Visualization of the Health Care System with NLP And GIS As Modern Tools of Technological Development

This paper combines accessibility analysis of NLP and GIS using traditional visual interfaces on health care systems. Natural Language Processing (NLP) approach provides support for the interaction between users and the information system. GIS is optimizing urban mobility data by providing location-based services through NLP tools. In this scenario, NLP provides users with precise expressions, consultation, and queries of synonyms and qualitative expressions of users (patients). NLP and GIS data exchange proposes a self-powerful interface method that help users wherever. NLP helps medical personnel to communicate with patients and collect data from patients while GIS help identify, locate and optimize urban mobility data for remote specialists and emergency worker.

To ascertain the massification of NLP and GIS with users for a perfect human-human and machine interaction, a GIS survey was conducted and imported to the paper. The combined system is defined and considered by the authors to evaluate the feasibility and relevance of (1) accessibility of technical skills required to perform remote activities, (2) Time required to identify the desired results, (3) Quality and quantity of human perception of remote and the standardization of the format to perform queries, (4) expectations and results from an experiment to performed task using a traditional GIS interface (ArcGIS) and an NLP based interface.

Machine learning models provide little or no explanations to the predictions. The study use NLP and GIS that enable an explainable machine learning system example that focus on interpreting the input data and outputs dat. or the connections between inputs and outputs data for health care systems.