ArticleStatistical methods in medical research2018
Construction of longitudinal prediction targets using semisupervised learning.
Article in Statistical methods in medical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review.Journal of medical Internet research · 2021Article
- Can Machine Learning Improve Screening for Targeted Delinquency Prevention Programs?Prevention science : the official journal of the Society for Prevention Research · 2020Article
- A Risk Calculator to Predict the Individual Risk of Conversion From Subthreshold Bipolar Symptoms to Bipolar Disorder I or II in Youth.Journal of the American Academy of Child and Adolescent Psychiatry · 2018Article
- Reorienting Latent Variable Modeling for Supervised Learning.Multivariate behavioral researchArticle
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Authors and funding
11 authors.
Funding
Abstract
In establishing prognostic models, often aided by machine learning methods, much effort is concentrated in identifying good predictors. However, the same level of rigor is often absent in improving the outcome side of the models. In this study, we focus on this rather neglected aspect of model development. We are particularly interested in the use of longitudinal information as a way of improving the outcome side of prognostic models. This involves optimally characterizing individuals' outcome status, classifying them, and validating the formulated prediction targets. None of these tasks are straightforward, which may explain why longitudinal prediction targets are not commonly used in practice despite their compelling benefits. As a way of improving this situation, we explore the joint use of empirical model fitting, clinical insights, and cross-validation based on how well formulated targets are predicted by clinically relevant baseline characteristics (antecedent validators). The idea here is that all these methods are imperfect but can be used together to triangulate valid prediction targets. The proposed approach is illustrated using data from the longitudinal assessment of manic symptoms study.
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Registered trials
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