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Methods for identifying suicide or suicidal ideation in EHRs.

K Haerian¹, H Salmasian, C Friedman

¹Department of Biomedical Informatics, Columbia University, New York, NY, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|January 11, 2013

Summary

This summary is machine-generated.

Automated detection of suicidality in electronic health records is crucial. Combining ICD-9 codes with Natural Language Processing (NLP) significantly improves accuracy in identifying suicide risk compared to ICD-9 codes alone.

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Area of Science:

Health Informatics
Clinical Informatics
Computational Linguistics

Background:

Electronic health records (EHRs) contain valuable data for identifying adverse drug reactions, genotype/phenotype information, and psychosocial risk factors for suicidality.
Suicide and suicidal ideation are often documented within clinical narratives in EHRs.
Further investigation into these data elements is warranted to understand their role as risk factors.

Purpose of the Study:

To develop and define an algorithm for the automated detection of suicide and suicidal ideation within EHRs.
To evaluate the effectiveness of different methods for automated detection.
To improve the accuracy of identifying serious mental health events.

Main Methods:

Utilized electronic health records (EHRs) data, including clinical narratives.
Developed and tested an algorithm for automated detection of suicidality.
Compared the performance of International Classification of Diseases, Ninth Revision (ICD-9) E-Codes alone versus a combination of ICD-9 codes and Natural Language Processing (NLP).
Conducted a qualitative analysis of errors from automated coding compared to manual review.

Main Results:

ICD-9 E-Codes alone demonstrated a low positive predictive value (PPV) of 0.55.
Combining ICD-9 codes with NLP achieved a significantly higher PPV of 0.97.
The study discusses the types of errors encountered with both ICD-9 and NLP automated coding methods.

Conclusions:

Automated detection of suicidality in EHRs is feasible and can be significantly enhanced.
A combined approach using ICD-9 codes and NLP offers superior accuracy for identifying suicide risk.
Understanding and classifying coding errors is essential for refining automated detection systems.