Machine learning algorithms to classify self-harm behaviours in New South Wales Ambulance electronic medical records: A retrospective study.
Int J Med Inform
; 161: 104734, 2022 05.
Article
in En
| MEDLINE
| ID: mdl-35287099
ABSTRACT
BACKGROUND:
There is increasing interest in suicide surveillance solutions to identify non-fatal suicidal and self-harming behaviours in the Australian community not currently captured through national administrative datasets.OBJECTIVE:
The aim of the present study was to develop machine learning models to classify self-harm related behaviours using unstructured clinical note text from New South Wales (NSW) Ambulance data and compare their performance via traditional methods.METHODS:
Primary data were derived from NSW Ambulance electronic medical records (eMRs) for potential self-harm related NSW Ambulance attendances for the period 2013-2019. Data included paramedic clinical notes detailing the nature of the attendance, clinical outcome, and narrative information. We assessed sensitivity, specificity, positive predictive value, negative predictive value, F-score, and the Matthews correlation coefficient (MCC) for four algorithms (Support Vector Machine, random forest, decision tree, and logistic regression).RESULTS:
The performance of these algorithms was compared using the MCC measure. In a test sample of 3157 ambulance attendances (1349 self-harm related behaviours and 1808 unrelated), the MCC for classification of self-harm related behaviour ranged from +0.681 to +0.730. The Support Vector Machine (sensitivity = 82.7%, specificity = 89.6%, MCC = 0.730) and the logistic regression (sensitivity = 83.1%, specificity = 89.3%, MCC = 0.727) models performed best.CONCLUSIONS:
This study demonstrates that machine learning models can be applied to paramedic notes within unstructured medical records to classify self-harm related behaviours. The resulting model could be used to compliment current manual abstraction of self-harm behaviours and provide more timely approximations to be used for self-harm surveillance.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Self-Injurious Behavior
/
Electronic Health Records
Type of study:
Diagnostic_studies
/
Guideline
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Oceania
Language:
En
Year:
2022
Type:
Article