Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis

Background Clinical research and medical practice can be advanced through the prediction of an individual’s health state, trajectory, and responses to treatments. However, the majority of current clinical risk prediction models are based on regression approaches or machine learning algorithms that are static, rather than dynamic. To benefit from the increasing emergence of large, heterogeneous data sets, such as electronic health records (EHRs), novel tools to support improved clinical decision making through methods for individual-level risk prediction that can handle multiple variables, their interactions, and time-varying values are necessary. Methods We introduce a novel dynamic approach to clinical risk prediction for survival, longitudinal, and multivariate (SLAM) outcomes, called random forest for SLAM data analysis (RF-SLAM). RF-SLAM is a continuous-time, random forest method for survival analysis that combines the strengths of existing statistical and machine learning methods to produce individualized Bayes estimates of piecewise-constant hazard rates. We also present a method-agnostic approach for time-varying evaluation of model performance. Results We derive and illustrate the method by predicting sudden cardiac arrest (SCA) in the Left Ventricular Structural (LV) Predictors of Sudden Cardiac Death (SCD) Registry. We demonstrate superior performance relative to standard random forest methods for survival data. We illustrate the importance of the number of preceding heart failure hospitalizations as a time-dependent predictor in SCA risk assessment. Conclusions RF-SLAM is a novel statistical and machine learning method that improves risk prediction by incorporating time-varying information and accommodating a large number of predictors, their interactions, and missing values. RF-SLAM is designed to easily extend to simultaneous predictions of multiple, possibly competing, events and/or repeated measurements of discrete or continuous variables over time.Trial registration: LV Structural Predictors of SCD Registry (clinicaltrials.gov, NCT01076660), retrospectively registered 25 February 2010.

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:20

Enthalten in:

BMC medical research methodology - 20(2019), 1 vom: 31. Dez.

Sprache:

Englisch

Beteiligte Personen:

Wongvibulsin, Shannon [VerfasserIn]
Wu, Katherine C. [VerfasserIn]
Zeger, Scott L. [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

Clinical risk prediction
Dynamic risk prediction
Random forests
Survival analysis

Anmerkungen:

© The Author(s) 2019

doi:

10.1186/s12874-019-0863-0

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2101353938

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