Semantic-based Approach for Solving the Heterogeneity of Clinical Data

utilization of biological systems to develop new products at one end of the spectrum to the application of technology for solving the problems in biology at the other end. [1] According to the new experimental technologies like DNA nucleotide sequencing and RNA sequencing genomics, [2] Mass Spectrometry [3] as well as X-ray crystallography and Nuclear Magnetic Resonance spectroscopy [4] in proteomics, large datasets are generated and sorted in the field of biotechnology that make new opportunities for researchers along with companies that present services and products in this area. Nowadays, performing stateof-the-art research in biotechnology is roughly impossible without exploiting database and artificial intelligence (AI) technologies. [5] The integration of AI and biotechno logy is now prevalent. AI elucidates computational systems which extract signals and learn from the input. [1] Historically, AI has a close relationship with biology. Precisely, this relation is between statistics and genetics. Compared to text or image datasets, biological data is more multidimensional, complicated, and noisy. Hence, AI models are more pertinent than simple statistical models for biological datasets. [6] Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

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“…Study results demonstrated that the proposed framework is significantly more accurate and faster than existing methods. [ 152 ]…”

Section: Challenging Issues Faced With Ai and Microfluidics For Biote...mentioning

confidence: 99%

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte

Soltani

Siavashy

et al. 2022

Small

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“…Additionally, Elsharkawy, Ahmed, and Salem [42] believe that the heterogeneity and complexity are two main issues highlighted for the solution and integration of clinical data. The data are generated in different formats, such as RDF, XML, and images.…”

Section: Which Of the Domains Are Mainly Focused On Researching Heterogeneity?mentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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“…Data can exist over numerous datasets, which requires data to be consolidated and merged using some common ﬁelds [ 12 ]. Data integration can be defined as the process of integrating data from various sources in a standard format, storing data in proper repositories, and providing a uniﬁed view of the data to be used for retrieval and analysis [ 60 ].…”

Section: Semantics For Healthcare Data Integrationmentioning

confidence: 99%

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Hammad

Barhoush

Abed-alguni

2020

Journal of Healthcare Engineering

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Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

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Semantic-based Approach for Solving the Heterogeneity of Clinical Data

Cited by 4 publications

References 17 publications

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

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