Data Science in Healthcare: Benefits, Challenges and Opportunities

Abedjan, Ziawasch; Boujemaa, Nozha; Campbell, Stuart I.; Casla, Patricia; Chatterjea, Supriyo; Consoli, Sergio; Costa-Soria, Cristóbal; Czech, Paul; Despenic, Marija; Garattini, Chiara; Hamelinck, Dirk; Heinrich, Adrienne; Kraaij, Wessel; Kustra, Jacek; Lojo, Aizea; Sanchez, Marga Martin; Mayer, Miguel Angel; Melideo, Matteo; Menasalvas, Ernestina; Aarestrup, Frank Møller; Artigot, Elvira Narro; Petković, Milan; Recupero, Diego Reforgiato; González, Alejandro Rodríguez; Kerremans, Gisele Roesems; Roller, Roland; Rom�ão, Mário; Rüping, Stefan; Sasaki, Fumio; Spek, Wouter; Stojanović, Nenad; Thoms, Jack; Vasiļjevs, Andrejs; Verachtert, Wilfried; Wuyts, Roel

doi:10.1007/978-3-030-05249-2_1

Cited by 28 publications

(17 citation statements)

References 22 publications

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“…Aggregating these data over needs and services and analyzing the variances in CCPs enable managers to better determine met and unmet needs in their populations; make informed choices in supporting a diversified offering adapted to these needs; offer a continuum of clinical information using certain performance indicators in order to monitor the performance and continuous improvement of practices; foster the complementarity of services; and enter into appropriate agreements with public, private, and community partners. 46…”

Section: Discussionmentioning

confidence: 99%

Computerized Care-Pathways (CCPs) System to Support Person-Centered, Integrated, and Proactive Care in Home-Care Settings

Dubuc

Brière

Corbin

et al. 2021

Informatics for Health and Social Care

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Section: Discussionmentioning

confidence: 99%

Computerized Care-Pathways (CCPs) System to Support Person-Centered, Integrated, and Proactive Care in Home-Care Settings

Dubuc

Brière

Corbin

et al. 2021

Informatics for Health and Social Care

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“…It is released using dual licensing, as an open-source project, but with the option of a premium license with support. For more information, we would like to point the reader to our extensive documentation online, previous publications [1,2,7], or published use cases [8][9][10][11][12][13][14][15] in addition to this paper.…”

Section: Discussionmentioning

confidence: 99%

“…This creates additional complexity from the implementation side, leading us to develop multiple new algorithms [1,2,7]. From a user's perspective, however, it makes elPrep a drop-in replacement for other tools, resulting in its adoption by different bioinformatics projects [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Multithreaded variant calling in elPrep 5

et al. 2021

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We present elPrep 5, which updates the elPrep framework for processing sequencing alignment/map files with variant calling. elPrep 5 can now execute the full pipeline described by the GATK Best Practices for variant calling, which consists of PCR and optical duplicate marking, sorting by coordinate order, base quality score recalibration, and variant calling using the haplotype caller algorithm. elPrep 5 produces identical BAM and VCF output as GATK4 while significantly reducing the runtime by parallelizing and merging the execution of the pipeline steps. Our benchmarks show that elPrep 5 speeds up the runtime of the variant calling pipeline by a factor 8-16x on both whole-exome and whole-genome data while using the same hardware resources as GATK4. This makes elPrep 5 a suitable drop-in replacement for GATK4 when faster execution times are needed.

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“…The process of information extraction helps to prepare unstructured data for big data analytics [6]. Among the challenges of information extraction from unstructured big data, usability is one of the major concerns due to its diversity, sparsity, and heterogeneity issues [7]. The usage of unreliable data from diverse sources, valueless, and irrelevance of data may lead to unnecessary effort and cost for companies [8].…”

Section: Introductionmentioning

confidence: 99%

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

2021

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Unstructured text contains valuable information for a range of enterprise applications and informed decision making. Text analytics is used to extract valuable insights from unstructured big data. Among the most significant challenges of text analytics, quality and usability are critical affecting the outcome of the analytical process. The enhancement in usability is important for the exploitation of unstructured data. Most of the existing literature focuses on the usability of structured data as compared to unstructured data whereas big data usability has been discussed merely in context of its assessment. The existing approaches do not provide proper guidelines on usability enhancement of unstructured data. In this study, a rigorous systematic literature review, using PRISMA framework, has been conducted to develop a model enhancing the usability of unstructured data and bridging the research gap. The recent approaches and solutions for text analytics have been investigated thoroughly. Furthermore, it identifies the usability issues of unstructured text data and their consequences on data preparation for analytics. Defining the usability dimensions for unstructured big data, identification of the usability determinants, and developing a relationship between usability dimension and determinants to derive usability rules are the significant contribution of this research, and are integrated to formulate the model. The proposed usability enhancement model is the major outcome of the study. It would contribute to making unstructured data usable and facilitating the data preparation activities with more valuable data that eventually improves the analytical process.

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Data Science in Healthcare: Benefits, Challenges and Opportunities

Cited by 28 publications

References 22 publications

Computerized Care-Pathways (CCPs) System to Support Person-Centered, Integrated, and Proactive Care in Home-Care Settings

Computerized Care-Pathways (CCPs) System to Support Person-Centered, Integrated, and Proactive Care in Home-Care Settings

Multithreaded variant calling in elPrep 5

Development of Usability Enhancement Model for Unstructured Big Data Using SLR

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