Predicting All-Cause Hospital Readmissions from Medical Claims Data of Hospitalised Patients

• URL: http://arxiv.org/abs/2510.26188v1
• Date: Thu, 30 Oct 2025 06:54:19 GMT
• Title: Predicting All-Cause Hospital Readmissions from Medical Claims Data of Hospitalised Patients
• Authors: Avinash Kadimisetty, Arun Rajagopalan, Vijendra SK,
• Abstract summary: We have used Logistic Regression, Random Forest and Support Vector Machines to analyze the health claims data.<n>These models can be used to identify the crucial factors causing readmissions and help identify patients to focus on to reduce the chances of readmission.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reducing preventable hospital readmissions is a national priority for payers, providers, and policymakers seeking to improve health care and lower costs. The rate of readmission is being used as a benchmark to determine the quality of healthcare provided by the hospitals. In thisproject, we have used machine learning techniques like Logistic Regression, Random Forest and Support Vector Machines to analyze the health claims data and identify demographic and medical factors that play a crucial role in predicting all-cause readmissions. As the health claims data is high dimensional, we have used Principal Component Analysis as a dimension reduction technique and used the results for building regression models. We compared and evaluated these models based on the Area Under Curve (AUC) metric. Random Forest model gave the highest performance followed by Logistic Regression and Support Vector Machine models. These models can be used to identify the crucial factors causing readmissions and help identify patients to focus on to reduce the chances of readmission, ultimately bringing down the cost and increasing the quality of healthcare provided to the patients.

Related papers

• Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases [0.0]
  Intracerebral hemorrhage (ICH) is a life-risking condition characterized by bleeding within the brain parenchyma.<n>This study utilized the Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV) databases to develop predictive models for ICU readmission risk.
  arXiv  Detail & Related papers  (2025-01-02T10:19:27Z)
• Fairness in Computational Innovations: Identifying Bias in Substance Use Treatment Length of Stay Prediction Models with Policy Implications [0.477529483515826]
  Predictive machine learning (ML) models are computational innovations that can enhance medical decision-making.<n>However, societal biases can be encoded into such models, raising concerns about inadvertently affecting health outcomes for disadvantaged groups.<n>This issue is particularly pressing in the context of substance use disorder (SUD) treatment, where biases in predictive models could significantly impact the recovery of highly vulnerable patients.
  arXiv  Detail & Related papers  (2024-12-08T06:47:23Z)
• Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model [4.918444397807014]
  This study analyzes Medicare hospital readmissions using LSTM networks with feature engineering to assess feature contributions. The LSTM model is designed to capture temporal dynamics from admission-level and patient-level data. The major features were the Charlson Comorbidity Index, hospital length of stay, the hospital admissions over the past 6 months, while demographic variables were less impactful.
  arXiv  Detail & Related papers  (2024-10-23T03:50:32Z)
• Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction [65.268245109828]
  This paper uses the MIMIC-IV dataset to examine the fairness and bias in an XGBoost binary classification model predicting the ICU length of stay. The research reveals class imbalances in the dataset across demographic attributes and employs data preprocessing and feature extraction. The paper concludes with recommendations for fairness-aware machine learning techniques for mitigating biases and the need for collaborative efforts among healthcare professionals and data scientists.
  arXiv  Detail & Related papers  (2023-12-31T16:01:48Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Building predictive models of healthcare costs with open healthcare data [0.0]
  We present an approach to developing a predictive model using machine-learning techniques. We analyzed de-identified patient data from New York StateS, consisting of 2.3 million records in 2016. We built models to predict costs from patient diagnoses and demographics.
  arXiv  Detail & Related papers  (2023-04-05T02:12:58Z)
• Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution [67.72545656557858]
  We propose a new method that uses medical text of Electronic Health Records for prediction. We represent discharge summaries of patients with multiview graphs enhanced by an external knowledge graph. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-12-19T01:45:57Z)
• Development of patients triage algorithm from nationwide COVID-19 registry data based on machine learning [1.0323063834827415]
  This paper provides the development processes of the severity assessment model using machine learning techniques. Model only requires basic patients' basic personal data, allowing for them to judge their own severity. We aim to establish a medical system that allows patients to check their own severity and informs them to visit the appropriate clinic center based on the past treatment details of other patients with similar severity.
  arXiv  Detail & Related papers  (2021-09-18T19:56:27Z)
• SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models [48.07469930813923]
  This work aims to forecast the demand for healthcare services, by predicting the number of patient visits to healthcare facilities. We introduce SANSformer, an attention-free sequential model designed with specific inductive biases to cater for the unique characteristics of EHR data. Our results illuminate the promising potential of tailored attention-free models and self-supervised pretraining in refining healthcare utilization predictions across various patient demographics.
  arXiv  Detail & Related papers  (2021-08-31T08:23:56Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• 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.