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Construction of longitudinal prediction targets using semisupervised learning.

Booil Jo¹, Robert L Findling², Trevor J Hastie¹

¹1 Stanford University, Stanford, USA.

Statistical Methods in Medical Research

|January 10, 2017

Summary

This summary is machine-generated.

This study enhances prognostic models by rigorously improving outcome prediction using longitudinal data. It introduces a novel approach combining empirical fitting, clinical insights, and cross-validation for robust target validation.

Keywords:

Prognostic model clinical threshold cross-validation latent trajectory class semisupervised learning

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Area of Science:

Biostatistics
Machine Learning in Healthcare
Longitudinal Data Analysis

Background:

Prognostic model development often prioritizes predictor identification over outcome definition.
Longitudinal information offers potential for improving outcome characterization but is underutilized.
Challenges exist in characterizing, classifying, and validating longitudinal prediction targets.

Purpose of the Study:

To address the neglected aspect of outcome improvement in prognostic model development.
To explore the utility of longitudinal data for refining prediction targets.
To propose and illustrate a method for validating longitudinal prediction targets.

Main Methods:

Joint use of empirical model fitting and clinical insights.
Cross-validation using antecedent validators (clinically relevant baseline characteristics).
Triangulation of valid prediction targets through imperfect but complementary methods.

Main Results:

Demonstration of a practical approach to validating longitudinal prediction targets.
Improved characterization and classification of individual outcome status.
Successful illustration using data from the longitudinal assessment of manic symptoms study.

Conclusions:

A novel, integrated approach can effectively validate longitudinal prediction targets for prognostic models.
Combining empirical, clinical, and validation methods enhances the rigor of outcome definition.
This methodology can increase the practical use of longitudinal data in clinical prediction.