A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction

• URL: http://arxiv.org/abs/2608.04180v1
• Date: Tue, 04 Aug 2026 19:37:29 GMT
• Title: A Comparative Study of Feature Selection Methods for EHR Diagnosis Codes in Opioid Use Disorder Prediction
• Abstract summary: We focus on diagnosis-related features and compare five feature selection paradigms for opioid use disorder (OUD) prediction.<n>We use a unified preprocessing and evaluation framework and assess each method by downstream predictive performance, resampling stability, and representation of infrequent diagnosis codes.<n>Our results demonstrate that performance improves with larger feature budgets with diminishing returns beyond a moderate size.
• Score: 14.398540855932708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feature selection is a critical step in electronic health record (EHR)-based predictive modeling, where input variables are often high-dimensional, sparse, noisy, and redundant. Large feature sets not only increase computational burden and overfitting risk, but also make model interpretation difficult, leading to limited usefulness in clinical settings. In this study, we focus on diagnosis-related features and compare five feature selection paradigms for opioid use disorder (OUD) prediction: recurrence enrichment, NTK-motivated early gradient sensitivity, LightGBM-SHAP, Elastic Net, and large language model (LLM)-guided semantic selection. We use a unified preprocessing and evaluation framework and assess each method by downstream predictive performance, resampling stability, and representation of infrequent diagnosis codes. Our results demonstrate that performance improves with larger feature budgets with diminishing returns beyond a moderate size. NTK sensitivity provides the best overall balance of accuracy and stability, and LLM-guided selection contributes complementary clinically meaningful signals despite lower standalone performance.

Related papers

• SMILE: Self-Explainable Multimodal Information Bottleneck for Medical Diagnosis [71.98702207668346]
  Explainability is increasingly seen as a crucial requirement in AI-based medical diagnosis, particularly in safety-critical clinical decision-making.<n>This paper addresses the problem of self-explainable multimodal diagnosis by formulating it within the information bottleneck (IB) framework.<n>We propose a unified learning paradigm that jointly optimize predictive performance and modality-specific explainability.
  arXiv  Detail & Related papers  (2026-09-04T14:13:24Z)
• Explainable AI for Mental Health Prediction in Drug-Affected Populations with Dragonfly Algorithm and GAN Oversampling [0.526647163965749]
  This study present a detailed, explainable machine learning (ML) model of mental health prediction using a multidimensional data set of drug-affected persons.<n>The behavioral, lifestyle, and health factors, particularly sleep quality, physical health, and emotional regulation, are strongly predictive of mental health.<n>This framework has the potential to generate valid mental health forecasting tools, which would facilitate early intervention and enhance the treatment of drug-influenced people.
  arXiv  Detail & Related papers  (2026-06-22T02:42:58Z)
• Timely Clinical Diagnosis through Active Test Selection [49.091903570068155]
  We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design) to better emulate real-world diagnostic reasoning.<n>LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data.<n>We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use.
  arXiv  Detail & Related papers  (2025-10-21T18:10:45Z)
• DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data [26.378114734793492]
  We propose DeepSelective, a novel end to end deep learning framework for predicting patient prognosis using EHR data.<n>DeepSelective combines data compression techniques with an innovative feature selection approach, integrating custom-designed modules.<n>Our experiments demonstrate that DeepSelective not only enhances predictive accuracy but also significantly improves interpretability, making it a valuable tool for clinical decision-making.
  arXiv  Detail & Related papers  (2025-04-15T15:04:39Z)
• Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability [0.0]
  This study aims to use heart rate variability (HRV) features to develop an effective predictive model for sepsis detection. A neural network model is trained on the HRV features, achieving an F1 score of 0.805, a precision of 0.851, and a recall of 0.763.
  arXiv  Detail & Related papers  (2024-08-01T01:47:29Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Unified Uncertainty Estimation for Cognitive Diagnosis Models [70.46998436898205]
  We propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. We decompose the uncertainty of diagnostic parameters into data aspect and model aspect. Our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
  arXiv  Detail & Related papers  (2024-03-09T13:48:20Z)
• Evaluating Echo State Network for Parkinson's Disease Prediction using Voice Features [1.2289361708127877]
  This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives. Various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. ESN consistently maintains a false negative rate of less than 8% in 83% of cases.
  arXiv  Detail & Related papers  (2024-01-28T14:39:43Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty [57.023423137202485]
  Concerns regarding the reliability of medical image segmentation persist among clinicians.<n>We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks.<n>By leveraging subjective logic theory, we explicitly model probability and uncertainty for medical image segmentation.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder [7.0876609220947655]
  Noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD) This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI.
  arXiv  Detail & Related papers  (2022-04-07T08:50:48Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.