Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing

McCoy, Thomas H.; Castro, Víctor M.; Roberson, Ashlee M.; Snapper, Leslie A.; Perlis, Roy H.

doi:10.1001/jamapsychiatry.2016.2172

Cited by 109 publications

(88 citation statements)

References 28 publications

(18 reference statements)

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“…Thus, the ability to incorporate the full phenotypic breadth of the EHR provides information beyond what would be feasible for individual clinicians to evaluate. In another recent analysis, McCoy and colleagues [McCoy and others 2016] focused on predicting risk of suicide or accidental death among patients discharged from inpatient hospitalization at Partners Healthcare. Models using coded variables with or without the addition of NLP, achieved an AUC of approximately 0.74.…”

Section: Application: Prediction Algorithmsmentioning

confidence: 99%

The use of electronic health records for psychiatric phenotyping and genomics

Smoller

2017

American J of Med Genetics Pt B

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The widespread adoption of electronic health record (EHRs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.

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Section: Application: Prediction Algorithmsmentioning

confidence: 99%

The use of electronic health records for psychiatric phenotyping and genomics

Smoller

2017

American J of Med Genetics Pt B

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“…Parallel efforts have been utilizing large-scale clinical data (diagnoses, medications, procedures, utilization, demographics, etc.) from electronic health records (EHR) to identify features associated with suicide attempt and to apply predictive analytics to assess risk of future suicidal behaviors [19][20][21] . The most recent efforts in this domain have applied machine learning with high accuracy (c-statistics above 0.8-0.9) and precision (above 0.8) for suicide attempts 20 and death 19,[21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

Ruderfer

Walsh

Aguirre

et al. 2018

Preprint

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“…In this context, index hospitalization refers either to the only hospitalization during the risk period or to a randomly‐selected hospitalization for individuals with multiple hospitalizations. Random selection is consistent with prior work and avoids the bias of selecting the first (or last) observed hospitalization from the observation period at the expense of total follow‐up duration . (See the Analysis section for description of sensitivity analyses incorporating all admissions.…”

Section: Methodsmentioning

confidence: 99%

“…Random selection is consistent with prior work and avoids the bias of selecting the first (or last) observed hospitalization from the observation period at the expense of total follow-up duration. 12…”

Section: Study Design and Cohort Derivationmentioning

confidence: 99%

Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

McCoy

Han

Pellegrini

et al. 2020

Alzheimer's & Dementia

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Introduction Preventing dementia, or modifying disease course, requires identification of presymptomatic or minimally symptomatic high‐risk individuals. Methods We used longitudinal electronic health records from two large academic medical centers and applied a validated natural language processing tool to estimate cognitive symptomatology. We used survival analysis to examine the association of cognitive symptoms with incident dementia diagnosis during up to 8 years of follow‐up. Results Among 267,855 hospitalized patients with 1,251,858 patient years of follow‐up data, 6516 (2.4%) received a new diagnosis of dementia. In competing risk regression, an increasing cognitive symptom score was associated with earlier dementia diagnosis (HR 1.63; 1.54–1.72). Similar results were observed in the second hospital system and in subgroup analysis of younger and older patients. Discussion A cognitive symptom measure identified in discharge notes facilitated stratification of risk for dementia up to 8 years before diagnosis.

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Improving Prediction of Suicide and Accidental Death After Discharge From General Hospitals With Natural Language Processing

Cited by 109 publications

References 28 publications

The use of electronic health records for psychiatric phenotyping and genomics

The use of electronic health records for psychiatric phenotyping and genomics

Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

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