Reviewing a decade of research into suicide and related behaviour using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) system

TY - JOUR

T1 - Reviewing a decade of research into suicide and related behaviour using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) system

AU - Bittar, Andre

AU - Velupillai, Sumithra

AU - Downs, Johnny

AU - Sedgwick, Rosemary

AU - Dutta, Rina

PY - 2020/11/27

Y1 - 2020/11/27

N2 - Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person’s risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient’s care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. Further studies in generalisability and external validation of these NLP and ML approaches are needed.

AB - Suicide is a serious public health issue worldwide, yet current clinical methods for assessing a person’s risk of taking their own life remain unreliable and new methods for assessing suicide risk are being explored. The widespread adoption of electronic health records (EHRs) has opened up new possibilities for epidemiological studies of suicide and related behaviour amongst those receiving healthcare. These types of records capture valuable information entered by healthcare practitioners at the point of care. However, much recent work has relied heavily on the structured data of EHRs, whilst much of the important information about a patient’s care pathway is recorded in the unstructured text of clinical notes. Accessing and structuring text data for use in clinical research, and particularly for suicide and self-harm research, is a significant challenge that is increasingly being addressed using methods from the fields of natural language processing (NLP) and machine learning (ML). In this review, we provide an overview of the range of suicide-related studies that have been carried out using the Clinical Records Interactive Search (CRIS): a database for epidemiological and clinical research that contains de-identified EHRs from the South London and Maudsley NHS Foundation Trust. We highlight the variety of clinical research questions, cohorts and techniques that have been explored for suicide and related behaviour research using CRIS, including the development of NLP and ML approaches. We demonstrate how EHR data provides comprehensive material to study prevalence of suicide and self-harm in clinical populations. Structured data alone is insufficient and NLP methods are needed to more accurately identify relevant information from EHR data. We also show how the text in clinical notes provide signals for ML approaches to suicide risk assessment. Further studies in generalisability and external validation of these NLP and ML approaches are needed.

KW - Electronic Health Records; Natural Language Processing; Machine Learning; Suicide, attempted; Suicide, completed; Self-Injurious Behavior

U2 - 10.3389/fpsyt.2020.553463

DO - 10.3389/fpsyt.2020.553463

M3 - Article

SN - 1664-0640

JO - Frontiers in psychiatry / Frontiers Research Foundation

JF - Frontiers in psychiatry / Frontiers Research Foundation

ER -