Role and Challenges of Unstructured Big Data in Healthcare

Adnan, K; Akbar, Rehan; Khor, Siak Wang; Ali, Adnan Bin Amanat

doi:10.1007/978-981-32-9949-8_22

Cited by 40 publications

(34 citation statements)

References 88 publications

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“…However, none of the publications offers a comprehensive overview of all possible challenges as well as opportunities for a particular scenario in terms of volume, velocity, variety and veracity. Case studies in customer-oriented businesses [54,62], the tactical domain [57], healthcare [61,64] and transportation [55] differ in their proposals on how software solutions have to be developed to meet predictable and unpredictable future requirements, in order for the analysis of the data to fulfil the meaning of an additional big data characteristic, the value [54].…”

Section: Unforeseeable Consequences Of Dynamic Business Environments mentioning

confidence: 99%

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

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Big data attracts researchers and practitioners around the globe in their desire to effectively manage the data deluge resulting from the ongoing evolution of the information systems domain. Consequently, many decision makers attempt to harness the potentials arising with the use of those modern technologies in a multitude of application scenarios. As a result, big data has gained an important role for many businesses. However, as of today, the developed solutions are oftentimes perceived as completed products, without considering that the application in highly dynamic environments might benefit from a deviation of this approach. Relevant data sources as well as the questions that are supposed to be answered by their analysis may change rapidly and so do subsequently the requirements regarding the functionalities of the system. To our knowledge, while big data itself is a prominent topic, fields of application that are likely to evolve in a short period of time and the resulting consequences were not specifically investigated until now. Therefore, this research aims to overcome this paucity by clarifying the relation between dynamic business environments and big data analytics (BDA), sensitizing researchers and practitioners for future big data engineering activities. Apart from a thorough literature review, expert interviews are conducted that evaluate the made inferences regarding dynamic and stable influencing factors, the influence of dynamic environments on BDA applications as well as possible countermeasures. The ascertained insights are condensed into a proposal for decision making, facilitating the alignment of BDA and business needs in dynamic business environments.

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Section: Unforeseeable Consequences Of Dynamic Business Environments mentioning

confidence: 99%

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Staegemann

Volk

Daase

et al. 2020

CSIMQ

View full text Add to dashboard Cite

show abstract

“…However, there are a number of domain-specific challenges encountered in human healthcare disciplines that are unique to these data frameworks. One example of this is the use of unstructured data, such as patient notes and interpretations of diagnostic tests, which contain rich information that can provide valuable insights at both the individual patient and population levels, but the heterogeneity, variability, and diversity of these data make them difficult to access if analyzed in a controlled manner [3]. Another challenge lies in the issues of privacy and security in human healthcare, which have drawn significant attention in recent years, but are especially important in healthcare settings because of concerns related to the introduction of the HIPAA Privacy Act, which declared medical information, including electronic medical records, to be protected health information covered under the Privacy Rule [4,5].…”

Section: Introductionmentioning

confidence: 99%

Veterinary Big Data: When Data Goes to the Dogs

Paynter

Dunbar

Creevy

et al. 2021

Animals

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Dogs provide an ideal model for study as they have the most phenotypic diversity and known naturally occurring diseases of all non-human land mammals. Thus, data related to dog health present many opportunities to discover insights into health and disease outcomes. Here, we describe several sources of veterinary medical big data that can be used in research. These sources include medical records from primary medical care centers or referral hospitals, medical claims data from animal insurance companies, and datasets constructed specifically for research purposes. No data source provides information that is without limitations, but large-scale, prospective, longitudinally collected data from dog populations are ideal for further research as they offer many advantages over other data sources.

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“…An obstacle to this objective is that most EMR data are unstructured free-text clinical notes without a standard format. This makes it challenging for the data to have a direct impact on clinical decisions (6)(7)(8)(9)(10)(11). Various studies have used unstructured data to assist in clinical decision-making based on deep-learning technology, such as natural language processing (NLP) (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival

et al. 2021

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Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based natural language processing model that analyzes serial magnetic resonance imaging reports of rectal cancer patients and predicts their overall survival. To evaluate the model, a retrospective cohort study of 4,338 rectal cancer patients was conducted. The experimental results revealed that the proposed model utilizing pre-trained clinical linguistic knowledge could predict the overall survival of patients without any structured information and was superior to the carcinoembryonic antigen in predicting survival. The deep-transfer-learning model using free-text radiological reports can predict the survival of patients with rectal cancer, thereby increasing the utility of unstructured medical big data.

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Role and Challenges of Unstructured Big Data in Healthcare

Cited by 40 publications

References 88 publications

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Discussing Relations Between Dynamic Business Environments and Big Data Analytics

Veterinary Big Data: When Data Goes to the Dogs

Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival

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