Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media

Table 1 Performance metrics

Name	Value
True positive (tp)	No. of true positives^a
True negative (tn)	No. of true negatives^a
False positive (fp)	No. of false positives^a
False negative (fn)	No. of false negatives^a
Accuracy (Acc)	\(\frac{{{\text{tp}} + {\text{tn}}}}{{{\text{tp}} + {\text{fp}} + {\text{tn}} + {\text{fn}}}}\)
Gwet AC1	\(\frac{{{\text{Acc}} - e(\pi )}}{1 - e(\pi )}\)
e(π)	\(\left\{ {\frac{{(2{\text{tp}} + {\text{fp}} + {\text{fn}})/2}}{{{\text{tp}} + {\text{fp}} + {\text{tn}} + {\text{fn}}}}} \right\}^{2} + \left\{ {\frac{{(2{\text{tn}} + {\text{fp}} + {\text{fn}})/2}}{{{\text{tp}} + {\text{fp}} + {\text{tn}} + {\text{fn}}}}} \right\}^{2}\)
Area under the curve	Trapezoidal method [30]