Artificial Intelligence in Clinical Decision Support

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: 31 May 2024 | Viewed by 3060

Special Issue Editor

Montera Inc., San Francisco, CA 94104, USA
Interests: artificial intelligence; machine learning; clinical decision support; bioinformatics

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:

  • Predictive modeling using Electronic Health Records (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 (2 papers)

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Research

21 pages, 2877 KiB  
Article
Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer’s Disease
by Robert P. Adelson, Anurag Garikipati, Jenish Maharjan, Madalina Ciobanu, Gina Barnes, Navan Preet Singh, Frank A. Dinenno, Qingqing Mao and Ritankar Das
Diagnostics 2024, 14(1), 13; https://doi.org/10.3390/diagnostics14010013 (registering DOI) - 20 Dec 2023
Cited by 1 | Viewed by 1056
Abstract
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old (n [...] Read more.
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24–48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24–48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support)
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10 pages, 534 KiB  
Article
Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models
by Catalina Bennasar, Irene García, Yolanda Gonzalez-Cid, Francesc Pérez and Juan Jiménez
Diagnostics 2023, 13(17), 2742; https://doi.org/10.3390/diagnostics13172742 - 23 Aug 2023
Viewed by 1505
Abstract
Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since [...] Read more.
Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist’s treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis. Full article
(This article belongs to the Special Issue Artificial Intelligence in Clinical Decision Support)
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