Special Issue "Big Data Evaluation and Non-Relational Databases in eHealth"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (31 October 2019).

Special Issue Editor

Dr. Giorgio Maria Di Nunzio
Website
Guest Editor
Department of Information Engineering, University of Padua, Italy
Interests: Medical Information Retrieval; Interactive Machine Learning; Computational Terminology

Special Issue Information

Dear Colleagues,

The efficiency and effectiveness of applications that manage very large datasets have become critical in many domains. In the eHealth field, for example, there are many open medical datasets that can be deployed to design and implement systems that support the decisions of physicians in critical situations. Nowadays, there are Big Data available for medical electronic publications (such as PubMed), electronic health records (such as MIMIC), and tools to bring together multiple biomedical vocabularies to enable interoperability (such as the Unified Medical Language System). All these data need proper management as well as procedures for the reproducibility of the experiments that use these datasets. In this Special Issue, we want to evaluate non-relational approaches to these questions, and we propose the following topics of interests.

Dr. Giorgio Maria Di Nunzio
Guest Editor

Manuscript Submission Information

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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. Information is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Comparison of relational vs non-relational data management in the eHealth
  • Non-relational approaches to data citation
  • Evaluation of medical systematic reviews
  • Continuous active learning in eHealth
  • Classification and retrieval of electronic health records (HER)
  • Semantic enrichment of medical data
  • Query rewriting in medical information retrieval
  • Reproducibility in eHealth using non-relational approaches

Published Papers (2 papers)

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Research

Open AccessArticle
A Study on Ranking Fusion Approaches for the Retrieval of Medical Publications
Information 2020, 11(2), 103; https://doi.org/10.3390/info11020103 - 14 Feb 2020
Abstract
In this work, we compare and analyze a variety of approaches in the task of medical publication retrieval and, in particular, for the Technology Assisted Review (TAR) task. This problem consists in the process of collecting articles that summarize all evidence that has [...] Read more.
In this work, we compare and analyze a variety of approaches in the task of medical publication retrieval and, in particular, for the Technology Assisted Review (TAR) task. This problem consists in the process of collecting articles that summarize all evidence that has been published regarding a certain medical topic. This task requires long search sessions by experts in the field of medicine. For this reason, semi-automatic approaches are essential for supporting these types of searches when the amount of data exceeds the limits of users. In this paper, we use state-of-the-art models and weighting schemes with different types of preprocessing as well as query expansion (QE) and relevance feedback (RF) approaches in order to study the best combination for this particular task. We also tested word embeddings representation of documents and queries in addition to three different ranking fusion approaches to see if the merged runs perform better than the single models. In order to make our results reproducible, we have used the collection provided by the Conference and Labs Evaluation Forum (CLEF) eHealth tasks. Query expansion and relevance feedback greatly improve the performance while the fusion of different rankings does not perform well in this task. The statistical analysis showed that, in general, the performance of the system does not depend much on the type of text preprocessing but on which weighting scheme is applied. Full article
(This article belongs to the Special Issue Big Data Evaluation and Non-Relational Databases in eHealth)
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Open AccessArticle
A Big Data Analytics Approach for the Development of Advanced Cardiology Applications
Information 2020, 11(2), 60; https://doi.org/10.3390/info11020060 - 23 Jan 2020
Cited by 1
Abstract
Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in [...] Read more.
Nowadays, we are observing a growing interest about Big Data applications in different healthcare sectors. One of this is definitely cardiology. In fact, electrocardiogram produces a huge amount of data about the heart health status that need to be stored and analysed in order to detect a possible issues. In this paper, we focus on the arrhythmia detection problem. Specifically, our objective is to address the problem of distributed processing considering big data generated by electrocardiogram (ECG) signals in order to carry out pre-processing analysis. Specifically, an algorithm for the identification of heartbeats and arrhythmias is proposed. Such an algorithm is designed in order to carry out distributed processing over the Cloud since big data could represent the bottleneck for cardiology applications. In particular, we implemented the Menard algorithm in Apache Spark in order to process big data coming form ECG signals in order to identify arrhythmias. Experiments conducted using a dataset provided by the Physionet.org European ST-T Database show an improvement in terms of response times. As highlighted by our outcomes, our solution provides a scalable and reliable system, which may address the challenges raised by big data in healthcare. Full article
(This article belongs to the Special Issue Big Data Evaluation and Non-Relational Databases in eHealth)
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