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Artificial Intelligence and Data Integration in Precision Health
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Artificial Intelligence and Data Integration in Precision Health

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Evidence Based Medicine".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 5580

Special Issue Editors

Dr. Yi Guo

E-Mail Website
Guest Editor
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, USA
Interests: electronic health records; data science; biostatistics; patient-reported outcomes
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe He

E-Mail Website
Guest Editor
Department of Behavioral Sciences and Social Medicine in the College of Medicine, Florida State University, Tallahassee, FL, USA
Interests: biomedical and health informatics; clinical research informatics; ontology-based data analytics
Dr. Xing He

E-Mail Website
Guest Editor
Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
Interests: artificial intelligence; biomedical informatics

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) techniques, including machine learning and deep learning methods, have been widely used for health surveillance, health risk and outcome prediction, medical diagnostics and therapeutics, clinical decision-making, and many more. However, developing AI models in the era of precision health requires the integration and examination of a comprehensive list of health determinants including genetic, biological, environmental, and social/behavioral factors from heterogeneous sources for prevention, diagnosis, and treatment. This Special Issue of the Journal of Personalized Medicine aims to highlight the development or application of AI methods through integrating heterogeneous data in precision (public) health research. Studies in, but not limited to, the following areas are welcomed: (1) novel methods for measuring, integrating, and analyzing determinants of health including genetic, biological, environmental, and social/behavioral factors; and (2) novel analyses of health outcomes through linking determinants of health data from heterogenous data sources.

Dr. Yi Guo
Dr. Zhe He
Dr. Xing He
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

  • machine learning 
  • deep learning 
  • natural language processing 
  • external exposome 
  • genomics 
  • real-world data 
  • social determinants of health

Published Papers (4 papers)

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Research

11 pages, 874 KiB  
Article
Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy
by Donato Romano, Pierfrancesco Novielli, Domenico Diacono, Roberto Cilli, Ester Pantaleo, Nicola Amoroso, Loredana Bellantuono, Alfonso Monaco, Roberto Bellotti and Sabina Tangaro
J. Pers. Med. 2024, 14(4), 430; https://doi.org/10.3390/jpm14040430 - 18 Apr 2024
Viewed by 521
Abstract
Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for [...] Read more.
Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Integration in Precision Health)
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10 pages, 4622 KiB  
Article
Automated Retrieval of Heterogeneous Proteomic Data for Machine Learning
by Abdul Rafay, Muzzamil Aziz, Amjad Zia and Abdul R. Asif
J. Pers. Med. 2023, 13(5), 790; https://doi.org/10.3390/jpm13050790 - 02 May 2023
Viewed by 1225
Abstract
Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a [...] Read more.
Proteomics instrumentation and the corresponding bioinformatics tools have evolved at a rapid pace in the last 20 years, whereas the exploitation of deep learning techniques in proteomics is on the horizon. The ability to revisit proteomics raw data, in particular, could be a valuable resource for machine learning applications seeking new insight into protein expression and functions of previously acquired data from different instruments under various lab conditions. We map publicly available proteomics repositories (such as ProteomeXchange) and relevant publications to extract MS/MS data to form one large database that contains the patient history and mass spectrometric data acquired for the patient sample. The extracted mapped dataset should enable the research to overcome the issues attached to the dispersions of proteomics data on the internet, which makes it difficult to apply emerging new bioinformatics tools and deep learning algorithms. The workflow proposed in this study enables a linked large dataset of heart-related proteomics data, which could be easily and efficiently applied to machine learning and deep learning algorithms for futuristic predictions of heart diseases and modeling. Data scraping and crawling offer a powerful tool to harvest and prepare the training and test datasets; however, the authors advocate caution because of ethical and legal issues, as well as the need to ensure the quality and accuracy of the data that are being collected. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Integration in Precision Health)
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0 pages, 5370 KiB  
Article
RETRACTED: Bidirectional Neural Network Model for Glaucoma Progression Prediction
by Hanan A. Hosni Mahmoud and Eatedal Alabdulkreem
J. Pers. Med. 2023, 13(3), 390; https://doi.org/10.3390/jpm13030390 - 23 Feb 2023
Cited by 2 | Viewed by 1388 | Retraction
Abstract
Deep learning models are usually utilized to learn from spatial data, only a few studies are proposed to predict glaucoma time progression utilizing deep learning models. In this article, we present a bidirectional recurrent deep learning model (Bi-RM) to detect prospective progressive visual [...] Read more.
Deep learning models are usually utilized to learn from spatial data, only a few studies are proposed to predict glaucoma time progression utilizing deep learning models. In this article, we present a bidirectional recurrent deep learning model (Bi-RM) to detect prospective progressive visual field diagnoses. A dataset of 5413 different eyes from 3321 samples is utilized as the learning phase dataset and 1272 eyes are used for testing. Five consecutive diagnoses are recorded from the dataset as input and the sixth progressive visual field diagnosis is matched with the prediction of the Bi-RM. The precision metrics of the Bi-RM are validated in association with the linear regression algorithm (LR) and term memory (TM) technique. The total prediction error of the Bi-RM is significantly less than those of LR and TM. In the class prediction, Bi-RM depicts the least prediction error in all three methods in most of the testing cases. In addition, Bi-RM is not impacted by the reliability keys and the glaucoma degree. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Integration in Precision Health)
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13 pages, 1493 KiB  
Article
A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients
by Jong Eun Lee, Luu Ngoc Do, Won Gi Jeong, Hyo Jae Lee, Kum Ju Chae, Yun Hyeon Kim and Ilwoo Park
J. Pers. Med. 2022, 12(11), 1859; https://doi.org/10.3390/jpm12111859 - 07 Nov 2022
Cited by 1 | Viewed by 1519
Abstract
Purpose: This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN). Materials and Methods: In a retrospective study, 239 [...] Read more.
Purpose: This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN). Materials and Methods: In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM. Results: The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features. Conclusion: Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Integration in Precision Health)
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