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Cheung KS. Big data approach in the field of gastric and colorectal cancer research. J Gastroenterol Hepatol 2024; 39:1027-1032. [PMID: 38413187 DOI: 10.1111/jgh.16527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 02/07/2024] [Indexed: 02/29/2024]
Abstract
Big data is characterized by three attributes: volume, variety,, and velocity. In healthcare setting, big data refers to vast dataset that is electronically stored and managed in an automated manner and has the potential to enhance human health and healthcare system. In this review, gastric cancer (GC) and postcolonoscopy colorectal cancer (PCCRC) will be used to illustrate application of big data approach in the field of gastrointestinal cancer research. Helicobacter pylori (HP) eradication only reduces GC risk by 46% due to preexisting precancerous lesions. Apart from endoscopy surveillance, identifying medications that modify GC risk is another strategy. Population-based cohort studies showed that long-term use of proton pump inhibitors (PPIs) associated with higher GC risk after HP eradication, while aspirin and statins associated with lower risk. While diabetes mellitus conferred 73% higher GC risk, metformin use associated with 51% lower risk, effect of which was independent of glycemic control. Nonetheless, nonsteroidal anti-inflammatory drugs (NA-NSAIDs) are not associated with lower GC risk. CRC can still occur after initial colonoscopy in which no cancer was detected (i.e. PCCRC). Between 2005 and 2013, the rate of interval-type PCCRC-3y (defined as CRC diagnosed between 6 and 36 months of index colonoscopy which was negative for CRC) was 7.9% in Hong Kong, with >80% being distal cancers and higher cancer-specific mortality compared with detected CRC. Certain clinical and endoscopy-related factors were associated with PCCRC-3 risk. Medications shown to have chemopreventive effects on PCCRC include statins, NA-NSAIDs, and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers.
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Affiliation(s)
- Ka Shing Cheung
- Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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2
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Enoe J, Sutherland M, Davis D, Ramlal B, Griffith-Charles C, Bhola KH, Asefa EM. A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment. GEOSPATIAL HEALTH 2024; 19. [PMID: 38551510 DOI: 10.4081/gh.2024.1264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/28/2024] [Indexed: 04/02/2024]
Abstract
Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.
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Affiliation(s)
- Jerry Enoe
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Michael Sutherland
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Dexter Davis
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Bheshem Ramlal
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Charisse Griffith-Charles
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Keston H Bhola
- Department of Computers and Technology, School of Arts and Science, St George's University.
| | - Elsai Mati Asefa
- School of Environmental Health, College of Health and Medical Sciences, Haramaya University, Harar.
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3
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Pecoraro V, Pirotti T, Trenti T. Evidence of SARS-CoV-2 reinfection: analysis of 35,000 subjects and overview of systematic reviews. Clin Exp Med 2023; 23:1213-1224. [PMID: 36289100 PMCID: PMC9607758 DOI: 10.1007/s10238-022-00922-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 10/11/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Reinfection by SARS-CoV-2 is a rare but possible event. We evaluated the prevalence of reinfections in the Province of Modena and performed an overview of systematic reviews to summarize the current knowledge. METHODS We applied big data analysis and retrospectively analysed the results of oro- or naso-pharyngeal swab results tested for molecular research of viral RNA of SARS-CoV-2 between 1 January 2021 and 30 June 2021 at a single center. We selected individuals with samples sequence of positive, negative and then positive results. Between first and second positive result we considered a time interval of 90 days to be sure of a reinfection. We also performed a search for and evaluation of systematic reviews reporting SARS-CoV-2 reinfection rates. Main information was collected and the methodological quality of each review was assessed, according to A Measurement Tool to Assess systematic Reviews (AMSTAR). RESULTS Initial positive results were revealed in more than 35,000 (20%) subjects; most (28%) were aged 30-49 years old. Reinfection was reported in 1,258 (3.5%); most (33%) were aged 30-49 years old. Reinfection rates according to vaccinated or non-vaccinated subjects were 0.6% vs 1.1% (p < 0.0001). Nine systematic reviews were identified and confirmed that SARS-CoV-2 reinfection rate is a rare event. AMSTAR revealed very low-moderate levels of quality among selected systematic reviews. CONCLUSIONS There is a real, albeit rare risk of SARS-CoV-2 reinfection. Big data analysis enabled accurate estimates of the reinfection rates. Nevertheless, a standardized approach to identify and report reinfection cases should be developed.
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Affiliation(s)
- Valentina Pecoraro
- Department of Laboratory Medicine and Pathology, Azienda USL of Modena, Modena, Italy
| | - Tommaso Pirotti
- Department of Laboratory Medicine and Pathology, Azienda USL of Modena, Modena, Italy
| | - Tommaso Trenti
- Department of Laboratory Medicine and Pathology, Azienda USL of Modena, Modena, Italy
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Taipalus T, Isomöttönen V, Erkkilä H, Äyrämö S. Data Analytics in Healthcare: A Tertiary Study. SN COMPUTER SCIENCE 2023; 4:87. [PMID: 36532635 PMCID: PMC9734338 DOI: 10.1007/s42979-022-01507-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/14/2022] [Indexed: 12/13/2022]
Abstract
The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.
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Affiliation(s)
- Toni Taipalus
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Ville Isomöttönen
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Hanna Erkkilä
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
| | - Sami Äyrämö
- grid.9681.60000 0001 1013 7965Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyvaskyla, Finland
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5
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Khan S, Khan HU, Nazir S. Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing. Sci Rep 2022; 12:22377. [PMID: 36572709 PMCID: PMC9792582 DOI: 10.1038/s41598-022-26090-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.
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Affiliation(s)
- Sulaiman Khan
- grid.412603.20000 0004 0634 1084Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Habib Ullah Khan
- grid.412603.20000 0004 0634 1084Department of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar
| | - Shah Nazir
- grid.502337.00000 0004 4657 4747Department of Computer Science, University of Swabi, Swabi, Pakistan
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Tang PP, Tam IL, Jia Y, Leung SW. Big Data Reality Check (BDRC) for public health: to what extent the environmental health and health services research did meet the 'V' criteria for big data? A study protocol. BMJ Open 2022; 12:e053447. [PMID: 35318232 PMCID: PMC8943752 DOI: 10.1136/bmjopen-2021-053447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Big data technologies have been talked up in the fields of science and medicine. The V-criteria (volume, variety, velocity and veracity, etc) for defining big data have been well-known and even quoted in most research articles; however, big data research into public health is often misrepresented due to certain common misconceptions. Such misrepresentations and misconceptions would mislead study designs, research findings and healthcare decision-making. This study aims to identify the V-eligibility of big data studies and their technologies applied to environmental health and health services research that explicitly claim to be big data studies. METHODS AND ANALYSIS Our protocol follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Scoping review and/or systematic review will be conducted. The results will be reported using PRISMA for Scoping Reviews (PRISMA-ScR), or PRISMA 2020 and Synthesis Without Meta-analysis guideline. Web of Science, PubMed, Medline and ProQuest Central will be searched for the articles from the database inception to 2021. Two reviewers will independently select eligible studies and extract specified data. The numeric data will be analysed with R statistical software. The text data will be analysed with NVivo wherever applicable. ETHICS AND DISSEMINATION This study will review the literature of big data research related to both environmental health and health services. Ethics approval is not required as all data are publicly available and involves confidential personal data. We will disseminate our findings in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD42021202306.
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Affiliation(s)
- Pui Pui Tang
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau Institute of Chinese Medical Science, Macau, China
| | - I Lam Tam
- State Key Laboratory of Quality Research in Chinese Medicine, University of Macau Institute of Chinese Medical Science, Macau, China
| | - Yongliang Jia
- BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Siu-Wai Leung
- Edinburgh Bayes Centre for AI Research in Shenzhen, College of Science and Engineering, University of Edinburgh, Scotland, UK
- Center for Machine Learning and Intelligent Applications, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, People's Republic of China
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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Chamola V, Hassija V, Gupta S, Goyal A, Guizani M, Sikdar B. Disaster and Pandemic Management Using Machine Learning: A Survey. IEEE INTERNET OF THINGS JOURNAL 2021; 8:16047-16071. [PMID: 35782181 PMCID: PMC8768997 DOI: 10.1109/jiot.2020.3044966] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 05/14/2023]
Abstract
This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
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Affiliation(s)
- Vinay Chamola
- Department of Electrical and Electronics Engineering & APPCAIRBirla Institute of Technology and Science at PilaniPilani333031India
| | - Vikas Hassija
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Sakshi Gupta
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Adit Goyal
- Department of Computer Science and ITJaypee Institute of Information TechnologyNoida201304India
| | - Mohsen Guizani
- Department of Computer Science and EngineeringQatar UniversityDohaQatar
| | - Biplab Sikdar
- Department of Electrical and Computer EngineeringNational University of SingaporeSingapore119077
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Naithani N, Sinha S, Misra P, Vasudevan B, Sahu R. Precision medicine: Concept and tools. Med J Armed Forces India 2021; 77:249-257. [PMID: 34305276 PMCID: PMC8282508 DOI: 10.1016/j.mjafi.2021.06.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Precision medicine is the new age medicine and refers to tailoring treatments to a subpopulation who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.
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Affiliation(s)
- Nardeep Naithani
- Director & Commandant, Armed Forces Medical College, Pune, India
| | - Sharmila Sinha
- Professor & Head, Department of Pharmacology, Armed Forces Medical College, Pune, India
| | - Pratibha Misra
- Professor & Head, Department of Biochemistry, Armed Forces Medical College, Pune, India
| | - Biju Vasudevan
- Professor & Head, Department of Dermatology, Armed Forces Medical College, Pune, India
| | - Rajesh Sahu
- Associate Professor, Department of Community Medicine, Armed Forces Medical College, Pune, India
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10
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Zahid A, Poulsen JK, Sharma R, Wingreen SC. A systematic review of emerging information technologies for sustainable data-centric health-care. Int J Med Inform 2021; 149:104420. [PMID: 33706031 DOI: 10.1016/j.ijmedinf.2021.104420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 02/14/2021] [Accepted: 02/15/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Of the Sustainable Development Goals (SDGs), the third presents the opportunity for a predictive universal digital healthcare ecosystem, capable of informing early warning, assisting in risk reduction and guiding management of national and global health risks. However, in reality, the existing technology infrastructure of digital healthcare systems is insufficient, failing to satisfy current and future data needs. OBJECTIVE This paper systematically reviews emerging information technologies for data modelling and analytics that have potential to achieve Data-Centric Health-Care (DCHC) for the envisioned objective of sustainable healthcare. The goal of this review is to: 1) identify emerging information technologies with potential for data modelling and analytics, and 2) explore recent research of these technologies in DCHC. FINDINGS A total of 1619 relevant papers have been identified and analysed in this review. Of these, 69 were probed deeply. Our analysis found that the extant research focused on elder care, rehabilitation, chronic diseases, and healthcare service delivery. Use-cases of the emerging information technologies included providing assistance, monitoring, self-care and self-management, diagnosis, risk prediction, well-being awareness, personalized healthcare, and qualitative and/or quantitative service enhancement. Limitations identified in the studies included vendor hardware specificity, issues with user interface and usability, inadequate features, interoperability, scalability, and compatibility, unjustifiable costs and insufficient evaluation in terms of validation. CONCLUSION Achievement of a predictive universal digital healthcare ecosystem in the current context is a challenge. State-of-the-art technologies demand user centric design, data privacy and protection measures, transparency, interoperability, scalability, and compatibility to achieve the SDG objective of sustainable healthcare by 2030.
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Affiliation(s)
- Arnob Zahid
- Department of Accounting and Information Systems, College of Business and Law, University of Canterbury, Christchurch, New Zealand.
| | | | - Ravi Sharma
- College of Technological Innovation, Zayed University, Abu Dhabi, United Arab Emirates.
| | - Stephen C Wingreen
- Department of Accounting and Information Systems, College of Business and Law, University of Canterbury, Christchurch, New Zealand.
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11
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Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review. Processes (Basel) 2020. [DOI: 10.3390/pr8080951] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.
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12
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Pirri S, Lorenzoni V, Turchetti G. Scoping review and bibliometric analysis of Big Data applications for Medication adherence: an explorative methodological study to enhance consistency in literature. BMC Health Serv Res 2020; 20:688. [PMID: 32709237 PMCID: PMC7379348 DOI: 10.1186/s12913-020-05544-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 07/14/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Medication adherence has been studied in different settings, with different approaches, and applying different methodologies. Nevertheless, our knowledge and efficacy are quite limited in terms of measuring and evaluating all the variables and components that affect the management of medication adherence regimes as a complex phenomenon. The study aim is mapping the state-of-the-art of medication adherence measurement and assessment methods applied in chronic conditions. Specifically, we are interested in what methods and assessment procedures are currently used to tackle medication adherence. We explore whether Big Data techniques are adopted to improve decision-making procedures regarding patients' adherence, and the possible role of digital technologies in supporting interventions for improving patient adherence and avoiding waste or harm. METHODS A scoping literature review and bibliometric analysis were used. Arksey and O'Malley's framework was adopted to scope the review process, and a bibliometric analysis was applied to observe the evolution of the scientific literature and identify specific characteristics of the related knowledge domain. RESULTS A total of 533 articles were retrieved from the Scopus academic database and selected for the bibliometric analysis. Sixty-one studies were identified and included in the final analysis. The Morisky medication adherence scale (36%) was the most frequently adopted baseline measurement tool, and cardiovascular/hypertension disease, the most investigated illness (38%). Heterogeneous findings emerged from the types of study design and the statistical methodologies used to assess and compare the results. CONCLUSIONS Our findings reveal a lack of Big Data applications currently deployed to address or measure medication adherence in chronic conditions. Our study proposes a general framework to select the methods, measurements and the corpus of variables in which the treatment regime can be analyzed.
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Affiliation(s)
- Salvatore Pirri
- Institute of Management, Scuola Superiore Sant’Anna, Pisa, Italy
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13
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D'Amico F, Baumann C, Rousseau H, Danese S, Peyrin-Biroulet L. Phase I, II and III Trials in Inflammatory Bowel Diseases: A Practical Guide for the Non-specialist. J Crohns Colitis 2020; 14:710-718. [PMID: 31901097 DOI: 10.1093/ecco-jcc/jjz214] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
In the last few decades several new molecules have been developed in the field of inflammatory bowel diseases. However, the process that leads to the approval and use of a new drug is very long, expensive and complex, consisting of various phases. There is a pre-clinical phase that is performed on animals and a clinical phase that is directed to humans. Each research phase aims to evaluate different aspects of the drug and involves a specific target group of subjects. In addition, many aspects must be considered in the evaluation of a clinical trial: randomization, presence of a control group, blind design, type of data analysis performed, and patient stratification. The objective of this review is to provide an overview of the clinical trial phases of a new drug in order to better understand and interpret their results.
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Affiliation(s)
- Ferdinando D'Amico
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,Department of Gastroenterology and Inserm NGERE U1256, University of Lorraine, Vandoeuvre-lès-Nancy, France
| | - Cedric Baumann
- Clinical Research Support Facility PARC, UMDS, Nancy University Hospital, Vandoeuvre-lès-Nancy, France
| | - Hélène Rousseau
- Clinical Research Support Facility PARC, UMDS, Nancy University Hospital, Vandoeuvre-lès-Nancy, France
| | - Silvio Danese
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,IBD Center, Department of Gastroenterology, Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Department of Gastroenterology and Inserm NGERE U1256, University of Lorraine, Vandoeuvre-lès-Nancy, France
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14
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Russak AJ, Chaudhry F, De Freitas JK, Baron G, Chaudhry FF, Bienstock S, Paranjpe I, Vaid A, Ali M, Zhao S, Somani S, Richter F, Bawa T, Levy PD, Miotto R, Nadkarni GN, Johnson KW, Glicksberg BS. Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype. J Cardiovasc Pharmacol Ther 2020; 25:379-390. [PMID: 32495652 DOI: 10.1177/1074248420928651] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.
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Affiliation(s)
- Adam J Russak
- Department of Internal Medicine, Mount Sinai Hospital, New York, NY, USA.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Farhan Chaudhry
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Garrett Baron
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Fayzan F Chaudhry
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Solomon Bienstock
- Department of Internal Medicine, Mount Sinai Hospital, New York, NY, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohsin Ali
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shan Zhao
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Felix Richter
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tejeshwar Bawa
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Phillip D Levy
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Division of Nephrology, Mount Sinai Hospital, New York, NY, USA.,Division of Cardiology, Mount Sinai Hospital, New York, NY, USA.,Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kipp W Johnson
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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15
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Sanchez-Duque JA, Gaviria-Mendoza A, Moreno-Gutierrez PA, Machado-Alba JE. Big data, farmacoepidemiología y farmacovigilancia. REVISTA DE LA FACULTAD DE MEDICINA 2020. [DOI: 10.15446/revfacmed.v68n1.73456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Big data es un término que comprende un grupo de herramientas tecnológicas capaces de procesar conjuntos de datos heterogéneos extremadamente grandes, los cuales se recolectan de manera continua, están disponibles para ser usados y constituyen una fuente de evidencia científica.En el área de la farmacoepidemiología, los análisis generados a partir de estos conjuntos de datos pueden resultar en la obtención de terapias médicas más eficientes, con menor número de reacciones adversas y menos costosas. Asimismo, el uso de herramientas como el Text Mining o el Machine Learning también ha llevado a grandes avances en las áreas de farmacoepidemiología y farmacovigilancia, por lo que es probable que su empleo sea cada vez mayor.
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16
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Du X, Zhu R, Li Y, Anjum A. Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis. FUTURE GENERATIONS COMPUTER SYSTEMS : FGCS 2019; 98:238-251. [PMID: 32287562 PMCID: PMC7103364 DOI: 10.1016/j.future.2019.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 12/15/2018] [Accepted: 01/13/2019] [Indexed: 06/11/2023]
Abstract
In biomedical domain, abbreviations are appearing more and more frequently in various data sets, which has caused significant obstacles to biomedical big data analysis. The dictionary-based approach has been adopted to process abbreviations, but it cannot handle ad hoc abbreviations, and it is impossible to cover all abbreviations. To overcome these drawbacks, this paper proposes an automatic abbreviation expansion method called LMAAE (Language Model-based Automatic Abbreviation Expansion). In this method, the abbreviation is firstly divided into blocks; then, expansion candidates are generated by restoring each block; and finally, the expansion candidates are filtered and clustered to acquire the final expansion result according to the language model and clustering method. Through restrict the abbreviation to prefix abbreviation, the search space of expansion is reduced sharply. And then, the search space is continuous reduced by restrained the effective and the length of the partition. In order to validate the effective of the method, two types of experiments are designed. For standard abbreviations, the expansion results include most of the expansion in dictionary. Therefore, it has a high precision. For ad hoc abbreviations, the precisions of schema matching, knowledge fusion are increased by using this method to handle the abbreviations. Although the recall for standard abbreviation needs to be improved, but this does not affect the good complement effect for the dictionary method.
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Affiliation(s)
- Xiaokun Du
- College of Computer Science, South-Central University for Nationalities, Wuhan, China
| | - Rongbo Zhu
- College of Computer Science, South-Central University for Nationalities, Wuhan, China
| | - Yanhong Li
- College of Computer Science, South-Central University for Nationalities, Wuhan, China
| | - Ashiq Anjum
- College of Engineering and Technology, University of Derby, Derby, UK
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17
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Decision-Making based on Big Data Analytics for People Management in Healthcare Organizations. J Med Syst 2019; 43:290. [PMID: 31332535 DOI: 10.1007/s10916-019-1419-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 07/08/2019] [Indexed: 12/30/2022]
Abstract
Big data analytics enables large-scale data sets integration, supporting people management decisions, and cost-effectiveness evaluation of healthcare organizations. The purpose of this article is to address the decision-making process based on big data analytics in Healthcare organizations, to identify main big data analytics able to support healthcare leaders' decisions and to present some strategies to enhance efficiency along the healthcare value chain. Our research was based on a systematic review. During the literature review, we will be presenting as well the different applications of big data in the healthcare context and a proposal for a predictive model for people management processes. Our research underlines the importance big data analytics can add to the efficiency of the decision-making process, through a predictive model and real-time analytics, assisting in the collection, management, and integration of data in healthcare organizations.
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18
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Kedra J, Radstake T, Pandit A, Baraliakos X, Berenbaum F, Finckh A, Fautrel B, Stamm TA, Gomez-Cabrero D, Pristipino C, Choquet R, Servy H, Stones S, Burmester G, Gossec L. Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations. RMD Open 2019; 5:e001004. [PMID: 31413871 PMCID: PMC6668041 DOI: 10.1136/rmdopen-2019-001004] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/26/2019] [Accepted: 06/29/2019] [Indexed: 12/27/2022] Open
Abstract
Objective To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs). Methods A systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs. Results Of 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs). Conclusions Big data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
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Affiliation(s)
- Joanna Kedra
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France.,Rheumatology Department, Hôpital Universitaire Pitié Salpêtrière, APHP, Paris, France
| | - Timothy Radstake
- Department of Rheumatology, Clinical Immunology and Laboratory for Translational Immunology, University of Utrecht Faculty of Medicine, Utrecht, The Netherlands
| | - Aridaman Pandit
- Department of Rheumatology, Clinical Immunology and Laboratory for Translational Immunology, University of Utrecht Faculty of Medicine, Utrecht, The Netherlands
| | | | - Francis Berenbaum
- Rheumatology Department, Hospital Saint-Antoine, APHP, Paris, Île-de-France, France
| | - Axel Finckh
- Division of Rheumatology, University Hospital of Geneva, Geneva, Switzerland
| | - Bruno Fautrel
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France.,Rheumatology Department, Hôpital Universitaire Pitié Salpêtrière, APHP, Paris, France
| | - Tanja A Stamm
- Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - David Gomez-Cabrero
- Departamento de Salud-Universidad Pública de Navarra, Translational Bioinformatics Unit, Navarra Biomed, Pamplona, Spain
| | | | | | | | - Simon Stones
- School of Healthcare, University of Leeds, Leeds, West Yorkshire, UK
| | - Gerd Burmester
- Department of Rheumatology and Clinical Immunology, Charité - University Medicine Berlin, Berlin, Germany
| | - Laure Gossec
- Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris, France.,Rheumatology Department, Hôpital Universitaire Pitié Salpêtrière, APHP, Paris, France
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19
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Cheung KS, Leung WK, Seto WK. Application of Big Data analysis in gastrointestinal research. World J Gastroenterol 2019; 25:2990-3008. [PMID: 31293336 PMCID: PMC6603810 DOI: 10.3748/wjg.v25.i24.2990] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 04/14/2019] [Accepted: 04/29/2019] [Indexed: 02/06/2023] Open
Abstract
Big Data, which are characterized by certain unique traits like volume, velocity and value, have revolutionized the research of multiple fields including medicine. Big Data in health care are defined as large datasets that are collected routinely or automatically, and stored electronically. With the rapidly expanding volume of health data collection, it is envisioned that the Big Data approach can improve not only individual health, but also the performance of health care systems. The application of Big Data analysis in the field of gastroenterology and hepatology research has also opened new research approaches. While it retains most of the advantages and avoids some of the disadvantages of traditional observational studies (case-control and prospective cohort studies), it allows for phenomapping of disease heterogeneity, enhancement of drug safety, as well as development of precision medicine, prediction models and personalized treatment. Unlike randomized controlled trials, it reflects the real-world situation and studies patients who are often under-represented in randomized controlled trials. However, residual and/or unmeasured confounding remains a major concern, which requires meticulous study design and various statistical adjustment methods. Other potential drawbacks include data validity, missing data, incomplete data capture due to the unavailability of diagnosis codes for certain clinical situations, and individual privacy. With continuous technological advances, some of the current limitations with Big Data may be further minimized. This review will illustrate the use of Big Data research on gastrointestinal and liver diseases using recently published examples.
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Affiliation(s)
- Ka-Shing Cheung
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, Guangdong Province, China
| | - Wai K Leung
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China
| | - Wai-Kay Seto
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, Guangdong Province, China
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20
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Silveira FP, Saul M, Nowalk MP, Saul S, Sax TM, Eng H, Zimmerman RK, Balasubramani GK. Determination of Eligibility for Influenza Research: A Clinical Informatics Approach. Open Forum Infect Dis 2019; 6:ofz231. [PMID: 31205975 PMCID: PMC6557306 DOI: 10.1093/ofid/ofz231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 05/31/2019] [Indexed: 11/14/2022] Open
Abstract
Background A clinical informatics algorithm (CIA) was developed to systematically identify potential enrollees for a test-negative, case-control study to determine influenza vaccine effectiveness, to improve enrollment over manual records review. Further testing may enhance the CIA for increased efficiency. Methods The CIA generated a daily screening list by querying all medical record databases for patients admitted in the last 3 days, using specified terms and diagnosis codes located in admission notes, emergency department notes, chief complaint upon registration, or presence of a respiratory viral panel charge or laboratory result (RVP). Classification and regression tree analysis (CART) and multivariable logistic regression were used to refine the algorithm. Results Using manual records review, 204 patients (<4/day) were approached and 144 were eligible in the 2014-2015 season compared with 3531 (12/day) patients who were approached and 1136 who were eligible in the 2016-2017 season using a CIA. CART analysis identified RVP as the most important indicator from the CIA list for determining eligibility, identifying 65%-69% of the samples and predicting 1587 eligible patients. RVP was confirmed as the most significant predictor in regression analysis, with an odds ratio (OR) of 4.9 (95% confidence interval [CI], 4.0-6.0). Other significant factors were indicators in admission notes (OR, 2.3 [95% CI, 1.9-2.8]) and emergency department notes (OR, 1.8 [95% CI, 1.4-2.3]). Conclusions This study supports the benefits of a CIA to facilitate recruitment of eligible participants in clinical research over manual records review. Logistic regression and CART identified potential eligibility screening criteria reductions to improve the CIA's efficiency.
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Affiliation(s)
- Fernanda P Silveira
- Department of Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Melissa Saul
- Department of Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Mary Patricia Nowalk
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Sean Saul
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Theresa M Sax
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
| | - Heather Eng
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
| | - Richard K Zimmerman
- Department of Family Medicine, School of Medicine, University of Pittsburgh, Pennsylvania
| | - Goundappa K Balasubramani
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pennsylvania
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21
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Olivera P, Danese S, Jay N, Natoli G, Peyrin-Biroulet L. Big data in IBD: a look into the future. Nat Rev Gastroenterol Hepatol 2019; 16:312-321. [PMID: 30659247 DOI: 10.1038/s41575-019-0102-5] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data.
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Affiliation(s)
- Pablo Olivera
- Gastroenterology Section, Department of Internal Medicine, Centro de Educación Médica e Investigaciones Clínicas (CEMIC), Buenos Aires, Argentina
| | - Silvio Danese
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Centre, Rozzano, Milan, Italy.,Humanitas Clinical Research Hospital, Rozzano, Milan, Italy
| | - Nicolas Jay
- Orpailleur and Department of Medical Information, LORIA and Nancy University Hospital, Vandoeuvre-lès-Nancy, Nancy, France
| | | | - Laurent Peyrin-Biroulet
- INSERM U954 and Department of Hepatogastroenterology, Nancy University Hospital, Université de Lorraine, Vandoeuvre-lès-Nancy, Nancy, France.
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22
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Riondino S, Ferroni P, Zanzotto FM, Roselli M, Guadagni F. Predicting VTE in Cancer Patients: Candidate Biomarkers and Risk Assessment Models. Cancers (Basel) 2019; 11:cancers11010095. [PMID: 30650562 PMCID: PMC6356247 DOI: 10.3390/cancers11010095] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 02/07/2023] Open
Abstract
Risk prediction of chemotherapy-associated venous thromboembolism (VTE) is a compelling challenge in contemporary oncology, as VTE may result in treatment delays, impaired quality of life, and increased mortality. Current guidelines do not recommend thromboprophylaxis for primary prevention, but assessment of the patient's individual risk of VTE prior to chemotherapy is generally advocated. In recent years, efforts have been devoted to building accurate predictive tools for VTE risk assessment in cancer patients. This review focuses on candidate biomarkers and prediction models currently under investigation, considering their advantages and disadvantages, and discussing their diagnostic performance and potential pitfalls.
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Affiliation(s)
- Silvia Riondino
- Interinstitutional Multidisciplinary Biobank, IRCCS San Raffaele Pisana, 00166 Rome, Italy.
- Department of Systems Medicine, Medical Oncology, University of Rome Tor Vergata, 00133 Rome, Italy.
| | - Patrizia Ferroni
- Interinstitutional Multidisciplinary Biobank, IRCCS San Raffaele Pisana, 00166 Rome, Italy.
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy.
| | - Fabio Massimo Zanzotto
- Department of Enterprise Engineering, University of Rome "Tor Vergata", 00133 Rome, Italy.
| | - Mario Roselli
- Department of Systems Medicine, Medical Oncology, University of Rome Tor Vergata, 00133 Rome, Italy.
| | - Fiorella Guadagni
- Interinstitutional Multidisciplinary Biobank, IRCCS San Raffaele Pisana, 00166 Rome, Italy.
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy.
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23
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Kim YK, Kang D, Lee I, Kim SY. Differences in the Incidence of Symptomatic Cervical and Lumbar Disc Herniation According to Age, Sex and National Health Insurance Eligibility: A Pilot Study on the Disease's Association with Work. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:E2094. [PMID: 30257414 PMCID: PMC6210730 DOI: 10.3390/ijerph15102094] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 09/15/2018] [Accepted: 09/19/2018] [Indexed: 02/07/2023]
Abstract
The aim of this research was to identify the differences in the incidence of symptomatic cervical and lumbar disc herniation according to age, sex, and national health insurance eligibility. We evaluated the hospital documents of patients who received medical treatment for symptomatic cervical and lumbar disc herniation between 2004 and 2010 and excluded those who claimed to have expenses at oriental medical clinics or pharmacies. Furthermore, any duplicate documents from the labor force population aged 20⁻69 years were excluded from the analysis. The results showed that the number of individuals diagnosed with symptomatic cervical and lumbar disc herniation increased with age, and the incidence of these diseases was higher in women than in men. Additionally, the incidence differed depending on the subject's qualification for health insurance. The incidence of lumbar disc herniation showed differences depending on the degree of the lumbar burden. The present study findings may help determine whether lumbar disc herniation is associated with tasks performed at the patient's workplace. Further research is needed to classify the risk of lumbar disk herniation in the workplace into detailed categories such as types of business, types of occupation, and lumbar compression force.
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Affiliation(s)
- Young-Ki Kim
- Department of Occupational and Environmental Medicine, Pusan National University, Yangsan Hopspital, Yangsan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
- Department of Preventive and Occupational & Environmental Medicine, School of Medicine, Pusan National University, Busan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
| | - Dongmug Kang
- Department of Occupational and Environmental Medicine, Pusan National University, Yangsan Hopspital, Yangsan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
- Department of Preventive and Occupational & Environmental Medicine, School of Medicine, Pusan National University, Busan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
| | - Ilho Lee
- Department of Occupational and Environmental Medicine, Pusan National University, Yangsan Hopspital, Yangsan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
| | - Se-Yeong Kim
- Department of Occupational and Environmental Medicine, Pusan National University, Yangsan Hopspital, Yangsan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
- Department of Preventive and Occupational & Environmental Medicine, School of Medicine, Pusan National University, Busan, Mulgeum-eup, Bumeo-ri, Yangsan, Gyongnam 626-770, Korea.
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24
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Alonso SG, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 2018; 42:161. [PMID: 30030644 DOI: 10.1007/s10916-018-1018-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 07/16/2018] [Indexed: 12/12/2022]
Abstract
Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.
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Affiliation(s)
- Susel Góngora Alonso
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
| | - Sofiane Hamrioui
- Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech Nantes, Nantes, France
| | - Miguel López-Coronado
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Diego Calvo Barreno
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Lola Morón Nozaleda
- Nozaleda and Lafora Mental Health Clinic, C/ José Ortega Y Gasset, 44, 28006, Madrid, Spain
| | - Manuel Franco
- Psiquiatry Service, Hospital Zamora, Hernán Cortés, Zamora, Spain
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