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Applications of Machine Learning and Deep Learning in Precision Medicine
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Applications of Machine Learning and Deep Learning in Precision Medicine

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 12835

Special Issue Editors

Prof. Dr. Abdul Rahman Asif

E-Mail Website
Guest Editor
Clinical Proteomics & Artificial Intelligence Group (CPAIG), UMG Laboratories and Clinical Chemistry, Department of Clinical Chemistry, University Medical Center Gottingen, 37075 Gottingen, Germany
Interests: clinical proteomics; drug–protein interaction; toxicoproteomics; diagnostic proteomics; artificial Intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jan Jürjens

E-Mail Website
Guest Editor
1. Faculty of Computer Science, University of Koblenz-Landau, 56070 Koblenz, Germany
2. Fraunhofer Institute for Software and Systems Engineering ISST, 44227 Dortmund, Germany
Interests: artificial intelligence; software engineering; IT Security; deep learning

Special Issue Information

Dear Colleagues,

Humans are the most observed species on the globe and data acquired from humans over time is not only heterogeneous but similarly very enriched. Human medical mega datasets hold huge potential to feed algorithms that are trained to identify specific patterns and to operate automatically within use cases and given areas based on the training examples. This all could revolutionize precision medicine and bring clinical care to the next level.

The recent developments have shown immense trust and growing confidence in machine learning techniques to increase the quality of life in almost every field of life. Similarly, this is the case in health care and precision medicine. This Special Issue aims to report innovative algorithms and applications of Artificial intelligence, machine learning, and deep learning to achieve and enhance precision medicine. We welcome the experimental studies and ongoing research on the specialized techniques of data preparation, modelling, training, as well as their outcome in precision medicine. Similarly, some special concerns and challenges attached to the ML and DL with reference to the topic of medicine should be given extra attention and need to be addressed explicitly. For instance, the topics such as the heterogeneity of medical data, in sufficient quality of datasets, privacy, and anonymity concerns are centric to this Special Issue call.

The Special Issue will publish original research articles, short communications and reviews from (but not limited to) the following research areas: Clinical Artificial Intelligence, Machine learning in precision medicine, AI in Individual Medicine. AI in Personalized Medicine, Algorithm and Architectures supporting precision medicine.

Prof. Dr. Abdul Rahman Asif
Prof. Dr. Jan Jürjens
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. Journal of Personalized Medicine 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

  • algorithm development
  • artificial intelligence
  • medical big data analysis and modeling
  • deep learning
  • theory, algorithm, architectures
  • machine learning
  • theory, algorithm, architectures
  • natural language processing (NLP)
  • pattern recognition
  • privacy concerns and solutions in medicinal AI
  • precision medicine
  • individualized medicine

Published Papers (4 papers)

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Research

Jump to: Review

9 pages, 878 KiB  
Article
Classification of Central Venous Catheter Tip Position on Chest X-ray Using Artificial Intelligence
by Seungkyo Jung, Jaehoon Oh, Jongbin Ryu, Jihoon Kim, Juncheol Lee, Yongil Cho, Myeong Seong Yoon and Ji Young Jeong
J. Pers. Med. 2022, 12(10), 1637; https://doi.org/10.3390/jpm12101637 - 03 Oct 2022
Cited by 3 | Viewed by 2303
Abstract
Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study [...] Read more.
Recent studies utilizing deep convolutional neural networks (CNN) have described the central venous catheter (CVC) on chest radiography images. However, there have been no studies for the classification of the CVC tip position with a definite criterion on the chest radiograph. This study aimed to develop an algorithm for the automatic classification of proper depth with the application of automatic segmentation of the trachea and the CVC on chest radiographs using a deep CNN. This was a retrospective study that used plain chest supine anteroposterior radiographs. The trachea and CVC were segmented on images and three labels (shallow, proper, and deep position) were assigned based on the vertical distance between the tracheal carina and CVC tip. We used a two-stage approach model for the automatic segmentation of the trachea and CVC with U-net++ and automatic classification of CVC placement with EfficientNet B4. The primary outcome was a successful three-label classification through five-fold validations with segmented images and a test with segmentation-free images. Of a total of 808 images, 207 images were manually segmented and the overall accuracy of the five-fold validation for the classification of three-class labels (mean (SD)) of five-fold validation was 0.76 (0.03). In the test for classification with 601 segmentation-free images, the average accuracy, precision, recall, and F1-score were 0.82, 0.73, 0.73, and 0.73, respectively. We achieved the highest accuracy value of 0.91 in the shallow position label, while the highest F1-score was 0.82 in the deep position label. A deep CNN can achieve a comparative performance in the classification of the CVC position based on the distance from the carina to the CVC tip as well as automatic segmentation of the trachea and CVC on plain chest radiographs. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Precision Medicine)
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26 pages, 7548 KiB  
Article
Framework for Testing Robustness of Machine Learning-Based Classifiers
by Joshua Chuah, Uwe Kruger, Ge Wang, Pingkun Yan and Juergen Hahn
J. Pers. Med. 2022, 12(8), 1314; https://doi.org/10.3390/jpm12081314 - 14 Aug 2022
Cited by 4 | Viewed by 1820
Abstract
There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that [...] Read more.
There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers’ performance and changes in the classifiers’ parameter values using factor analysis and Monte Carlo simulations. Specifically, this work evaluates (1) the importance of a classifier’s input features and (2) the variability of a classifier’s output and model parameter values in response to data perturbations. Additionally, it was found that one can estimate a priori how much replacement noise a classifier can tolerate while still meeting accuracy goals. To illustrate the evaluation framework, six different AI/ML-based biomarkers are developed using commonly used techniques (linear discriminant analysis, support vector machines, random forest, partial-least squares discriminant analysis, logistic regression, and multilayer perceptron) for a metabolomics dataset involving 24 measured metabolites taken from 159 study participants. The framework was able to correctly predict which of the classifiers should be less robust than others without recomputing the classifiers itself, and this prediction was then validated in a detailed analysis. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Precision Medicine)
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Review

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18 pages, 7933 KiB  
Review
Predicting the Onset of Diabetes with Machine Learning Methods
by Chun-Yang Chou, Ding-Yang Hsu and Chun-Hung Chou
J. Pers. Med. 2023, 13(3), 406; https://doi.org/10.3390/jpm13030406 - 24 Feb 2023
Cited by 23 | Viewed by 4413
Abstract
The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that [...] Read more.
The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Precision Medicine)
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20 pages, 1584 KiB  
Review
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
by Heather D. Couture
J. Pers. Med. 2022, 12(12), 2022; https://doi.org/10.3390/jpm12122022 - 07 Dec 2022
Cited by 7 | Viewed by 3601
Abstract
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective [...] Read more.
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Precision Medicine)
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