NPV	99%
Not high	4	367	371	1085	1456	SPEC	97%	Accur	98%
Total	314	380	694	1412	2106	PPV	96%
Sentiment
High	188	8	196	30	226	SEN	60%	NPV	75%
Not high	126	372	498	1382	1880	SPEC	98%	Accur	81%
Total	314	380	694	1412	2106	PPV	96%

Accur. Accuracy, NPV Negative Predictive Value, PPV Positive Predictive Value, SEN Sensitivity, SPEC Specificity, SVM Subject Vector Machine model

^aSee Table 1 for details

^bCalculated using dichotomized risk categories of ‘high’ and ‘not high’ as the gold standard. Medications that the sources categorized as undetermined risk (see Table 1) were not included in models’ characteristic calculations

^cProvided for informational purposes; these data were not used to calculate the model characteristics

^dCategorized as ‘High’ if all 500/500 SVM models categorized a medication as high else a medication was categorized as ‘Not High’. See Methods for more details