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Health risk prediction models incorporating personality data: Motivation, challenges, and illustration.

ABSTRACT: The age of "big data" in health has ushered in an era of prediction models promising to forecast individual health events. Although many models focus on enhancing the predictive power of medical risk factors with genomic data, a recent proposal is to augment traditional health predictors with psychosocial data, such as personality measures. In this article we provide a general overview of the medical risk prediction models and then discuss the rationale for integrating personality data. We suggest three principles that should guide work in this area if personality data is ultimately to be useful within risk prediction as it is actually practiced in the health care system. These include (a) prediction of specific, priority health outcomes; (b) sufficient incremental validity beyond established biomedical risk factors; and (c) technically responsible model-building that does not overfit the data. We then illustrate the application of these principles in the development of a personality-augmented prediction model for the occurrence of mild cognitive impairment, designed for a primary care setting. We evaluate the results, drawing conclusions for the direction an iterative, programmatic approach would need to take to eventually achieve clinical utility. Although there is great potential for personality measurement to play a key role in the coming era of risk prediction models, the final section reviews the many challenges that must be faced in real-world implementation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

SUBMITTER: Chapman BP

PROVIDER: S-EPMC6319275 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Health risk prediction models incorporating personality data: Motivation, challenges, and illustration.

Chapman Benjamin P BP Lin Feng F Roy Shumita S Benedict Ralph H B RHB Lyness Jeffrey M JM

Personality disorders 20190101 1

The age of "big data" in health has ushered in an era of prediction models promising to forecast individual health events. Although many models focus on enhancing the predictive power of medical risk factors with genomic data, a recent proposal is to augment traditional health predictors with psychosocial data, such as personality measures. In this article we provide a general overview of the medical risk prediction models and then discuss the rationale for integrating personality data. We sugge ...[more]

PMID: 30604983

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Project description:BackgroundSuicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data.ObjectivesA set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014-2017) from these systems.MethodsWe evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value.ResultsModels were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death.ConclusionPerformance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.

Dataset Information

Health risk prediction models incorporating personality data: Motivation, challenges, and illustration.

Publications

Health risk prediction models incorporating personality data: Motivation, challenges, and illustration.

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