Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients

• URL: http://arxiv.org/abs/2406.05189v1
• Date: Fri, 7 Jun 2024 18:13:21 GMT
• Title: Analyzing the factors that are involved in length of inpatient stay at the hospital for diabetes patients
• Authors: Jorden Lam, Kunpeng Xu,
• Abstract summary: The paper investigates the escalating concerns surrounding the surge in diabetes cases, exacerbated by the COVID-19 pandemic. The research aims to construct a predictive model quantifying factors influencing inpatient hospital stay durations for diabetes patients.
• Score: 1.534667887016089
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The paper investigates the escalating concerns surrounding the surge in diabetes cases, exacerbated by the COVID-19 pandemic, and the subsequent strain on medical resources. The research aims to construct a predictive model quantifying factors influencing inpatient hospital stay durations for diabetes patients, offering insights to hospital administrators for improved patient management strategies. The literature review highlights the increasing prevalence of diabetes, emphasizing the need for continued attention and analysis of urban-rural disparities in healthcare access. International studies underscore the financial implications and healthcare burden associated with diabetes-related hospitalizations and complications, emphasizing the significance of effective management strategies. The methodology involves a quantitative approach, utilizing a dataset comprising 10,000 observations of diabetic inpatient encounters in U.S. hospitals from 1999 to 2008. Predictive modeling techniques, particularly Generalized Linear Models (GLM), are employed to develop a model predicting hospital stay durations based on patient demographics, admission types, medical history, and treatment regimen. The results highlight the influence of age, medical history, and treatment regimen on hospital stay durations for diabetes patients. Despite model limitations, such as heteroscedasticity and deviations from normality in residual analysis, the findings offer valuable insights for hospital administrators in patient management. The paper concludes with recommendations for future research to address model limitations and explore the implications of predictive models on healthcare management strategies, ensuring equitable patient care and resource allocation.

Related papers

• Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail and Elderly Patient Services [0.0]
  The aim of this research is to assess how various predictive and prescriptive analytical methods contribute to addressing the operational challenges within an area of healthcare that is growing in demand. On the prescriptive side, deterministic and two-stage programs are developed to determine how to optimally plan for beds and ward staff. Our research reveals that healthcare managers should consider using predictive and prescriptive models to make more informed decisions.
  arXiv  Detail & Related papers  (2023-11-13T12:25:45Z)
• Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers [69.81176740997175]
  We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality. Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
  arXiv  Detail & Related papers  (2023-06-01T14:08:05Z)
• Privacy-preserving machine learning for healthcare: open challenges and future perspectives [72.43506759789861]
  We conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service. The aim of this review is to guide the development of private and efficient ML models in healthcare.
  arXiv  Detail & Related papers  (2023-03-27T19:20:51Z)
• Hospitalization Length of Stay Prediction using Patient Event Sequences [4.204781617630707]
  This paper proposes a novel approach for predicting hospital length of stay (LOS) by modeling patient information as sequences of events. We present a transformer-based model, termed Medic-BERT (M-BERT), for LOS prediction using the unique features describing patients medical event sequences. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional nonsequence-based machine learning approaches.
  arXiv  Detail & Related papers  (2023-03-20T11:48:36Z)
• A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches [0.0]
  Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. In this study, we reviewed papers on LOS prediction using machine learning and statistical approaches.
  arXiv  Detail & Related papers  (2021-12-30T03:48:41Z)
• Towards Trustworthy Cross-patient Model Development [3.109478324371548]
  We study differences in model performance and explainability when trained for all patients and one patient at a time. The results show that patients' demographics has a large impact on the performance and explainability and thus trustworthiness.
  arXiv  Detail & Related papers  (2021-12-20T10:51:04Z)
• 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)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• 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)
• From predictions to prescriptions: A data-driven response to COVID-19 [42.57407485467993]
  We propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19. We build personalized calculators to predict the risk of infection and mortality. We propose an optimization model to re-allocate ventilators and alleviate shortages.
  arXiv  Detail & Related papers  (2020-06-30T03:34:00Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

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.