TY  - JOUR
PY  - 2022//
TI  - Improving ascertainment of suicidal ideation and suicide attempt with natural language processing
JO  - Scientific reports
A1  - Bejan, Cosmin A.
A1  - Ripperger, Michael
A1  - Wilimitis, Drew
A1  - Ahmed, Ryan
A1  - Kang, JooEun
A1  - Robinson, Katelyn
A1  - Morley, Theodore J.
A1  - Ruderfer, Douglas M.
A1  - Walsh, Colin G.
SP  - e15146
EP  - e15146
VL  - 12
IS  - 1
N2  - METHODS relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC]: 98.6, 95% confidence interval [CI] 97.1-99.5) and suicide attempt (AUROC: 97.3, 95% CI 95.2-98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.<p />  <p>Language: en</p>
LA  - en
SN  - 2045-2322
UR  - http://dx.doi.org/10.1038/s41598-022-19358-3
ID  - ref1
ER  -