Population Density-Based Hospital Recommendation with Mobile LBS Big Data

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.

[1]  Jerome L. Myers,et al.  Research Design and Statistical Analysis , 1991 .

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Ralf Steinberger,et al.  Text Mining from the Web for Medical Intelligence , 2007, NATO ASI Mining Massive Data Sets for Security.

[4]  Joaquín A. Pacheco,et al.  Heuristic Solutions for Locating Health Resources , 2008, IEEE Intelligent Systems.

[5]  L. Shan.  Survey and measures on un-affordability of health service in one general hospital outpatient sector , 2008 .

[6]  Taesik Lee,et al.  Heuristics for Locating Two Types of Public Health-Care Facilities , 2012 .

[7]  Mark Dredze,et al.  How Social Media Will Change Public Health , 2012, IEEE Intelligent Systems.

[8]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[9]  Peter Dolog,et al.  Making use of social media data in public health , 2012, WWW.

[10]  Bin Fan,et al.  Cuckoo Filter: Practically Better Than Bloom , 2014, CoNEXT.

[11]  K. Denecke,et al.  Social Media and Internet-Based Data in Global Systems for Public Health Surveillance: A Systematic Review , 2014, The Milbank quarterly.

[12]  Bo Hu,et al.  Social Topic Modeling for Point-of-Interest Recommendation in Location-Based Social Networks , 2014, 2014 IEEE International Conference on Data Mining.

[13]  Xing Xie,et al.  GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation , 2014, KDD.

[14]  Zi Huang,et al.  Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation , 2015, ACM Multimedia.

[15]  Kwan Hui Lim.  Recommending Tours and Places-of-Interest based on User Interests from Geo-tagged Photos , 2015, SIGMOD PhD Symposium.

[16]  Larry A. Wasserman,et al.  On Clinical Pathway Discovery from Electronic Health Record Data , 2015, IEEE Intelligent Systems.

[17]  Yu Liu,et al.  Ghost Cities Analysis Based on Positioning Data in China , 2015, ArXiv.

[18]  Cyrus Shahabi,et al.  GeoSocialBound: an efficient framework for estimating social POI boundaries using spatio--textual information , 2016, GeoRich@SIGMOD.

[19]  Hui Xiong,et al.  POI Recommendation: A Temporal Matching between POI Popularity and User Regularity , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[20]  Huan Liu,et al.  What Your Images Reveal: Exploiting Visual Contents for Point-of-Interest Recommendation , 2017, WWW.