Investigating online activity in UK adolescent mental health patients: a feasibility study using a natural language processing approach for electronic health records

TY - JOUR

T1 - Investigating online activity in UK adolescent mental health patients

T2 - a feasibility study using a natural language processing approach for electronic health records

AU - Sedgwick, Rosemary

AU - Bittar, André

AU - Kalsi, Herkiran

AU - Barack, Tamara

AU - Downs, Johnny

AU - Dutta, Rina

N1 - © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. Funding Information: The authors acknowledge infrastructure support from the National Institute for Health Research (NIHR). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Funding Information: RS was funded by the National Institute for Health and Social Care Research (NIHR) Academic Clinical Fellowship and a NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust Preparatory Fellowship. JD is supported by NIHR Clinician Science Fellowship award (CS-2018-18-ST2-014) and has received support from a Medical Research Council (MRC) Clinical Research Training Fellowship (MR/L017105/1) and Psychiatry Research Trust Peggy Pollak Research Fellowship in Developmental Psychiatry. RD is funded by an MRC award (grant code: MR/S020365/1); a Clinician Scientist Fellowship from the Health Foundation in partnership with the Academy of Medical Sciences and her work is supported by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. Publisher Copyright: © 2023 BMJ Publishing Group. All rights reserved.

PY - 2023/5/25

Y1 - 2023/5/25

N2 - OBJECTIVES: To assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SETTING: The Clinical Records Interactive Search system allows detailed research based on deidentified EHRs from the South London and Maudsley NHS Foundation Trust, a large south London Mental Health Trust providing secondary and tertiary mental healthcare.PARTICIPANTS AND METHODS: We developed a gazetteer of online activity terms and annotation guidelines, from 5480 clinical notes (200 adolescents, aged 11-17 years) receiving specialist mental healthcare. The preprocessing and manual curation steps of this real-world data set allowed development of a rule-based NLP application to automate identification of online activity (internet, social media, online gaming) mentions in EHRs. The context of each mention was also recorded manually as: supportive, detrimental or neutral in a subset of data for additional analysis.RESULTS: The NLP application performed with good precision (0.97) and recall (0.94) for identification of online activity mentions. Preliminary analyses found 34% of online activity mentions were considered to have been documented within a supportive context for the young person, 38% detrimental and 28% neutral.CONCLUSION: Our results provide an important example of a rule-based NLP methodology to accurately identify online activity recording in EHRs, enabling researchers to now investigate associations with a range of adolescent mental health outcomes.

AB - OBJECTIVES: To assess the feasibility of using a natural language processing (NLP) application for extraction of free-text online activity mentions in adolescent mental health patient electronic health records (EHRs).SETTING: The Clinical Records Interactive Search system allows detailed research based on deidentified EHRs from the South London and Maudsley NHS Foundation Trust, a large south London Mental Health Trust providing secondary and tertiary mental healthcare.PARTICIPANTS AND METHODS: We developed a gazetteer of online activity terms and annotation guidelines, from 5480 clinical notes (200 adolescents, aged 11-17 years) receiving specialist mental healthcare. The preprocessing and manual curation steps of this real-world data set allowed development of a rule-based NLP application to automate identification of online activity (internet, social media, online gaming) mentions in EHRs. The context of each mention was also recorded manually as: supportive, detrimental or neutral in a subset of data for additional analysis.RESULTS: The NLP application performed with good precision (0.97) and recall (0.94) for identification of online activity mentions. Preliminary analyses found 34% of online activity mentions were considered to have been documented within a supportive context for the young person, 38% detrimental and 28% neutral.CONCLUSION: Our results provide an important example of a rule-based NLP methodology to accurately identify online activity recording in EHRs, enabling researchers to now investigate associations with a range of adolescent mental health outcomes.

KW - Humans

KW - Adolescent

KW - Electronic Health Records

KW - Mental Health

KW - Feasibility Studies

KW - Natural Language Processing

KW - London

UR - http://www.scopus.com/inward/record.url?scp=85160455874&partnerID=8YFLogxK

U2 - 10.1136/bmjopen-2022-061640

DO - 10.1136/bmjopen-2022-061640

M3 - Article

C2 - 37230520

SN - 2044-6055

VL - 13

SP - e061640

JO - BMJ Open

JF - BMJ Open

IS - 5

M1 - e061640

ER -