Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels

• URL: http://arxiv.org/abs/2006.13427v1
• Date: Wed, 24 Jun 2020 02:15:15 GMT
• Title: Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels
• Authors: Lichin Chen, Yu Tsao, Ji-Tian Sheu
• Abstract summary: This study used nationwide insurance data, accumulated possible features discussed in existing literature, and used a deep neural network to predict the patients choices of hospital levels. The results showed that the model was able to predict with high area under the receiver operating characteristics curve (AUC) (0.90), accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly imbalanced label.
• Score: 10.985001960872264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In countries that enabled patients to choose their own providers, a common problem is that the patients did not make rational decisions, and hence, fail to use healthcare resources efficiently. This might cause problems such as overwhelming tertiary facilities with mild condition patients, thus limiting their capacity of treating acute and critical patients. To address such maldistributed patient volume, it is essential to oversee patients choices before further evaluation of a policy or resource allocation. This study used nationwide insurance data, accumulated possible features discussed in existing literature, and used a deep neural network to predict the patients choices of hospital levels. This study also used explainable artificial intelligence methods to interpret the contribution of features for the general public and individuals. In addition, we explored the effectiveness of changing data representations. The results showed that the model was able to predict with high area under the receiver operating characteristics curve (AUC) (0.90), accuracy (0.90), sensitivity (0.94), and specificity (0.97) with highly imbalanced label. Generally, social approval of the provider by the general public (positive or negative) and the number of practicing physicians serving per ten thousand people of the located area are listed as the top effecting features. The changing data representation had a positive effect on the prediction improvement. Deep learning methods can process highly imbalanced data and achieve high accuracy. The effecting features affect the general public and individuals differently. Addressing the sparsity and discrete nature of insurance data leads to better prediction. Applications using deep learning technology are promising in health policy making. More work is required to interpret models and practice implementation.

Related papers

• Pre-Ictal Seizure Prediction Using Personalized Deep Learning [0.0]
  Approximately 23 million or 30% of epilepsy patients worldwide suffer from drug-resistant epilepsy (DRE) The unpredictability of seizure occurrences, which causes safety issues as well as social concerns, restrict the lifestyles of DRE patients. The goal of this research was to employ improved technologies and methods to predict seizures up to two hours before onset.
  arXiv  Detail & Related papers  (2024-10-07T21:04:41Z)
• An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records [2.156208381257605]
  We propose a novel, interpretable deep-learning framework for predicting unplanned hospital readmissions. We validate our system on the two predictive tasks of hospital readmission within 30 and 180 days, using real-world data.
  arXiv  Detail & Related papers  (2023-10-16T08:48:52Z)
• Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation [7.2666838978096875]
  Existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme.
  arXiv  Detail & Related papers  (2022-02-25T10:30:29Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• 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)
• Predicting the Travel Distance of Patients to Access Healthcare using Deep Neural Networks [12.155001613499625]
  This study proposes a deep neural network approach to model the complex decision of patient choice in travel distance to access care. We successfully demonstrated the effectiveness of using a CNN-based framework to predict the travel distance of patients.
  arXiv  Detail & Related papers  (2021-12-07T07:34:15Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• 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)
• Improving healthcare access management by predicting patient no-show behaviour [0.0]
  This work develops a Decision Support System (DSS) to support the implementation of strategies to encourage attendance. We assess the effectiveness of different machine learning approaches to improve the accuracy of regression models. In addition to quantifying relationships reported in previous studies, we find that income and neighbourhood crime statistics affect no-show probabilities.
  arXiv  Detail & Related papers  (2020-12-10T14:57:25Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.