Using Information from the Electronic Health Record to Improve Measurement of Unemployment in Service Members and Veterans with mTBI and Post-Deployment Stress

Dillahunt-Aspillaga, Christina; Finch, Dezon; Massengale, Jill; Kretzmer, Tracy; Luther, Stephen L.; McCart, James A.

doi:10.1371/journal.pone.0115873

Cited by 18 publications

(12 citation statements)

References 65 publications

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“…NLP has been used to extract a variety of information from freetext clinical notes, demonstrating a large range of potential uses. These applications include extracting concepts such as drug polypharmacy (17), symptoms of mental illness (37), the presence of suicidal behaviors (20,38), or employment status (21). These approaches share similarities with ours and support the idea that it is feasible to use NLP to extract historical variables from EHRs.…”

Section: Study 2: Application Of Natural Language Processing Tools Tomentioning

confidence: 67%

“…This makes extraction of data difficult, relying either on resource-intensive manual review of clinical records, or, increasingly, automated natural language processing (NLP) algorithms (15,16). NLP processes have been applied to extract a variety of information from free-text clinical records including medication use (17,18), self-harm (19,20), and socio-demographic history (21,22); such approaches have addressed model development using limited numbers of annotated text examples (23). One approach to NLP for EHRs is to build information retrieval systems based on named entity recognition (NER), where a model is trained to recognize concepts which are related to variables of interest.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Senior

Burghart

et al. 2020

Front. Psychiatry

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Background: Oxford Mental Illness and Suicide tool (OxMIS) is a brief, scalable, freely available, structured risk assessment tool to assess suicide risk in patients with severe mental illness (schizophrenia-spectrum disorders or bipolar disorder). OxMIS requires further external validation, but a lack of large-scale cohorts with relevant variables makes this challenging. Electronic health records provide possible data sources for external validation of risk prediction tools. However, they contain large amounts of information within free-text that is not readily extractable. In this study, we examined the feasibility of identifying suicide predictors needed to validate OxMIS in routinely collected electronic health records. Methods: In study 1, we manually reviewed electronic health records of 57 patients with severe mental illness to calculate OxMIS risk scores. In study 2, we examined the feasibility of using natural language processing to scale up this process. We used anonymized freetext documents from the Clinical Record Interactive Search database to train a named entity recognition model, a machine learning technique which recognizes concepts in freetext. The model identified eight concepts relevant for suicide risk assessment: medication (antidepressant/antipsychotic treatment), violence, education, self-harm, benefits receipt, drug/alcohol use disorder, suicide, and psychiatric admission. We assessed model performance in terms of precision (similar to positive predictive value), recall (similar to sensitivity) and F1 statistic (an overall performance measure). Results: In study 1, we estimated suicide risk for all patients using the OxMIS calculator, giving a range of 12 month risk estimates from 0.1-3.4%. For 13 out of 17 predictors, there was no missing information in electronic health records. For the remaining 4 predictors missingness ranged from 7-26%; to account for these missing variables, it was possible for OxMIS to estimate suicide risk using a range of scores. In study 2, the named entity recognition model had an overall precision of 0.77, recall of 0.90 and F1 score of 0.83. The concept with the best precision and recall was medication (precision

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Section: Study 2: Application Of Natural Language Processing Tools Tomentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Senior

Burghart

et al. 2020

Front. Psychiatry

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“… 64–66 NLP has been useful for extracting medical information such as principal diagnosis, information related to employment and medication use from clinical narratives. 64 67 68 This has led to a better understanding of the conditions patients face and potential interventions. 69 Manual chart review for annotation has been used extensively and when appropriate rigour is applied, the information extracted is very reliable and is often used as the reference standard to evaluate IE systems.…”

Section: Discussionmentioning

confidence: 99%

Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource

et al. 2016

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Purpose The South London and Maudsley National Health Service (NHS) Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register and its Clinical Record Interactive Search (CRIS) application were developed in 2008, generating a research repository of real-time, anonymised, structured and open-text data derived from the electronic health record system used by SLaM, a large mental healthcare provider in southeast London. In this paper, we update this register's descriptive data, and describe the substantial expansion and extension of the data resource since its original development. Participants Descriptive data were generated from the SLaM BRC Case Register on 31 December 2014. Currently, there are over 250 000 patient records accessed through CRIS. Findings to date Since 2008, the most significant developments in the SLaM BRC Case Register have been the introduction of natural language processing to extract structured data from open-text fields, linkages to external sources of data, and the addition of a parallel relational database (Structured Query Language) output. Natural language processing applications to date have brought in new and hitherto inaccessible data on cognitive function, education, social care receipt, smoking, diagnostic statements and pharmacotherapy. In addition, through external data linkages, large volumes of supplementary information have been accessed on mortality, hospital attendances and cancer registrations. Future plans Coupled with robust data security and governance structures, electronic health records provide potentially transformative information on mental disorders and outcomes in routine clinical care. The SLaM BRC Case Register continues to grow as a database, with approximately 20 000 new cases added each year, in addition to extension of follow-up for existing cases. Data linkages and natural language processing present important opportunities to enhance this type of research resource further, achieving both volume and depth of data. However, research projects still need to be carefully tailored, so that they take into account the nature and quality of the source information.

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“…When working with free text from EHRs, a variety of entities have been the focus. For example, NLP for safety surveillance by extracting information on postoperative complications [ 21 ] and adverse drug effects from psychiatric records [ 22 ], clinical event detection (eg, fever, change in output) for transcriptions of the handoff communication between nurses during shift changes [ 23 , 24 ], and even the creation of new data such as veterans’ employment information [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

Leroy

Pettygrove

et al. 2018

J Med Internet Res

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BackgroundElectronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive.ObjectiveOur objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data.MethodsWe developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms.ResultsWe evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs.ConclusionsOur results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

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Using Information from the Electronic Health Record to Improve Measurement of Unemployment in Service Members and Veterans with mTBI and Post-Deployment Stress

Cited by 18 publications

References 65 publications

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Identifying Predictors of Suicide in Severe Mental Illness: A Feasibility Study of a Clinical Prediction Rule (Oxford Mental Illness and Suicide Tool or OxMIS)

Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application

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