Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.6
2.6
Journals
Medicina
Special Issues
AI and Big Data in Healthcare
share announcement

AI and Big Data in Healthcare

A special issue of Medicina (ISSN 1648-9144).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2725

Special Issue Editors

Prof. Dr. Miguel Mascarenhas

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: inflammatory bowel disease; applied artificial intelligence; capsule endoscopy; neurogastroenterology; coloproctology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guilherme Macedo

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: gastroenterology; hepatology; liver transplantation; hepatocellular carcinoma; gastrointestinal diseases; biliary tract diseases; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence has subtly and progressively been integrated into our daily life. The applicability of heuristic algorithms revolutionized how we manage extensive databases and AI, providing an adequate framework for systematic analysis, promptly revealing its enormous potential in healthcare.

The excitement of a plausible application of innovative systems in our practice generated overwhelming enthusiasm in our community, and clinicians rapidly dove into a new lexicon, such as convolutional neural network models, deep learning methods, training machines, computer-aided detection systems, etc. Soon, we all realized that cross-pollination research with biomedical engineers, informaticians, and clinicians was more than an episodic drift of our mindset but an indispensable move towards a new advancing frontier.

In this Special Issue, we aim to showcase the state of the art of AI in multiple fields of healthcare, with examples of cutting-edge research being carried out in this field.

You may choose our Joint Special Issue in Diagnostics.

Dr. Miguel Mascarenhas Saraiva
Prof. Dr. Guilherme Macedo
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. Medicina is an international peer-reviewed open access monthly 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 1800 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
  • big data
  • healthcare
  • convolutional neural networks
  • precision medicine

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

10 pages, 554 KiB  
Article
Pituitary-Related Adverse Events and Onset Patterns Caused by Immune Checkpoint Inhibitors: Analysis Using the Japanese Adverse Drug Event Report Database
by Hiroki Asano, Yoshihiro Noguchi, Michio Kimura, Eiseki Usami and Tomoaki Yoshimura
Medicina 2023, 59(11), 1963; https://doi.org/10.3390/medicina59111963 - 7 Nov 2023
Cited by 1 | Viewed by 1092
Abstract
Background and Objectives: One type of immune-related adverse event caused by immune checkpoint inhibitors (ICIs) is pituitary-related adverse events. The management of pituitary-related adverse events is important because they can be fatal if not treated promptly. Therefore, this study was conducted to [...] Read more.
Background and Objectives: One type of immune-related adverse event caused by immune checkpoint inhibitors (ICIs) is pituitary-related adverse events. The management of pituitary-related adverse events is important because they can be fatal if not treated promptly. Therefore, this study was conducted to investigate the onset of pituitary-related adverse events using the Japanese Adverse Drug Report (JADER) database. Materials and Methods: Cases registered in the JADER database from 2004 to 2019 were used. The target drugs were ipilimumab, nivolumab, pembrolizumab, avelumab, atezolizumab, and durvalumab, and the target adverse events were the high-level terms “Anterior pituitary hypofunction,” “Anterior pituitary hyperfunction,” “Posterior pituitary disorder,” and “Pituitary neoplasm” in the Medical Dictionary for Regulatory Activities, Japanese version (MedDRA/J). The information component (IC) was used for signal detection and IC delta (ICΔ) was used for women-related signals. Onset timing and patterns were analyzed using the Weibull distribution. Results: Signals were detected with ipilimumab, nivolumab, pembrolizumab, and atezolizumab in “Anterior pituitary hypofunction,” with ICs and 95% credible intervals (95%CrI) of 5.53 (5.30–5.69), 4.96 (4.79–5.08), 4.04 (3.76–4.25), and 2.40 (1.53–3.00). Significant signals were detected in women, except for atezolizumab. Additionally, the time of onset was classified as the wear-out failure type. Inverse signals were detected with ipilimumab and nivolumab in “Posterior pituitary disorder,” with ICs (95%CrI) of −1.24 (−2.80–−0.26), and −0.89 (−1.64–−0.37). Conclusions: Anterior pituitary hypofunction is likely to occur with the long-term administration of ipilimumab, nivolumab, and pembrolizumab. Further investigation is needed to determine the differences in the tendencies to detect signals in the anterior and posterior pituitaries between ipilimumab and nivolumab. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
►▼ Show Figures

Figure 1

16 pages, 6711 KiB  
Article
Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network
by Febriyanti Panjaitan, Siti Nurmaini and Radiyati Umi Partan
Medicina 2023, 59(8), 1394; https://doi.org/10.3390/medicina59081394 - 29 Jul 2023
Cited by 2 | Viewed by 1393
Abstract
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing [...] Read more.
Sudden cardiac death (SCD) is a significant global health issue that affects individuals with and without a history of heart disease. Early identification of SCD risk factors is crucial in reducing mortality rates. This study aims to utilize electrocardiogram (ECG) tools, specifically focusing on heart rate variability (HRV), to detect early SCD risk factors. In this study, we expand the comparison group dataset to include five groups: Normal Sinus Rhythm (NSR), coronary artery disease (CAD), Congestive Heart Failure (CHF), Ventricular Tachycardia (VT), and SCD. ECG signals were recorded for 30 min and segmented into 5 min intervals, following the recommended HRV feature analysis guidelines. We introduce an innovative approach to HRV signal analysis by utilizing Convolutional Neural Networks (CNN). The CNN model was optimized by tuning hyperparameters such as the number of layers, learning rate, and batch size, significantly impacting the prediction accuracy. The findings demonstrate that the HRV approach, in conjunction with linear features and the DL method, achieved a higher accuracy rate, averaging 99.30%, reaching 97% sensitivity, 99.60% specificity, and 97.87% precision. Future research should focus on further exploring and refining DL methods in the context of HRV analysis to improve SCD prediction. Full article
(This article belongs to the Special Issue AI and Big Data in Healthcare)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop