Machine Learning and Artificial Intelligence in Healthcare with Big Data Analytics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2131

Special Issue Editor

Special Issue Information

Dear Colleagues,

With the prompt development of information technology, machine learning and artificial intelligence have been prevailing in analyzing data for decision makers. The aim of this Special Issue is to explore applications of machine learning and artificial intelligence with big data analytics in healthcare. For example, using artificial intelligence to make a complete digital patient profile. It can comprise of lab reports, past medical procedures, diagnostics, and more to allow medical practitioners easy access and interpretation. This will allow them to provide faster and more personalized treatment plans. The scopes include but are not limited to the following issues:

  1. Chatbot for patient administration (clerical) processes;
  2. Insurances bot for Process insurance claims, pre-authorization of insurance;
  3. Applications of pattern recognition algorithms in healthcare;
  4. High risk detection for inpatient safety;
  5. Disease prediction;
  6. Speeding up the DRG (Diagnostic Related Groups) coding process;
  7. Recommend system for health promotion related suggestions;
  8. Medical record problems list generator;
  9. Artificial intelligence and big data analytics for forecasting infectious diseases;
  10. Survey papers in related fields.

Prof. Dr. Ping-Feng Pai
Guest Editor

Manuscript Submission Information

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Keywords

  • machine learning
  • artificial intelligence
  • healthcare
  • big data

Published Papers (1 paper)

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Research

20 pages, 2246 KiB  
Article
Using Medical Data and Clustering Techniques for a Smart Healthcare System
by Wen-Chieh Yang, Jung-Pin Lai, Yu-Hui Liu, Ying-Lei Lin, Hung-Pin Hou and Ping-Feng Pai
Electronics 2024, 13(1), 140; https://doi.org/10.3390/electronics13010140 - 28 Dec 2023
Cited by 1 | Viewed by 1654
Abstract
With the rapid advancement of information technology, both hardware and software, smart healthcare has become increasingly achievable. The integration of medical data and machine-learning technology is the key to realizing this potential. The quality of medical data influences the results of a smart [...] Read more.
With the rapid advancement of information technology, both hardware and software, smart healthcare has become increasingly achievable. The integration of medical data and machine-learning technology is the key to realizing this potential. The quality of medical data influences the results of a smart healthcare system to a great extent. This study aimed to design a smart healthcare system based on clustering techniques and medical data (SHCM) to analyze potential risks and trends in patients in a given time frame. Evidence-based medicine was also employed to explore the results generated by the proposed SHCM system. Thus, similar and different discoveries examined by applying evidence-based medicine could be investigated and integrated into the SHCM to provide personalized smart medical services. In addition, the presented SHCM system analyzes the relationship between health conditions and patients in terms of the clustering results. The findings of this study show the similarities and differences in the clusters obtained between indigenous patients and non-indigenous patients in terms of diseases, time, and numbers. Therefore, the analyzed potential health risks could be further employed in hospital management, such as personalized health education control, personal healthcare, improvement in the utilization of medical resources, and the evaluation of medical expenses. Full article
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