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

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 4472

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

E-Mail Website
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:

  1. Early disease detection: Innovations in AI and machine learning for the early diagnosis of diseases such as cancer, cardiovascular conditions, and neurological disorders.
  2. Risk prediction: Predictive models utilizing patient data, genetics, and environmental factors to assess an individual’s risk of developing specific diseases.
  3. Personalized treatment: Approaches to tailoring medical treatments based on patient’s unique genetic profile, lifestyle, and clinical history.
  4. Diagnostic imaging: Advancements in AI for analyzing medical imaging data to enhance diagnostic accuracy.
  5. Genomic analysis: Research focusing on machine learning applications in genomics, transcriptomics, and proteomics for understanding disease mechanisms.
  6. 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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 2508 KiB  
Article
Machine Learning-Based Radiomics Analysis for Identifying KRAS Mutations in Non-Small-Cell Lung Cancer from CT Images: Challenges, Insights and Implications
by Mirjam Schöneck, Nicolas Rehbach, Lars Lotter-Becker, Thorsten Persigehl, Simon Lennartz and Liliana Lourenco Caldeira
Life 2025, 15(1), 83; https://doi.org/10.3390/life15010083 - 11 Jan 2025
Viewed by 861
Abstract
Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT [...] Read more.
Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT images to identify KRAS mutations in NSCLC patients. Both datasets were analyzed using parametric (t test) and non-parametric statistical tests (Mann–Whitney U test) and dimensionality reduction techniques. Afterwards, the proposed ML pipeline was applied to both datasets using a five-fold cross-validation on the training set (70/30 train/test split) before being validated on the other dataset. The results show that the radiomic features are significantly different (Mann–Whitney U test; p < 0.05) between the two datasets, despite the use of identical feature extraction methods. Model transferability is therefore difficult to achieve, which became evident during external testing (F1 score = 0.41). Oversampling, undersampling, clustering and harmonization techniques were applied to balance and harmonize the datasets, but did not improve the classification of KRAS mutation presence. In general, due to only a single moderate result (highest test F1 score = 0.67), the accuracy of KRAS prediction is not sufficient for clinical application. In future work, the complexity of KRAS mutation might be addressed by taking submutations into consideration. Larger multicentric datasets with balanced tumor stages, including multi-scanner datasets, seem to be necessary for building robust predictive models. 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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14 pages, 4746 KiB  
Article
Differentiating Gliosarcoma from Glioblastoma: A Novel Approach Using PEACE and XGBoost to Deal with Datasets with Ultra-High Dimensional Confounders
by Amir Saki, Usef Faghihi and Ismaila Baldé
Life 2024, 14(7), 882; https://doi.org/10.3390/life14070882 - 16 Jul 2024
Cited by 2 | Viewed by 1296
Abstract
In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme [...] Read more.
In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. Our method provides both positive and negative versions of PEACE together with their averages to calculate the positive and negative causal effects of the radiomic features on the variable representing the type of tumor (GSM or GBM). In our model, PEACE and its variations are equipped with a degree d which varies from 0 to 1 and it reflects the importance of the rarity and frequency of the events. By using PEACE with XGBoost, we achieved a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM. To assess the XGBoost model, we used cross-validation and obtained a mean accuracy of 83% and an average model MSE of 0.130. This performance is notable given the high number of columns and low number of rows (code on GitHub). 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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14 pages, 758 KiB  
Article
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 - 30 May 2024
Cited by 3 | Viewed by 1251
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, [...] Read more.
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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