Artificial Intelligence for the Health Ecosystems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2021) | Viewed by 5322

Special Issue Editors


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Guest Editor
Institute of Telematics, University of Lübeck, 23562 Lübeck, Germany
Interests: computer networks; distributed systems; nano networks; AI in distributed systems; connected health
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Guest Editor
Department of Radiology and Nuclear Medicine, University Medical Center Schleswig-Holstein, 23562 Lübeck, Germany
Interests: preclinical and clinical imaging; artificial intelligence in radiology; cardiovascular diseases; precision medicine

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Guest Editor
Institute for Neuro and Bioinformatics, University of Lübeck, 23562 Lübeck, Germany
Interests: neural networks; machine learning; AI for image processing; AI for pattern recognition

Special Issue Information

Dear Colleagues,

An ecosystem is a decentralized network in which participants establish mutual relationships. They set up interfaces for accessing their own data, share it and thus allow the other actors to work. Joint application/business models are developed, prototypes are experimented on and creative cooperations are entered into. This network enables the actors to engage in professional exchange with other ecosystem participants. In the foreground of a medical ecosystem are its players—patients, physicians, scientists, hospitals, health insurance companies, medical technology producers, pharmaceutical companies, etc.—and their interactions. Important topics that a health ecosystem works with are clinical use cases, the development of physiological reference models, and, in particular, disruptive applications in health care, often based on common platforms.

Artificial intelligence is considered a game-changer for any health ecosystem. It creates new challenges, such as much more complex regulatory issues, but it also brings with it a multitude of new technical possibilities and business opportunities. To name just a few examples, AI can be used for supporting diagnosis, patient-specific therapies (precision medicine), faster drug developments, improved gene editing, automation of processes in the hospital, and new business models for startups, etc.

This Special Issue is intended to cover all areas of the application of artificial intelligence in health ecosystems. Contributions can be of a technical nature, but can also concern the redesigning of business models, processes, and relationships through the influence of AI. Finally, the focus can also be on medical, medical-technical, pharmaceutical, or nutritional issues.

Prof. Dr. Stefan Fischer
Prof. Dr. Jörg Barkhausen
Prof. Dr. Thomas Martinetz
Guest Editors

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Keywords

  • artificial intelligence
  • health ecosystem
  • platforms
  • medical applications
  • medical technology
  • eHealth
  • nutrition
  • clinical use cases
  • health ecosystem networks
  • application-oriented training
  • AI-based startups

Published Papers (1 paper)

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Research

11 pages, 8881 KiB  
Article
An AI-Based Exercise Prescription Recommendation System
by Hung-Kai Chen, Fueng-Ho Chen and Shien-Fong Lin
Appl. Sci. 2021, 11(6), 2661; https://doi.org/10.3390/app11062661 - 16 Mar 2021
Cited by 6 | Viewed by 4811
Abstract
The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance [...] Read more.
The European Association of Preventive Cardiology Exercise Prescription in Everyday Practice and Rehabilitative Training (EXPERT) tool has been developed for digital training and decision support in cardiovascular disease patients in clinical practice. Exercise prescription recommendation systems for sub-healthy people are essential to enhance this dominant group’s physical ability as well. This study aims to construct a guided exercise prescription system for sub-healthy groups using exercise community data to train an AI model. The system consists of six modules, including three-month suggested exercise mode (3m-SEM), predicted value of rest heart rate (rest HR) difference after following three-month suggested exercise mode (3m-PV), two-month suggested exercise mode (2m-SEM), predicted value of rest HR difference after following two-month suggested exercise mode (2m-PV), one-month suggested exercise mode (1m-SEM) and predicted value of rest HR difference after following one-month suggested exercise mode (1m-PV). A new user inputs gender, height, weight, age, and current rest HR value, and the above six modules will provide the user with a prescription. A four-layer neural network model is applied to construct the above six modules. The AI-enabled model produced 95.80%, 100.00%, and 95.00% testing accuracy in 1m-SEM, 2m-SEM, and 3m-SEM, respectively. It reached 3.15, 2.89, and 2.75 BPM testing mean absolute error in 1m-PV, 2m-PV, and 3m-PV. The developed system provides quantitative exercise prescriptions to guide the sub-healthy group to engage in effective exercise programs. Full article
(This article belongs to the Special Issue Artificial Intelligence for the Health Ecosystems)
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