Life | Special Issue : AI and Precision Medicine: Using Machine Learning for Disease Diagnosis and Prediction

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AI and Precision Medicine: Using Machine Learning for Disease Diagnosis and Prediction

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Special Issue Editors

Dr. Vasilis Nikolaou

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Guest Editor

Business School, University of Surrey, Guildford, UK
Interests: medical statistics; machine learning; deep learning; diagnostics; respiratory diseases; cardiovascular diseases

Dr. Sarantis Chlamydas

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Guest Editor

Scientific Director-MultiOmics, Olink Proteomics AB, Uppsala, Sweden
Interests: epigenetics; biomarkers; drug discovery; multiomics

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has revolutionized the healthcare industry, and precision medicine is at the forefront of this transformation. In this Special Issue, we aim to explore the intersection of AI and precision medicine, with a specific emphasis on machine learning approaches.

We invite researchers, academics and experts in the fields of AI and precision medicine to submit their original research articles, reviews, and perspectives on the following topics:

Early disease detection: Innovations in AI and machine learning for the early diagnosis of diseases such as cancer, cardiovascular conditions, and neurological disorders.
Risk prediction: Predictive models utilizing patient data, genetics, and environmental factors to assess an individual’s risk of developing specific diseases.
Personalized treatment: Approaches to tailoring medical treatments based on patient’s unique genetic profile, lifestyle, and clinical history.
Diagnostic imaging: Advancements in AI for analyzing medical imaging data to enhance diagnostic accuracy.
Genomic analysis: Research focusing on machine learning applications in genomics, transcriptomics, and proteomics for understanding disease mechanisms.
Clinical Decision Support: Applications of AI as clinical decision support tools for healthcare professionals in diagnosis and treatment planning.

The Special Issue aims to provide a platform for the exchange of ideas, discussion, and dissemination of cutting-edge research in this area.

Dr. Vasilis Nikolaou
Dr. Sarantis Chlamydas
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. Life 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 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

AI
machine learning
cancer
cardiovascular diseases
neurological disorders
genomics
diagnosis
prognosis
treatment response
disease-risk prediction

Published Papers (1 paper)

Research

14 pages, 758 KiB

Open AccessArticle

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

by Alex Mirugwe, Clare Ashaba, Alice Namale, Evelyn Akello, Edward Bichetero, Edgar Kansiime and Juwa Nyirenda

Life 2024, 14(6), 708; https://doi.org/10.3390/life14060708 (registering DOI) - 30 May 2024

Viewed by 72

Abstract

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated much attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyse the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95%. These findings confirm the reported effectiveness of Transformer-based architectures in tasks related to natural language processing, such as sentiment analysis. Full article

(This article belongs to the Special Issue AI and Precision Medicine: Using Machine Learning for Disease Diagnosis and Prediction)

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