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Big Data Analysis in Personalized Medicine
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Big Data Analysis in Personalized 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 (20 April 2022) | Viewed by 7288

Special Issue Editor

Prof. Dr. Dagmar Waltemath

Website
Guest Editor
Medical Informatics Laboratory, University Medicine Greifswald, Greifswald, Germany
Interests: big data; medical informatics

Special Issue Information

Dear Colleagues,

The increase in healthcare and biomedical data generated over the past decades underlines the undeniable significance of Big Data analysis.

Studies have indicated the substantial role of Big Data analysis in advancing personalized medicine in terms of drug development, patient treatment, and overall quality of life. It is expected that in the near future, healthcare will progressively shift from a ‘one size fit all’ approach to a targeted person-centered healthcare (also termed precision medicine) in which medical decisions are tailored to the patient’s specific genomic, demographic, lifestyle, personal, and other relevant clinical information.

To date, risk predictions, early diagnosis, and treatments are mostly realized by understanding patterns and complex relations in both structured and unstructured Big Data. Recently, new methods for big data analysis became available, and the FAIRification of medical data has gained more attention.

The aim of this Special Issue is to highlight the role and application of Big Data analysis for personalized medicine. We invite authors to submit their original research and review articles focusing on application and relevance of Big Data analysis in both genomic and routine health data for improving patient level healthcare.

Potential topics include, but are not limited to:

  • novel algorithms for big data analysis;
  • applications of Big Data approaches;
  • evaluation of Big Data approaches;
  • patient level predictions using Big Data approaches;
  • data FAIRification and quality evaluation for Big Data Analysis. 

Prof. Dr. Dagmar Waltemath
Guest Editor

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.

Published Papers (3 papers)

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Research

12 pages, 613 KiB  
Article
The Impact of the Association between Cancer and Diabetes Mellitus on Mortality
by Sung-Soo Kim and Hun-Sung Kim
J. Pers. Med. 2022, 12(7), 1099; https://doi.org/10.3390/jpm12071099 - 1 Jul 2022
Viewed by 1740
Abstract
The prevalence of cancer, diabetes mellitus (DM), and hypertension is increasing in ageing populations. We analyzed the association of DM with cancer and its effects on cancer mortality. The data of 2009–2018 from the Korea National Hospital Discharge In-depth Injury Survey were used; [...] Read more.
The prevalence of cancer, diabetes mellitus (DM), and hypertension is increasing in ageing populations. We analyzed the association of DM with cancer and its effects on cancer mortality. The data of 2009–2018 from the Korea National Hospital Discharge In-depth Injury Survey were used; 169,959 adults with cancer as the main diagnosis were identified. The association rule for unsupervised machine learning was used. Association rule mining was used to analyze the association between the diseases. Logistic regression was performed to determine the effects of DM on cancer mortality. DM prevalence was 12.9%. Cancers with high DM prevalence were pancreatic (29.9%), bile duct (22.7%), liver (21.4%), gallbladder (15.5%), and lung cancers (15.4%). Cancers with high hypertension prevalence were bile duct (31.4%), ureter (30.5%), kidney (29.5%), pancreatic (28.1%), and bladder cancers (27.5%). The bidirectional association between DM and hypertension in cancer was the strongest (lift = 2.629, interest support [IS] scale = 0.426), followed by that between lung cancer and hypertension (lift = 1.280, IS scale = 0.204), liver cancer and DM (lift = 1.658, IS scale = 0.204), hypertension and liver cancer and DM (lift = 3.363, IS scale = 0.197), colorectal cancer and hypertension (lift = 1.133, IS scale = 0.180), and gastric cancer and hypertension (lift = 1.072, IS scale = 0.175). DM increased liver cancer mortality (p = 0.000), while hypertension significantly increased the mortality rate of stomach, colorectal, liver, and lung cancers. Our study confirmed the association between cancer and DM. Consequently, a patient management strategy with presumptive diagnostic ability for DM and hypertension is required to decrease cancer mortality rates. Full article
(This article belongs to the Special Issue Big Data Analysis in Personalized Medicine)
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16 pages, 901 KiB  
Article
Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis
by Yung-Chuan Huang, Yu-Chen Cheng, Mao-Jhen Jhou, Mingchih Chen and Chi-Jie Lu
J. Pers. Med. 2022, 12(5), 756; https://doi.org/10.3390/jpm12050756 - 6 May 2022
Cited by 12 | Viewed by 3110
Abstract
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term [...] Read more.
Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques—naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)—were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine. Full article
(This article belongs to the Special Issue Big Data Analysis in Personalized Medicine)
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12 pages, 823 KiB  
Article
Newly Diagnosed Type 2 Diabetes Care between Family Physicians, Endocrinologists, and Other Internists in Taiwan: A Retrospective Population-Based Cohort Study
by Pei-Lin Chou, I-Hui Chiang, Chi-Wei Lin, His-Hao Wang, Hao-Kuang Wang, Chi-Hsien Huang, Chao-Sung Chang, Ru-Yi Huang and Chung-Ying Lin
J. Pers. Med. 2022, 12(3), 461; https://doi.org/10.3390/jpm12030461 - 14 Mar 2022
Cited by 1 | Viewed by 1718
Abstract
(1) Background: We aimed to determine whether physicians of different specialties perform differently in the monitoring, cost control, and prevention of acute outcomes in diabetes care. (2) Methods: Using data from the Health and Welfare Data Science Center, participants with newly diagnosed type [...] Read more.
(1) Background: We aimed to determine whether physicians of different specialties perform differently in the monitoring, cost control, and prevention of acute outcomes in diabetes care. (2) Methods: Using data from the Health and Welfare Data Science Center, participants with newly diagnosed type 2 diabetes (n = 206,819) were classified into three cohorts based on their primary care physician during the first year of diagnosis: family medicine (FM), endocrinologist, and other internal medicine (IM). The three cohorts were matched in a pairwise manner (FM (n = 28,269) vs. IM (n = 28,269); FM (n = 23,407) vs. endocrinologist (n = 23,407); IM (n = 43,693) vs. endocrinologist (n = 43,693)) and evaluated for process indicators, expenditure on diabetes care, and incidence of acute complications (using subdistribution hazard ratio; sHR). (3) Results: Compared to the FM cohort, both the IM (sHR, 1.26; 95% CI, 1.08 to 1.47) and endocrinologist cohorts (sHR, 1.57; 95% CI, 1.38–1.78) had higher incidences of acute complications. The FM cohort incurred lower costs than the IM cohort (USD 487.41 vs. USD 507.67, p = 0.01) and expended less than half of the diabetes-related costs of the endocrinology cohort (USD 484.39 vs. USD 927.85, p < 0.001). (4) Conclusion: Family physicians may provide better care at a lower cost to newly diagnosed type 2 diabetes patients. Relatively higher costs incurred by other internists and endocrinologists in the process of diabetes care may be explained by the more frequent ordering of specialized tests. Full article
(This article belongs to the Special Issue Big Data Analysis in Personalized Medicine)
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