Using multiclass classification to automate the identification of patient safety incident reports by type and severity

Table 1 Text classifiers were trained to identify reports about 10 safety problems in hospitals by type and severity level. This table shows the composition of balanced and stratified datasets used for classifier training and testing

	balanced AIMS benchmark	stratified AIMS original		stratified Riskman independent
	n	n	%	n	%
Incident type
Falls	260	90	20	872	15
Medications	260	68	15	1053	18
Pressure injury	260	37	8	190	3
Aggression	260	49	11	487	8
Documentation	260	26	6	252	4
Blood product	260	5	1	59	1
Patient identification	260	7	2	86	1
Infection	260	6	1	22	<1
Clinical handover	260	7	2	87	1
Deteriorating patient	260	1	<1	14	<1
Others	260	148	33	2878	48
Total	2860	444		6000
Severity level
SAC1	290	25	<1	23	<1
SAC2	290	95	2	105	2
SAC3	290	2198	45	2609	44
SAC4	290	2519	52	3213	54
Total	1160	4837		5950

ISSN: 1472-6947