Artificial Intelligence in Clinical Decision Support—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 28

Special Issue Editor


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Guest Editor
Montera Inc., San Francisco, CA 94104, USA
Interests: artificial intelligence; machine learning; clinical decision support; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence has been increasingly used in Clinical Decision Support (CDS) systems to aid healthcare professionals in making timely and informed diagnoses and treatment decisions. The use of artificial intelligence has the potential to revolutionize CDS by providing more accurate and efficient diagnoses and treatment, improving patient outcomes, and reducing costs.

This Special Issue welcomes original research and review articles on developing and validating artificial intelligence-based clinical decision support algorithms and systems for chronic and acute conditions in various clinical settings. Potential topics include, but are not limited to, the following:

  • Predictive modeling using Electronic Health Record (EHR) data;
  • Real-time patient monitoring and risk prediction;
  • Diagnostic support using comprehensive medical records, including imaging and waveform data;
  • Treatment or therapy recommendation for chronic conditions;
  • Clinical trial design and optimization;
  • Personalized medicine.

Dr. Qingqing Mao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • clinical decision support
  • predictive modeling
  • patient monitoring
  • diagnostic support
  • treatment recommendation
  • safety and privacy

Published Papers (1 paper)

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Research

21 pages, 2149 KiB  
Article
Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes
by Robert P. Adelson, Anurag Garikipati, Yunfan Zhou, Madalina Ciobanu, Ken Tawara, Gina Barnes, Navan Preet Singh, Qingqing Mao and Ritankar Das
Diagnostics 2024, 14(11), 1152; https://doi.org/10.3390/diagnostics14111152 - 31 May 2024
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D [...] Read more.
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support—2nd Edition)
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