ArticlePloS one2015
Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review.BMJ open · 2022Pooled it
- Value-based neural representations predict social decision preferences.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea.Diagnostics (Basel, Switzerland) · 2021Article
- Establishment of a novel nomogram for the clinically diagnostic prediction of minimal change disease, -a common cause of nephrotic syndrome.BMC nephrology · 2020Article
- Analysing repeated hospital readmissions using data mining techniques.Health systems (Basingstoke, England) · 2018Article
- Bayesian hierarchical vector autoregressive models for patient-level predictive modeling.PloS one · 2018Article
- Analysing repeated hospital readmissions using data mining techniques.Health systems (Basingstoke, England) · 2018Article
- Observational
- Large-scale Analysis of Opioid Poisoning Related Hospital Visits in New York State.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients.PeerJ · 2017Article
- A Framework for Considering Comprehensibility in Modeling.Big data · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755-0.771) to 0.769 (95% CI: 0.761-0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.
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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.