Big Data and Applications of Machine Learning in Medicine

A special issue of Medical Sciences (ISSN 2076-3271).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 4829

Special Issue Editors


E-Mail Website
Guest Editor
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Interests: artificial Intelligence; machine learning; meta-analysis; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Interests: artificial intelligence; machine Learning; nephrology; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, machine learning and artificial intelligence have found an essential role in diverse areas of medicine and healthcare, helping clinicians enhance clinical care, including disease detection, diagnosis, risk, and outcome prediction, all helping to improve the quality of patient care.

In this Special Issue, we call on researchers and clinicians to submit their invaluable works, including original clinical research, database studies, meta-analyses, artificial intelligence research, and applications that will provide additional knowledge and skills in the field of medicine and healthcare.

Potential topics include, but are not limited to:

-Methodological, philosophical, ethical, and social issues of artificial intelligence in medicine and healthcare;

-Machine learning, deep learning, artificial intelligence, and data science in medicine;

-Artificial intelligence and machine learning for disease detection, diagnosis, and risk prediction;

-Computational intelligence in bio- and clinical medicine;

-Data analytics and mining for biomedical decision support;

-Intelligent data analysis in medicine and health care;

-Artificial intelligence-based clinical decision making;

-Intelligent medical devices and instruments;

-Natural language processing in medicine and healthcare;

-Automated reasoning and meta-reasoning in medicine;

-New computational platforms and models for biomedicine;

-Medical knowledge graphs and ontologies;

-Computational intelligence and machine learning in bioinformatics.

Dr. Wisit Cheungpasitporn
Dr. Charat Thongprayoon
Dr. Wisit Kaewput
Guest Editors

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. Medical Sciences is an international peer-reviewed open access quarterly 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 1400 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
  • deep learning
  • ontology
  • systematic review
  • meta-analysis
  • precision digital medicine
  • semantic technology
  • smart electronic health records
  • computerized clinical practice
  • biomedical imaging and signal processing
  • visual analytics
  • clinical decision support systems
  • patient data processing and management
  • data mining
  • natural language processing

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

12 pages, 1008 KiB  
Article
Antidepressants and Risk of Sudden Cardiac Death: A Network Meta-Analysis and Systematic Review
by Narut Prasitlumkum, Wisit Cheungpasitporn, Nithi Tokavanich, Kimberly R. Ding, Jakrin Kewcharoen, Charat Thongprayoon, Wisit Kaewput, Tarun Bathini, Saraschandra Vallabhajosyula and Ronpichai Chokesuwattanaskul
Med. Sci. 2021, 9(2), 26; https://doi.org/10.3390/medsci9020026 - 23 Apr 2021
Cited by 6 | Viewed by 3723
Abstract
Background: Antidepressants are one of the most prescribed medications, particularly for patients with mental disorders. Nevertheless, there are still limited data regarding the risk of ventricular arrhythmia (VA) and sudden cardiac death (SCD) associated with these medications. Thus, we performed systemic review [...] Read more.
Background: Antidepressants are one of the most prescribed medications, particularly for patients with mental disorders. Nevertheless, there are still limited data regarding the risk of ventricular arrhythmia (VA) and sudden cardiac death (SCD) associated with these medications. Thus, we performed systemic review and meta-analysis to characterize the risks of VA and SCD among patients who used common antidepressants. Methods: A literature search for studies that reported risk of ventricular arrhythmias and sudden cardiac death in antidepressant use from MEDLINE, EMBASE, and Cochrane Database from inception through September 2020. A random-effects model network meta-analysis model was used to analyze the relation between antidepressants and VA/SCD. Surface Under Cumulative Ranking Curve (SUCRA) was used to rank the treatment for each outcome. Results: The mean study sample size was 355,158 subjects. Tricyclic antidepressant (TCA) patients were the least likely to develop ventricular arrhythmia events/sudden cardiac deaths at OR 0.24, 0.028–1.2, OR 0.32 (95% CI 0.038–1.6) for serotonin and norepinephrine reuptake inhibitors (SNRI), and OR 0.36 (95% CI 0.043, 1.8) for selective serotonin reuptake inhibitors (SSRI), respectively. According to SUCRA analysis, TCA was on a higher rank compared to SNRI and SSRI considering the risk of VA/SCD. Conclusion: Our network meta-analysis demonstrated the low risk of VA/SCD among patients using antidepressants for SNRI, SSRI and especially, TCA. Despite the relatively lowest VA/SCD in TCA, drug efficacy and other adverse effects should be taken into account in patients with mental disorders. Full article
(This article belongs to the Special Issue Big Data and Applications of Machine Learning in Medicine)
Show Figures

Figure 1

Back to TopTop