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Future Internet
Special Issues
Curative Power of Medical Data 2020
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Curative Power of Medical Data 2020

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 13312

Special Issue Editors

Dr. Kevin Cohen

E-Mail Website
Guest Editor
University of Colorado School of Medicine, Aurora, CO, USA
Interests: spinal cord injury and regeneration; analysis of the speech of suicidal individuals; temporality in health records; information extraction from epilepsy clinic notes
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Gifu

E-Mail Website
Guest Editor
1. Faculty of Computer Science, "Alexandru Ioan Cuza" University of Iași, 700057 Iaşi, Romania
2. Institute of Computer Science, Romanian Academy - Iași branch, 700481 Iaşi, Romania
Interests: Web of Linked Data; Machine Learning; Semantic Annotation; Text Categorization; Content Analysis; Sentiment Analysis in Social Media; Discourse Analysis; Word Sense Disambiguation
Dr. Youzhu Li

E-Mail
Guest Editor
College of Public Administration, Huazhong Agricultural University, Wuhan, Hongshan district, Hubei 430070, China
Interests: data mining of social media
Dr. Jingbo Xia

E-Mail Website
Guest Editor
Department of Big data science, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China
Interests: BioNLP; data mining; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Ripple

E-Mail Website
Guest Editor
Cognitive Sciences Branch, Lister Hill National Center for Biomedical Communications, U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
Interests: pharmacovigilance; data mining; database searching; NLP

Special Issue Information

Dear Colleagues,

In an era when massive amounts of medical data became available, researchers working in biological, biomedical and clinical domains have increasingly started to require the help of language engineers to process large quantities of biomedical and  molecular biology literature (such as PubMed), patient data, or health records. Linking the contents of these documents to each other, as well as to specialized ontologies, could enable access to and discovery of structured clinical information and foster a major leap in natural language processing and health research.

MEDA-2020 aims to gather innovative approaches for the exploitation of biomedical data using semantic web technologies and linked data by bringing together practitioners, researchers, and scholars to share examples, use cases, theories, and analysis of biomedical data. The main objective of this second edition workshop is to consolidate an internationally appreciated forum for scientific research in BioMed, with emphasis on crowdsourcing, semantic web, knowledge integration, and data linking.

Topical Outline of the Workshop: The scientific program of MEDA-2020 will focus around the following topics:

  • Natural language processing/text mining
  • Data science/applied mathematics
  • Drug related knowledge discovery
  • Genomic assays
  • Reproducibility

Dr. Kevin Cohen
Dr. Daniela Gifu
Dr. Youzhu Li
Dr. Jingbo Xia
Dr. Anna Ripple
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. Future Internet 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 1600 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

  • Medical NLP
  • Bio NLP
  • Text mining
  • Natural language processing
  • Data science
  • Clinical Curation
  • Drug knowledge discovery

Published Papers (3 papers)

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Research

13 pages, 713 KiB  
Article
Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature
by Xiaolin Zhang and Chao Che
Future Internet 2021, 13(1), 14; https://doi.org/10.3390/fi13010014 - 8 Jan 2021
Cited by 17 | Viewed by 4147
Abstract
The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a [...] Read more.
The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods. Full article
(This article belongs to the Special Issue Curative Power of Medical Data 2020)
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10 pages, 1808 KiB  
Article
Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism
by Mingxuan Che, Kui Yao, Chao Che, Zhangwei Cao and Fanchen Kong
Future Internet 2021, 13(1), 13; https://doi.org/10.3390/fi13010013 - 7 Jan 2021
Cited by 18 | Viewed by 6154
Abstract
The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, [...] Read more.
The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19. Full article
(This article belongs to the Special Issue Curative Power of Medical Data 2020)
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11 pages, 833 KiB  
Article
IgA Nephropathy Prediction in Children with Machine Learning Algorithms
by Ping Zhang, Rongqin Wang and Nianfeng Shi
Future Internet 2020, 12(12), 230; https://doi.org/10.3390/fi12120230 - 17 Dec 2020
Cited by 4 | Viewed by 2275
Abstract
Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records [...] Read more.
Immunoglobulin A nephropathy (IgAN) is the most common primary glomerular disease all over the world and it is a major cause of renal failure. IgAN prediction in children with machine learning algorithms has been rarely studied. We retrospectively analyzed the electronic medical records from the Nanjing Eastern War Zone Hospital, chose eXtreme Gradient Boosting (XGBoost), random forest (RF), CatBoost, support vector machines (SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM) models in order to predict the probability that the patient would not reach or reach end-stage renal disease (ESRD) within five years, used the chi-square test to select the most relevant 16 features as the input of the model, and designed a decision-making system (DMS) of IgAN prediction in children that is based on XGBoost and Django framework. The receiver operating characteristic (ROC) curve was used in order to evaluate the performance of the models and XGBoost had the best performance by comparison. The AUC value, accuracy, precision, recall, and f1-score of XGBoost were 85.11%, 78.60%, 75.96%, 76.70%, and 76.33%, respectively. The XGBoost model is useful for physicians and pediatric patients in providing predictions regarding IgAN. As an advantage, a DMS can be designed based on the XGBoost model to assist a physician to effectively treat IgAN in children for preventing deterioration. Full article
(This article belongs to the Special Issue Curative Power of Medical Data 2020)
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