Abstract
ObjectiveThe rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity to address the pressing public health problem of adolescent suicidal behavior. We describe the development and evaluation of a machine learning algorithm using natural language processing of electronic health records to identify suicidal behavior among psychiatrically hospitalized adolescents.MethodsAdolescents hospitalized on a psychiatric inpatient unit in a community health system in the northeastern United States were surveyed for history of suicide attempt in the past 12 months. A total of 73 respondents had electronic health records available prior to the index psychiatric admission. Unstructured clinical notes were downloaded from the year preceding the index inpatient admission. Natural language processing identified phrases from the notes associated with the suicide attempt outcome. We enriched this group of phrases with a clinically focused list of terms representing known risk and protective factors for suicide attempt in adolescents. We then applied the random forest machine learning algorithm to develop a classification model. The model performance was evaluated using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.ResultsThe final model had a sensitivity of 0.83, specificity of 0.22, AUC of 0.68, a PPV of 0.42, NPV of 0.67, and an accuracy of 0.47. The terms mostly highly associated with suicide attempt clustered around terms related to suicide, family members, psychiatric disorders, and psychotropic medications.ConclusionThis analysis demonstrates modest success of a natural language processing and machine learning approach to identifying suicide attempt among a small sample of hospitalized adolescents in a psychiatric setting.
Highlights
In 2017, 17.2% of U.S high school students reported having seriously considered attempting suicide and 7.4% reported having attempted suicide in the past year [1]
To address this gap in the research literature, we describe the development of a machine learning algorithm that generates classification models from codes developed by Natural language processing (NLP) analysis of electronic health record (EHR) notes in order to categorize adolescents by history of suicide attempt
To enrich the classification power of the Concept Unique Identifiers (CUIs) identified through NLP, a “curated” list of 34 suicide-related predictive factors and 30 protective factors was developed by behavioral health clinicians on the research team, drawing from the literature on risk factors for adolescent suicide [39,40,41,42] (S1 Table)
Summary
We describe the development and evaluation of a machine learning algorithm using natural language processing of electronic health records to identify suicidal behavior among psychiatrically hospitalized adolescents. Data Availability Statement: The data used for this study contain identifiable private health information from electronic health records of 73 adolescent patients in our hospital system, including clinical notes from outpatient, inpatient, emergency, and primary care encounters that are difficult to deidentify. These patients were recruited from an inpatient psychiatric unit and the data contain sensitive health information regarding the mental health of minors.
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