Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem

Zhong, Qiu‐Yue; Mittal, Leena; Nathan, Margo; Brown, Kara M.; González, Deborah Knudson; Cai, Tianrun; Finan, Sean; Gelaye, Bizu; Avillach, Paul; Smoller, Jordan W.; Karlson, Elizabeth W.; Cai, Tianxi; Williams, Michelle A.

doi:10.1007/s10654-018-0470-0

Cited by 38 publications

(41 citation statements)

References 47 publications

(60 reference statements)

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“…[21][22][23] For example, we recently showed that natural language processing substantially improved the detection of suicidal behavior among pregnant women. 24,25 We are currently exploring the addition of natural language processing to our suicide risk prediction models. Fourth, because patients may receive care outside the participating health care system, relevant predictors or outcomes may have been missed, resulting in weaker than optimal performance of our models.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Barak–Corren

Castro²,

Nock

et al. 2020

JAMA Netw Open

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IMPORTANCE Suicide is a leading cause of mortality, with suicide-related deaths increasing in recent years. Automated methods for individualized risk prediction have great potential to address this growing public health threat. To facilitate their adoption, they must first be validated across diverse health care settings.OBJECTIVE To evaluate the generalizability and cross-site performance of a risk prediction method using readily available structured data from electronic health records in predicting incident suicide attempts across multiple, independent, US health care systems. DESIGN, SETTING, AND PARTICIPANTSFor this prognostic study, data were extracted from longitudinal electronic health record data comprising International Classification of Diseases, Ninth Revision diagnoses, laboratory test results, procedures codes, and medications for more than 3.7 million patients from 5 independent health care systems participating in the Accessible Research Commons for Health network. Across sites, 6 to 17 years' worth of data were available, up to 2018. Outcomes were defined by International Classification of Diseases, Ninth Revision codes reflecting incident suicide attempts (with positive predictive value >0.70 according to expert clinician medical record review). Models were trained using naive Bayes classifiers in each of the 5 systems. Models were cross-validated in independent data sets at each site, and performance metrics were calculated. Data analysis was performed from November 2017 to August 2019. MAIN OUTCOMES AND MEASURES The primary outcome was suicide attempt as defined by a previously validated case definition using International Classification of Diseases, Ninth Revision codes. The accuracy and timeliness of the prediction were measured at each site. RESULTS Across the 5 health care systems, of the 3 714 105 patients (2 130 454 female [57.2%])included in the analysis, 39 162 cases (1.1%) were identified. Predictive features varied by site but, as expected, the most common predictors reflected mental health conditions (eg, borderline personality disorder, with odds ratios of 8.1-12.9, and bipolar disorder, with odds ratios of 0.9-9.1) and substance use disorders (eg, drug withdrawal syndrome, with odds ratios of 7.0-12.9). Despite variation in geographical location, demographic characteristics, and population health characteristics, model performance was similar across sites, with areas under the curve ranging from 0.71 (95% CI, 0.70-0.72) to 0.76 (95% CI, 0.75-0.77). Across sites, at a specificity of 90%, the models detected a mean of 38% of cases a mean of 2.1 years in advance. CONCLUSIONS AND RELEVANCEAcross 5 diverse health care systems, a computationally efficient approach leveraging the full spectrum of structured electronic health record data was able to detect (continued) Key Points Question Can a process for training machine-learning algorithms based on electronic health records identify individuals at increased risk of suicide attempts across independent health care systems? Abstract (contin...

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Section: Strengths and Limitationsmentioning

confidence: 99%

Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Barak–Corren

Castro²,

Nock

et al. 2020

JAMA Netw Open

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Section: Methodsmentioning

confidence: 99%

“…This algorithm combined information from codified billing data (ICD codes related to suicidal behaviour), clinical notes processed by natural language processing (NLP), and medications in electronic prescriptions. The process of NLP has been described in detail elsewhere . In brief, we first screened for suicidal behaviour using diagnostic codes.…”

Section: Methodsmentioning

confidence: 99%

“…Since suicidal behaviour is not well coded in the EMRs, we previously trained a machine learning algorithm (adaptive elastic net) to classify the presence of suicidal behaviour during pregnancy leveraging additional information embedded in the clinical notes. 21,22 Psychiatrists manually reviewed clinical charts to identify relevant features for suicidal behaviour and to obtain gold-standard labels. This algorithm combined information from codified billing data (ICD codes related to suicidal behaviour), clinical notes processed by natural language processing (NLP), and medications in electronic prescriptions.…”

Section: Suicidal Behaviour During Pregnancymentioning

confidence: 99%

“…The process of NLP has been described in detail elsewhere. 21,22 In brief, we first screened for suicidal behaviour using diagnostic codes. Among the women without any diagnostic codes related to suicidal behaviour, we searched clinical notes and identified women with at least one mention of the terms related to suicidal behaviour (suicide, suicidal, suicidality, self harm, self-harm, harm to self, intentional self-harm, deliberate self-harm, self poison, self-poison, passive SI, kill herself, kill myself, killing herself, killing myself, self-injurious, self injurious, self injury, self-inflicted, self inflicted, self mutilation self cutting, deliberate self-cutting).…”

Section: Suicidal Behaviour During Pregnancymentioning

confidence: 99%

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Associations of antepartum suicidal behaviour with adverse infant and obstetric outcomes

Zhong

Gelaye

Karlson

et al. 2019

Paediatric Perinatal Epid

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Background Relatively little is known about antepartum suicidal behaviour and pregnancy outcomes. We examined associations of antepartum suicidal behaviour, alone and in combination with psychiatric disorders, with adverse infant and obstetric outcomes. Methods We included 188 925 singleton livebirths from a retrospective cohort (1996‐2016). Suicidal behaviour, psychiatric disorders, and outcomes were derived from electronic medical records. We performed multivariable logistic regressions with generalised estimating equations to estimate adjusted odds ratios (aOR) with 95% confidence intervals (95%CI). Results The prevalence of antepartum suicidal behaviour was 152.44 per 100 000 singleton livebirths. Nearly two‐thirds (64.24%) of women with suicidal behaviour also had psychiatric disorders. Compared to women without psychiatric disorders and suicidal behaviour, women with psychiatric disorders alone had 1.3‐fold to 1.4‐fold increased odds of delivering low birthweight or preterm infants and 1.2‐fold increased odds of experiencing obstetric complications. Women with suicidal behaviour alone had increased odds of preterm labour (aOR 2.05, 95% CI 1.16, 3.62). Women with both suicidal behaviour and psychiatric disorders had > twofold increased odds of delivering low birthweight (aOR 2.52, 95% CI 1.40, 4.54), preterm birth (aOR 2.44, 95% CI 1.63, 3.66), and low birthweight/preterm birth (aOR 2.30, 95% CI 1.54, 3.44) infants; the odds of preterm labour (aOR 1.62, 95% CI 1.06, 2.47), placental abruption (aOR 2.33, 95% CI 1.20, 4.51), preterm rupture of membranes (aOR 1.63, 95% CI 1.08, 2.46), and postpartum haemorrhage (aOR 1.93, 95%CI 1.09, 3.40) were elevated. Conclusions Antepartum suicidal behaviour, when co‐occurring with psychiatric disorders, is associated with increased odds of adverse infant and obstetric outcomes. Future studies are warranted to understand the causal roles of suicidal behaviour and psychiatric disorders in pregnancy.

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How Will Artificial Intelligence Affect Patient-Clinician Relationships?

Nagy

Sisk

2020

AMA Journal of Ethics

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Artificial intelligence (AI) could improve the efficiency and accuracy of health care delivery, but how will AI influence the patient-clinician relationship? While many suggest that AI might improve the patientclinician relationship, various underlying assumptions will need to be addressed to bring these potential benefits to fruition. Will off-loading tedious work result in less time spent on administrative burden during patient visits? If so, will clinicians use this extra time to engage relationally with their patients? Moreover, given the desire and opportunity, will clinicians have the ability to engage in effective relationship building with their patients? In order for the best-case scenario to become a reality, clinicians and technology developers must recognize and address these assumptions during the development of AI and its implementation in health care.

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Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem

Cited by 38 publications

References 47 publications

Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems

Associations of antepartum suicidal behaviour with adverse infant and obstetric outcomes

How Will Artificial Intelligence Affect Patient-Clinician Relationships?

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