Using time series analysis to forecast emergency patient arrivals in CT department

Luo, Li; Feng, Yabing

doi:10.1109/icsssm.2015.7170134

Cited by 3 publications

References 6 publications

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A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

Safdarid¹,

Khan

Shaukat

2022

IEEE Access

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This paper proposes an adaptive data-driven novel technique for generating physician's schedules exploring the trade-off between physician waiting time and patient experience. Generally, in hospitals fix time slots are assigned to the patients, without differentiating patients into different categories. Some patients are referral patients who need less time, others are first-time visitors who need more time than referral patients. Some are serious patients who take even more time, so a fixed time slot can increase the waiting time both at the doctor and the patient level. So, there is a need to categorize patients according to their treatment which could help in improving the waiting time of doctors and as well of the patients. This technique trains the patient's data according to their specific category based on the time doctors usually spend on them. This can help in generating a schedule that will minimize the waiting of patients and at the same time improve the hospital performance by scheduling maximum patients ensuring that the same patient's get the best experience. To achieve this a data-driven scheduling algorithm (DDSA) is proposed which uses probability distribution, clustering and doctor's scheduling algorithm to classify patients into different categories. The data is collected through the RFID machines. The patients are given cluster head time for the treatment. Different patient categories are given different treatment times. Then patient and doctor commutative waiting times are calculated. The system applies different set-up times to allow hospital management to trade-off between doctor and patient waiting times. The average waiting time for each patient using DDSA comes out to be seven minutes. Hence, this technique can help hospitals across the globe in improving their performance and patient experience.

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A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

Safdarid¹,

Khan

Shaukat

2022

IEEE Access

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Health Care Management System Using Time Series Analysis

Shukla

Singh

Neopane

et al. 2019

2019 4th International Conference on Information Systems and Computer Networks (ISCON)

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Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM

Liu

Wang

Aftab

2022

Front. Bioeng. Biotechnol.

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In order to more accurately and comprehensively characterize the changes and development rules of lesion characteristics in pulmonary medical images in different periods, the study was conducted to predict the evolution of pulmonary nodules in the longitudinal dimension of time, and a benign and malignant prediction model of pulmonary lesions in different periods was constructed under multiscale three-dimensional (3D) feature fusion. According to the sequence of computed tomography (CT) images of patients at different stages, 3D interpolation was conducted to generate 3D lung CT images. The 3D features of different size lesions in the lungs were extracted using 3D convolutional neural networks for fusion features. A time-modulated long short-term memory was constructed to predict the benign and malignant lesions by using the improved time-length memory method to learn the feature vectors of lung lesions with temporal and spatial characteristics in different periods. The experiment shows that the area under the curve of the proposed method is 92.71%, which is higher than that of the traditional method.

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Using time series analysis to forecast emergency patient arrivals in CT department

Cited by 3 publications

References 6 publications

A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

A Novel Data-Driven Adaptive Technique to Generate a Physician Visiting Schedule for Better Patient Experience

Health Care Management System Using Time Series Analysis

Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM

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