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Development of a 3-Step theory of suicide ontology to facilitate 3ST factor extraction from clinical progress notes.
Meerwijk, Esther L; Jones, Gabrielle A; Shotqara, Asqar S; Reyes, Sofia; Tamang, Suzanne R; Eddington, Hyrum S; Reeves, Ruth M; Finlay, Andrea K; Harris, Alex H S.
Afiliación
  • Meerwijk EL; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA. Electronic address: esther.meerwijk@va.gov.
  • Jones GA; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
  • Shotqara AS; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
  • Reyes S; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA.
  • Tamang SR; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA; Department of Medicine, Stanford University, Stanford, CA, USA.
  • Eddington HS; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA; Department of Surgery, Stanford University, Stanford, CA, USA.
  • Reeves RM; VA Tennessee Valley Healthcare System, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Finlay AK; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA; VA National Center on Homelessness Among Veterans, USA; Schar School of Policy and Government, George Mason University, Arlington, VA, USA.
  • Harris AHS; VA Health Services Research & Development, Center for Innovation to Implementation (Ci2i), VA Palo Alto Health Care System, Menlo Park, CA, USA; Department of Surgery, Stanford University, Stanford, CA, USA.
J Biomed Inform ; 150: 104582, 2024 02.
Article en En | MEDLINE | ID: mdl-38160758
ABSTRACT

OBJECTIVE:

Suicide risk prediction algorithms at the Veterans Health Administration (VHA) do not include predictors based on the 3-Step Theory of suicide (3ST), which builds on hopelessness, psychological pain, connectedness, and capacity for suicide. These four factors are not available from structured fields in VHA electronic health records, but they are found in unstructured clinical text. An ontology and controlled vocabulary that maps psychosocial and behavioral terms to these factors does not exist. The objectives of this study were 1) to develop an ontology with a controlled vocabulary of terms that map onto classes that represent the 3ST factors as identified within electronic clinical progress notes, and 2) to determine the accuracy of automated extractions based on terms in the controlled vocabulary.

METHODS:

A team of four annotators did linguistic annotation of 30,000 clinical progress notes from 231 Veterans in VHA electronic health records who attempted suicide or who died by suicide for terms relating to the 3ST factors. Annotation involved manually assigning a label to words or phrases that indicated presence or absence of the factor (polarity). These words and phrases were entered into a controlled vocabulary that was then used by our computational system to tag 14 million clinical progress notes from Veterans who attempted or died by suicide after 2013. Tagged text was extracted and machine-labelled for presence or absence of the 3ST factors. Accuracy of these machine-labels was determined for 1000 randomly selected extractions for each factor against a ground truth created by our annotators.

RESULTS:

Linguistic annotation identified 8486 terms that related to 33 subclasses across the four factors and polarities. Precision of machine-labeled extractions ranged from 0.73 to 1.00 for most factor-polarity combinations, whereas recall was somewhat lower 0.65-0.91.

CONCLUSION:

The ontology that was developed consists of classes that represent each of the four 3ST factors, subclasses, relationships, and terms that map onto those classes which are stored in a controlled vocabulary (https//bioportal.bioontology.org/ontologies/THREE-ST). The use case that we present shows how scores based on clinical notes tagged for terms in the controlled vocabulary capture meaningful change in the 3ST factors during weeks preceding a suicidal event.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Veteranos / Ideación Suicida Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Veteranos / Ideación Suicida Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos