A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework

• URL: http://arxiv.org/abs/2212.11879v1
• Date: Thu, 22 Dec 2022 17:14:10 GMT
• Title: A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework
• Authors: Abdulaziz Ahmed, Khalid Y.Aram, Salih Tutun
• Abstract summary: This paper proposes a framework for studying the factors that affect left before treatment complete outcomes in emergency departments. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED.
• Score: 1.933681537640272
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adverse effects. This paper proposes a framework for studying the factors that affect LBTC outcomes in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization--one of the main challenges of machine learning model development. Three metaheuristic optimization algorithms are employed for optimizing the parameters of extreme gradient boosting (XGB), which are simulated annealing (SA), adaptive simulated annealing (ASA), and adaptive tabu simulated annealing (ATSA). The optimized XGB models are used to predict the LBTC outcomes for the patients under treatment in ED. The designed algorithms are trained and tested using four data groups resulting from the feature selection phase. The model with the best predictive performance is interpreted using SHaply Additive exPlanations (SHAP) method. The findings show that ATSA-XGB outperformed other mode configurations with an accuracy, area under the curve (AUC), sensitivity, specificity, and F1-score of 86.61%, 87.50%, 85.71%, 87.51%, and 86.60%, respectively. The degree and the direction of effects of each feature were determined and explained using the SHAP method.

Related papers

• Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques [0.0]
  Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. We implemented six machine learning models using the MIMIC-III database.
  arXiv  Detail & Related papers  (2024-08-02T09:44:18Z)
• Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis [6.796017024594715]
  We suggest two novel feature selection (FS) methods based upon an imperialist competitive algorithm (ICA) and a bat algorithm (BA) This study aims to enhance diagnostic models' efficiency and present a comprehensive analysis to help clinical physicians make much more precise and reliable decisions than before.
  arXiv  Detail & Related papers  (2024-07-19T19:07:53Z)
• An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations [3.7851234061033847]
  Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs.
  arXiv  Detail & Related papers  (2022-12-22T17:25:12Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Robust and Agnostic Learning of Conditional Distributional Treatment Effects [62.44901952244514]
  The conditional average treatment effect (CATE) is the best point prediction of individual causal effects. In aggregate analyses, this is usually addressed by measuring distributional treatment effect (DTE) We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a wide class of problems.
  arXiv  Detail & Related papers  (2022-05-23T17:40:31Z)
• Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation [48.821062916381685]
  Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. In this work, we propose an efficient reinforcement learning(RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset and two real-world medical image segmentation datasets.
  arXiv  Detail & Related papers  (2022-03-12T04:11:42Z)
• An Integrated Optimization and Machine Learning Models to Predict the Admission Status of Emergency Patients [1.0323063834827415]
  Three machine learning algorithms are proposed: T-XGB, T-ADAB, and T-MLP. The proposed framework can mitigate the crowding problem by proactively planning the patient boarding process. The results show that the newly proposed algorithms resulted in high AUC and outperformed the traditional algorithms.
  arXiv  Detail & Related papers  (2022-02-18T13:50:44Z)
• Pseudo-Spherical Contrastive Divergence [119.28384561517292]
  We propose pseudo-spherical contrastive divergence (PS-CD) to generalize maximum learning likelihood of energy-based models. PS-CD avoids the intractable partition function and provides a generalized family of learning objectives.
  arXiv  Detail & Related papers  (2021-11-01T09:17:15Z)
• Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis [87.31348761201716]
  Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. BaBSim.Hospital is a tool for capacity planning based on discrete event simulation. We aim to investigate and optimize these parameters to improve BaBSim.Hospital.
  arXiv  Detail & Related papers  (2021-05-16T12:38:35Z)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)

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.