Improving Hospital Readmission Prediction Using Domain Knowledge Based Virtual Examples

Vukičević, Milan; Radovanović, Sandro; Kovačević, Ana; Štiglic, Gregor; Obradović, Zoran

doi:10.1007/978-3-319-21009-4_51

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…Recent works favor the application of predictive machine learning approaches, formulating readmission prediction as a binary classification problem [7, 10]. For example, the literature report results from support vector machines (SVM) [4, 11, 12], deep learning [13, 14], artificial neural network [8], and Naive Bayes [5, 15].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Wolff

Graña

Ríos

et al. 2019

BioMed Research International

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Background. Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions.Objective. To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile.Materials. An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child’s treatment administrative cost.Methods. Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size.Results. Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms.Conclusions. We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Wolff

Graña

Ríos

et al. 2019

BioMed Research International

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“…The survey, including summary and analysis, not only provides a thorough understanding of existing challenges and methods in this field, but also lists available resources to advance the research for accurate hospital readmission prediction and modelling. [6], [73] All cause Medicare 10E5 NA Medicare Current Beneficiary Survey (MCBS) [158] All cause Medicare 10E5 NA Health Care's Enterprise Data Warehouse (EDW) [29] All cause 18+ 10E5 NA Cerner's Millennium® EHR software system [29] All cause NA 10E5 NA New Zealand National Minimum Dataset [30], [87] All cause All patients 10E5 NA National Inpatient Dataset (NIS) [32] All cause All patients 10E5 NA State Inpatient DB (SID) [33], [50], [68], [75], [76], [145] All cause All patients 10E5 NA Nationwide Readmissions Database (NRD) [84] All cause All patients 10E5 NA Resource and Patient Management System (RPMS) [45] All cause All patients 10E5 NA Nationally Rep. Health and Retirement Study (HRS) [47] All cause 50+ 20000 NA Queensland Hosp. Admit.…”

Section: Discussionmentioning

confidence: 99%

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Wang

Zhu

2021

Preprint

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Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

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“…There are many papers focused on predicting hospital readmissions, [1][2][3][4] especially predicting hospital admissions and readmissions to emergency or intensive care units. [5][6][7][8][9][10][11] This editorial focuses on the basics of a knowledge-based recommendation system 12,13 for decreasing the number of readmissions to hospitals, based on actionable knowledge extracted from medical datasets, using the concept of action rules [14][15][16] to provide recommendations.…”

Section: Introductionmentioning

confidence: 99%

Reduction of hospital readmissions

Raś¹

2022

Adv Clin Exp Med

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In recent years, healthcare spending has risen and become a burden on many governments. There are multiple reasons for this increase such as overtesting, long medical treatment path, ignoring doctors' orders, ineffective use of technologies, medical errors, many hospital readmissions, unnecessary emergency room (ER) visits, and medical treatment acquired side effects and infections. The first part of this editorial presents Healthcare Cost and Utilization Project (HCUP) datasets and their hierarchical partition used to build hierarchically structured personalized recommendation systems in healthcare domain. The second part outlines a simple strategy for reducing the number of readmissions using the concept of action rules to provide recommendations. First, we extract from HCUP datasets all possible procedure paths (course of treatments) for a given initial medical procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the course of treatment following this initial procedure. Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions for these patients. There is not much research done on decreasing the number of readmissions to hospitals after initial procedure and almost none based on action rules.

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Improving Hospital Readmission Prediction Using Domain Knowledge Based Virtual Examples

Cited by 11 publications

References 21 publications

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Machine Learning Readmission Risk Modeling: A Pediatric Case Study

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Reduction of hospital readmissions

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