Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 19, 2018
Open Peer Review Period: Dec 31, 2018 - Feb 25, 2019
Date Accepted: Feb 22, 2020
(closed for review but you can still tweet)
Predicting Chronic Respiratory Diseases Urgency Demand of Outpatient and Emergency Departments Visitors Using Adaptive Boosting Double-Layer Perceptron
ABSTRACT
Background:
Efficient management of patient flow in outpatient and emergency departments (OEDs) has become an urgent issue for many hospital administrations. The overcrowding of hospital OEDs due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in degradation of quality of medical care, and even limits it's availability. Predicting the urgency demand of OEDs arrivals is supposed as a challenge to adapt medical service in advance.
Objective:
In order to adapt medical service in advance, this paper proposes a novel approach to predict the urgency demand of OEDs arrivals.
Methods:
In this paper, we develop an adaptive boosting-based ensemble classifier by using weak learners double-layer perceptron, which can predict the peak event (peak demand days) of the OEDs visiting patients with chronic respiratory diseases, mainly asthma, respiratory infection, and chronic obstructive pulmonary disease (COPD) combining with corresponding daily weather data and environmental pollution data in Guangzhou of China. We identify 13218 patients’ visits to OEDs in hospitals with chronic respiratory diseases from 1/1/2016 to 12/31/2017.
Results:
The proposed classifier yields a detection accuracy of 86.49% with high Precision, Recall and F-Measures.
Conclusions:
The proposed model is generic enough to cope with similar medical scenarios, provided that the data is available.
Citation
Per the author's request the PDF is not available.
Copyright
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