Population Density-based Hospital Recommendation with Mobile LBS Big Data

TL;DR

This paper introduces a model utilizing LBS big data and deep learning to estimate hospital population densities in real-time, aiding patients in selecting less crowded hospitals and improving healthcare accessibility.

Contribution

The paper develops a novel LBS data-based model with denoising, behavior classification, and LSTM prediction for real-time hospital density estimation and recommendation.

Findings

Effective denoising and classification of LBS data.

Accurate prediction of population density trends.

Successful deployment in Beijing hospitals.

Abstract

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system.