Artificial Intelligence and Decision Support System Research for Patient-Centric Care

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 3516

Special Issue Editor


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Guest Editor
School of Global Health Management and Informatics, University of Central Florida, Orlando, FL 32816, USA
Interests: health informatics; decision analysis and modeling; health service outcomes and evaluation; artificial intelligence in healthcare; population health management; patient-centric care management for chronic conditions; shared decision making in healthcare; aging and health; evidence-based healthcare management
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Special Issue Information

Dear Colleagues,

Human factor principles play an important role in healing design and processes, particularly when it comes to chronic conditions. Systematic reviews and meta-analyses of clinical trial studies on reducing hospitalization and readmission can yield valuable information or positive proof of the beneficial effect of specific care management strategies that may alter patients’ knowledge, motivation, attitudes, preventive practice, and outcomes. The primary aim of this Special Issue on “Artificial Intelligence (AI) and Decision Support System (DSS) Research and Applications in Patient-Centric Care” is twofold: 1) the identification of the transdisciplinary convergemce in theoretical formulations to account for the variability in healthcare outcomes; and 2) a demonstration of empirical or methodological approaches to AI and DSS applications. Interventions with human factor principles can be designed to reduce the risk of readmissions and to improve outcomes for patients. Ultimately, this Special Issue may help to direct future research in the field of AI applications in healthcare and to reconfigure the design, implementation, and evaluation of clinical practice for the prevention, diagnosis, treatment, and rehabilitation of chronic illnesses at the patient and population levels. 

Topics of interest include but are not limited to:

  • Chronic care management;
  • Patient-centered care;
  • AI applications;
  • Systematic reviews and meta-analyses;
  • Convergence and simulation research;
  • AI methods or techniques;
  • Outcomes research;
  • Population health management;
  • Transdisciplinary approaches;
  • Software design;
  • Innovative care modalities;
  • Health informatics and evaluation;
  • Clinical decision support systems for chronic care.

Dr. Thomas Wan
Guest Editor

Manuscript Submission Information

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Published Papers (1 paper)

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Research

27 pages, 5627 KiB  
Article
Data Analysis of the Risks of Type 2 Diabetes Mellitus Complications before Death Using a Data-Driven Modelling Approach: Methodologies and Challenges in Prolonged Diseases
by Ming-Yen Lin, Jia-Sin Liu, Tzu-Yang Huang, Ping-Hsun Wu, Yi-Wen Chiu, Yihuang Kang, Chih-Cheng Hsu, Shang-Jyh Hwang and Hsing Luh
Information 2021, 12(8), 326; https://doi.org/10.3390/info12080326 - 12 Aug 2021
Cited by 4 | Viewed by 3013
Abstract
(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of [...] Read more.
(1) Background: A disease prediction model derived from real-world data is an important tool for managing type 2 diabetes mellitus (T2D). However, an appropriate prediction model for the Asian T2D population has not yet been developed. Hence, this study described construction details of the T2D Holistic Care model via estimating the probability of diabetes-related complications and the time-to-occurrence from a population-based database. (2) Methods: The model was based on the database of a Taiwan pay-for-performance reimbursement scheme for T2D between November 2002 and July 2017. A nonhomogeneous Markov model was applied to simulate multistate (7 main complications and death) transition probability after considering the sequential and repeated difficulties. (3) Results: The Markov model was constructed based on clinical care information from 163,452 patients with T2D, with a mean follow-up time of 5.5 years. After simulating a cohort of 100,000 hypothetical patients over a 10-year time horizon based on selected patient characteristics at baseline, a good predicted complication and mortality rates with a small range of absolute error (0.3–3.2%) were validated in the original cohort. Better and optimal predictabilities were further confirmed compared to the UKPDS Outcomes model and applied the model to other Asian populations, respectively. (4) Contribution: The study provides well-elucidated evidence to apply real-world data to the estimation of the occurrence and time point of major diabetes-related complications over a patient’s lifetime. Further applications in health decision science are encouraged. Full article
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