ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Balancing Performance and Interpretability: Selecting Features with Bootstrapped Ridge Regression.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for self-harm and suicide: a systematic review and critical appraisal.BMC medicine · 2025Pooled it
- Task-specific determinants of one-year performance after unilateral primary total knee arthroplasty.Bone & joint open · 2026Article
- Nomogram Development and Feature Selection Strategy Comparison for Predicting Surgical Site Infection After Lower Extremity Fracture Surgery.Medicina (Kaunas, Lithuania) · 2025Article
- Stigma, biomarkers, and algorithmic bias: recommendations for precision behavioral health with artificial intelligence.JAMIA open · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Informctticists sometimes attempt to predict chronic healthcare events that are not fully understood. The resulting models often incorporate copious numbers of predictors derived across diverse datasets. This approach may yield desirable performance characteristics, but it sacrifices interpretability and portability. The Bootstrapped Ridge Selector (BoRidge) offers a tool to balance performance with interpretability. Compared to two modern feature selection methods, Bootstrapped LASSO regression (BoLASSO) and a minimal-redundancy-maximal-relevance selector (mRMR), the BoRidge bested them for binary classification on artificially generated data (sensitivity: 0.83, specificity:0.72) versus BoLASSO (sensitivity: 0.1, specificity:1) and mRMR (sensitivity: 0.69, specificity: 0.69). On a dataset used to validate a published suicide risk prediction model, the BoRidge selected an equally precise model to the publication, with far fewer predictors (114 versus the 1,538 used in the published model). The BoRidge has the potential to simplify classification models for complex problems, making them easier to translate and act upon.
Indexed as
Identifiers
30815182PMC6371276What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.