151
|
A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management. ALGORITHMS 2020. [DOI: 10.3390/a13040102] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population health include better healthcare outcomes, improvement of clinical operations, reducing costs of care, and generation of accurate medical information. Several machine learning algorithms implemented by the authors can use the large standardized datasets integrated into the platform to improve the effectiveness of public health interventions, improving diagnosis, and clinical decision support. The data integrated into the platform come from Electronic Health Records (EHR), Hospital Information Systems (HIS), Radiology Information Systems (RIS), and Laboratory Information Systems (LIS), as well as data generated by public health platforms, mobile data, social media, and clinical web portals. This massive volume of data is integrated using big data techniques for storage, retrieval, processing, and transformation. This paper presents the design of a digital health platform in a healthcare organization in Colombia to integrate operational, clinical, and business data repositories with advanced analytics to improve the decision-making process for population health management.
Collapse
|
152
|
Capobianco E, Iacoviello L, de Gaetano G, Donati MB. Editorial: Trends in Digital Medicine. Front Med (Lausanne) 2020; 7:116. [PMID: 32309285 PMCID: PMC7145957 DOI: 10.3389/fmed.2020.00116] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 03/13/2020] [Indexed: 12/02/2022] Open
Affiliation(s)
- Enrico Capobianco
- Institute Data Science and Computing (IDSC), University of Miami, Miami, FL, United States
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy.,Department of Medicine and Surgery, University of Insubria, Varese, Italy
| | | | | |
Collapse
|
153
|
Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects. Updates Surg 2020; 72:355-378. [PMID: 32306277 DOI: 10.1007/s13304-020-00771-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022]
Abstract
Gastric cancer is the fifth malignancy and the third cause of cancer death worldwide, according to the global cancer statistics presented in 2018. Its definition and staging have been revised in the eight edition of the AJCC/TNM classification, which took effect in 2018. Novel molecular classifications for GC have been recently established and the process of translating these classifications into clinical practice is ongoing. The cornerstone of GC treatment is surgical, in a context of multimodal therapy. Surgical treatment is being standardized, and is evolving according to new anatomical concepts and to the recent technological developments. This is leading to a massive improvement in the use of mini-invasive techniques. Mini-invasive techniques aim to be equivalent to open surgery from an oncologic point of view, with better short-term outcomes. The persecution of better short-term outcomes also includes the optimization of the perioperative management, which is being implemented on large scale according to the enhanced recovery after surgery principles. In the era of precision medicine, multimodal treatment is also evolving. The long-time-awaited results of many trials investigating the role for preoperative and postoperative management have been published, changing the clinical practice. Novel investigations focused both on traditional chemotherapeutic regimens and targeted therapies are currently ongoing. Modern platforms increase the possibility for further standardization of the different treatments, promote the use of big data and open new possibilities for surgical learning. This systematic review in two parts assesses all the current updates in GC treatment.
Collapse
|
154
|
Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
Collapse
Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
| |
Collapse
|
155
|
Capobianco E. Imprecise Data and Their Impact on Translational Research in Medicine. Front Med (Lausanne) 2020; 7:82. [PMID: 32266273 PMCID: PMC7096475 DOI: 10.3389/fmed.2020.00082] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 03/02/2020] [Indexed: 11/13/2022] Open
Abstract
The medical field expects from big data essentially two main results: the ability to build predictive models and the possibility of applying them to obtain accurate patient risk profiles and/or health trajectories. Note that the paradigm of precision has determined that similar challenges need to be faced in both population and individualized studies, namely the need of assembling, integrating, modeling, and interpreting data from a variety of information sources and scales potentially influencing disease from onset to progression. In many cases, data require computational treatment through solutions for otherwise intractable problems. However, as precision medicine remains subject to a substantial amount of data imprecision and lack of translational impact, a revision of methodological inference approaches is needed. Both the relevance and the usefulness of such revision crucially deal with the assimilation of data features dynamically interconnected.
Collapse
Affiliation(s)
- Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, United States
| |
Collapse
|
156
|
Mirbagheri E, Ahmadi M, Salmanian S. Common data elements of breast cancer for research databases: A systematic review. J Family Med Prim Care 2020; 9:1296-1301. [PMID: 32509607 PMCID: PMC7266190 DOI: 10.4103/jfmpc.jfmpc_931_19] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 02/11/2020] [Accepted: 02/13/2020] [Indexed: 11/05/2022] Open
Abstract
Background: Common Data Elements (CDEs) are data-metadata descriptors used to collect research study data. CDEs facilitate the collection, processing, and sharing of breast cancer data. This study intended to explore the CDEs of breast cancer for research databases and primary care systems. Methods: This study was conducted using systematic search and review. This systematic literature review covered PubMed, Scopus, Science Direct, SID, ISC, Web of Science, and Google Scholar search engine. It included studies in English language with accessible full-text from the beginning of 2007 to September 2019. Results: Reviewing 25 studies revealed that 52 percent of studies were carried out in the US and most studies were conducted between 2013 and 2015. The most domains for using CDEs were: Pathology Report and Registry. The CDEs of breast cancer for research databases were categorized into three categories namely clinical, research, and non-clinical and indicate the importance of these data elements. Most of the studies focused on creating and deploying clinical CDEs as physical examination, clinical history and pathology data. Conclusion: The integration of biomedical and clinical data relevant to breast cancer enhances the power of research variable analysis and statistical analysis, thereby facilitating improved knowledge of effective therapeutic interventions. Also CDEs used to collect, store, and retrieve patient data in various health setting such as primary care and research databases.
Collapse
Affiliation(s)
- Esmat Mirbagheri
- Department of Health Information Management, School of Health Management and Information Sciences, Tehran, Iran
| | - Maryam Ahmadi
- Department of Health Information Management, School of Management and Medical Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Soraya Salmanian
- Assistant Professor, Radiation Oncology, Oncophathology Research Center, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
157
|
Vandenberg O, Durand G, Hallin M, Diefenbach A, Gant V, Murray P, Kozlakidis Z, van Belkum A. Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies. Clin Microbiol Rev 2020; 33:e00057-19. [PMID: 32102900 PMCID: PMC7048017 DOI: 10.1128/cmr.00057-19] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.
Collapse
Affiliation(s)
- Olivier Vandenberg
- Innovation and Business Development Unit, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom
| | - Géraldine Durand
- bioMérieux, Microbiology Research and Development, La Balme Les Grottes, France
| | - Marie Hallin
- Department of Microbiology, LHUB-ULB, Groupement Hospitalier Universitaire de Bruxelles (GHUB), Université Libre de Bruxelles, Brussels, Belgium
| | - Andreas Diefenbach
- Department of Microbiology, Infectious Diseases and Immunology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Labor Berlin, Charité-Vivantes GmbH, Berlin, Germany
| | - Vanya Gant
- Department of Clinical Microbiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Patrick Murray
- BD Life Sciences Integrated Diagnostic Solutions, Scientific Affairs, Sparks, Maryland, USA
| | - Zisis Kozlakidis
- Laboratory Services and Biobank Group, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Alex van Belkum
- bioMérieux, Open Innovation and Partnerships, La Balme Les Grottes, France
| |
Collapse
|
158
|
Guvakova MA. Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule. Oncoscience 2020; 6:383-385. [PMID: 31984216 PMCID: PMC6959929 DOI: 10.18632/oncoscience.494] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 10/01/2019] [Indexed: 11/25/2022] Open
Abstract
In clinical research, determining cutoff values for continuous variables in test results remains challenging, particularly when considering candidate biomarkers or therapeutic targets for disease. Distribution of a continuous variable into two populations is known as dichotomization and has been commonly used in clinical studies. We recently reported a new method for determining multiple cutoffs for continuous variables. The development of this original approach was based on fitting Gaussian Mixture Models (GMM) onto real-world clinical data. We also explored how to leverage Bayesian probability to minimize uncertainty while classifying individual patients into respective subpopulations. In addition, we investigated the performance of the proposed method for the distribution of classical prognostic markers in breast cancer. Finally, we applied the proposed method to analyze a candidate marker and a target for cancer therapy. Here, we present an overview of this method and our prospects for its implementation in biomedical and clinical research.
Collapse
Affiliation(s)
- Marina A Guvakova
- Department of Surgery, Division of Endocrine & Oncologic Surgery, Harrison Department of Surgical Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
159
|
Porumb M, Stranges S, Pescapè A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 2020; 10:170. [PMID: 31932608 PMCID: PMC6957484 DOI: 10.1038/s41598-019-56927-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 01/21/2023] Open
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
Collapse
Affiliation(s)
- Mihaela Porumb
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Antonio Pescapè
- Department of Electrical Engineering, University of Napoli "Federico II", Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
| |
Collapse
|
160
|
Evolution of mHealth Eco-System: A Step Towards Personalized Medicine. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2020. [DOI: 10.1007/978-981-15-1286-5_30] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
161
|
Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci 2020; 16:365-373. [PMID: 32015674 PMCID: PMC6990894 DOI: 10.7150/ijbs.39387] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/11/2019] [Indexed: 12/13/2022] Open
Abstract
In the past 20 years, the concept of surgery has undergone profound changes. Surgical practice has shifted from emphasizing the complete elimination of lesions to achieving optimal rehabilitation in patients. Collaborative optimization of surgery consists of three core elements, removal of lesions, organ protection and injury close monitoring, and controlled surgical intervention. As a result, the traditional surgical paradigm has quietly transformed into a modern precision surgical paradigm. In this review, we summarized the latest breakthroughs and applications of precision medicine in liver surgery. In addition, we also outlined the progresses that have been made in precision liver surgery, the opportunities and challenges that may encountered in the future.
Collapse
Affiliation(s)
- Hao Chen
- Department of Hepatic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, HeFei, 230001, China.,Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, HeFei, 230001, China
| | - Yuchen He
- Xiangya School of Medicine, Central South University, ChangSha, 410008, China
| | - Weidong Jia
- Department of Hepatic Surgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, HeFei, 230001, China.,Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, HeFei, 230001, China
| |
Collapse
|
162
|
Systems and Precision Medicine in Necrotizing Soft Tissue Infections. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1294:187-207. [PMID: 33079370 DOI: 10.1007/978-3-030-57616-5_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.
Collapse
|
163
|
Clinical neurophysiology standards of EMG instrumentation: Twenty years of changes. Clin Neurophysiol 2020; 131:235-236. [DOI: 10.1016/j.clinph.2019.08.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 11/20/2022]
|
164
|
Donovan BM, Bastarache L, Turi KN, Zutter MM, Hartert TV. The current state of omics technologies in the clinical management of asthma and allergic diseases. Ann Allergy Asthma Immunol 2019; 123:550-557. [PMID: 31494234 PMCID: PMC6931133 DOI: 10.1016/j.anai.2019.08.460] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/27/2019] [Accepted: 08/29/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review the state of omics science specific to asthma and allergic diseases and discuss the current and potential applicability of omics in clinical disease prediction, treatment, and management. DATA SOURCES Studies and reviews focused on the use of omics technologies in asthma and allergic disease research and clinical management were identified using PubMed. STUDY SELECTIONS Publications were included based on relevance, with emphasis placed on the most recent findings. RESULTS Omics-based research is increasingly being used to differentiate asthma and allergic disease subtypes, identify biomarkers and pathological mediators, predict patient responsiveness to specific therapies, and monitor disease control. Although most studies have focused on genomics and transcriptomics approaches, increasing attention is being placed on omics technologies that assess the effect of environmental exposures on disease initiation and progression. Studies using omics data to identify biological targets and pathways involved in asthma and allergic disease pathogenesis have primarily focused on a specific omics subtype, providing only a partial view of the disease process. CONCLUSION Although omics technologies have advanced our understanding of the molecular mechanisms underlying asthma and allergic disease pathology, omics testing for these diseases are not standard of care at this point. Several important factors need to be addressed before these technologies can be used effectively in clinical practice. Use of clinical decision support systems and integration of these systems within electronic medical records will become increasingly important as omics technologies become more widely used in the clinical setting.
Collapse
Affiliation(s)
- Brittney M Donovan
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kedir N Turi
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Mary M Zutter
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tina V Hartert
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
| |
Collapse
|
165
|
Hulsen T. The ten commandments of translational research informatics. DATA SCIENCE 2019. [DOI: 10.3233/ds-190020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Tim Hulsen
- Department of Professional Health Solutions & Services, Philips Research, Eindhoven, The Netherlands. E-mail:
| |
Collapse
|
166
|
Nussinov R, Tsai C, Jang H. Autoinhibition can identify rare driver mutations and advise pharmacology. FASEB J 2019; 34:16-29. [DOI: 10.1096/fj.201901341r] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 09/18/2019] [Accepted: 10/09/2019] [Indexed: 12/16/2022]
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Chung‐Jung Tsai
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Hyunbum Jang
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| |
Collapse
|
167
|
[An integral view of cancer (III). Evaluation of new biomarkers and treatment strategies]. REVISTA ESPAÑOLA DE PATOLOGÍA : PUBLICACIÓN OFICIAL DE LA SOCIEDAD ESPAÑOLA DE ANATOMÍA PATOLÓGICA Y DE LA SOCIEDAD ESPAÑOLA DE CITOLOGÍA 2019; 53:88-99. [PMID: 32199599 DOI: 10.1016/j.patol.2019.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 08/27/2019] [Indexed: 11/20/2022]
Abstract
We propose a comprehensive approach to oncological disease, based on a systemic consideration of biology, health and disease. Our two previous review articles focused on tumour microenvironment and the discovery of new biomarkers; here we discuss the practical application of these principles to pathology, through the identification, evaluation and quantitative analysis of new prognostic and predictive factors (Immunoscore, TIME). We also consider the clinical use of promising, better tolerated treatments, such as immunotherapy. The integrative pathologist now has access to the latest improved oncology stratification tools designed to identify effective treatment strategies, based on the natural evolution of clinical and scientific knowledge that transcend the gene-centric theory of cancer.
Collapse
|
168
|
Miscio G, Paroni G, Bisceglia P, Gravina C, Urbano M, Lozupone M, Piccininni C, Prisciandaro M, Ciavarella G, Daniele A, Bellomo A, Panza F, Di Mauro L, Greco A, Seripa D. Pharmacogenetics in the clinical analysis laboratory: clinical practice, research, and drug development pipeline. Expert Opin Drug Metab Toxicol 2019; 15:751-765. [PMID: 31512953 DOI: 10.1080/17425255.2019.1658742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Introduction: Over the last decade, the spread of next-generation sequencing technology along with the rising cost in health management in national health systems has led to widespread use/abuse of pharmacogenetic tests (PGx) in the practice of many clinical disciplines. However, given their clinical significance, it is important to standardize these tests for having an interaction with the clinical analysis laboratory (CAL), in which a PGx service can meet these requirements. Areas covered: A diagnostic test must meet the criteria of reproducibility and validity for its utility in the clinical routine. This present review mainly describes the utility of introducing PGx tests in the CAL routine to produce correct results useful for setting up personalized drug treatments. Expert opinion: With a PGx service, CALs can provide the right tool to help clinicians to make better choices about different categories of drugs and their dosage and to manage the economic impact both in hospital-based settings and in National Health Services, throughout electronic health records. Advances in PGx also allow a new approach for pharmaceutical companies in order to improve drug development and clinical trials. As a result, CALs can achieve a powerful source of epidemiological, clinical, and research findings from PGx tests.
Collapse
Affiliation(s)
- Giuseppe Miscio
- Clinical Laboratory Analysis and Transfusional Medicine, Laboratory and Transfusional Diagnostics, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Giulia Paroni
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Paola Bisceglia
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Carolina Gravina
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Maria Urbano
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Madia Lozupone
- Neurodegenerative Disease Unit, Department of Basic Medical Sciences, Neuroscience, and Sense Organs, University of Bari Aldo Moro , Bari , Italy
| | - Carla Piccininni
- Psychiatric Unit, Department of Clinical and Experimental Medicine, University of Foggia , Foggia , Italy
| | - Michele Prisciandaro
- Clinical Laboratory Analysis and Transfusional Medicine, Laboratory and Transfusional Diagnostics, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Grazia Ciavarella
- Clinical Laboratory Analysis and Transfusional Medicine, Laboratory and Transfusional Diagnostics, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Antonio Daniele
- Institute of Neurology, Catholic University of Sacred Heart , Rome , Italy.,Institute of Neurology, Fondazione Policlinico Universitario A. Gemelli IRCCS , Rome , Italy
| | - Antonello Bellomo
- Psychiatric Unit, Department of Clinical and Experimental Medicine, University of Foggia , Foggia , Italy
| | - Francesco Panza
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy.,Neurodegenerative Disease Unit, Department of Basic Medical Sciences, Neuroscience, and Sense Organs, University of Bari Aldo Moro , Bari , Italy
| | - Lazzaro Di Mauro
- Clinical Laboratory Analysis and Transfusional Medicine, Laboratory and Transfusional Diagnostics, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Antonio Greco
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| | - Davide Seripa
- Research Laboratory, Complex Structure of Geriatrics, Department of Medical Sciences, Fondazione IRCCS Casa Sollievo della Sofferenza , Foggia , Italy
| |
Collapse
|
169
|
Sterk PJ. Modern Inflammatory Phenotyping of Asthma. Breathomics Is Here to Stay. Am J Respir Crit Care Med 2019; 200:405-406. [PMID: 30995075 PMCID: PMC6701043 DOI: 10.1164/rccm.201904-0733ed] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Affiliation(s)
- Peter J Sterk
- 1Amsterdam UMCUniversity of AmsterdamAmsterdam, the Netherlands
| |
Collapse
|
170
|
Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9153065] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.
Collapse
|
171
|
Grogan KL, Goldsmith MP, Masino AJ, Nelson O, Tsui FC, Simpao AF. A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine. J Cardiothorac Vasc Anesth 2019; 34:479-482. [PMID: 31327699 DOI: 10.1053/j.jvca.2019.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 05/20/2019] [Accepted: 06/07/2019] [Indexed: 01/05/2023]
Abstract
Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.
Collapse
Affiliation(s)
- Kelly L Grogan
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael P Goldsmith
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Aaron J Masino
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Olivia Nelson
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Fu-Chiang Tsui
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Allan F Simpao
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
| |
Collapse
|
172
|
He Y, Mohamedali A, Huang C, Baker MS, Nice EC. Oncoproteomics: Current status and future opportunities. Clin Chim Acta 2019; 495:611-624. [PMID: 31176645 DOI: 10.1016/j.cca.2019.06.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/05/2019] [Accepted: 06/05/2019] [Indexed: 02/07/2023]
Abstract
Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.
Collapse
Affiliation(s)
- Yujia He
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, PR China
| | - Abidali Mohamedali
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, New South Wales 2109, Australia
| | - Canhua Huang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, PR China
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, New South Wales 2109, Australia.
| | - Edouard C Nice
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu 610041, PR China; Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, New South Wales 2109, Australia; Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia.
| |
Collapse
|
173
|
Vizirianakis IS, Miliotou AN, Mystridis GA, Andriotis EG, Andreadis II, Papadopoulou LC, Fatouros DG. Tackling pharmacological response heterogeneity by PBPK modeling to advance precision medicine productivity of nanotechnology and genomics therapeutics. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2019. [DOI: 10.1080/23808993.2019.1605828] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Ioannis S. Vizirianakis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Androulla N. Miliotou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George A. Mystridis
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleftherios G. Andriotis
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ioannis I. Andreadis
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Lefkothea C. Papadopoulou
- Laboratory of Pharmacology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitrios G. Fatouros
- Laboratory of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
174
|
Aiello M, Cavaliere C, D'Albore A, Salvatore M. The Challenges of Diagnostic Imaging in the Era of Big Data. J Clin Med 2019; 8:E316. [PMID: 30845692 PMCID: PMC6463157 DOI: 10.3390/jcm8030316] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 02/27/2019] [Accepted: 02/28/2019] [Indexed: 01/08/2023] Open
Abstract
The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. Nevertheless, the mere production of a large amount of data does not automatically permit the real exploitation of their intrinsic value. Therefore, it is necessary to develop digital platforms and applications that favor the correct and advantageous management of diagnostic images such as Big data. This work aims to frame the role of diagnostic imaging in this new scenario, emphasizing the open challenges in exploiting such intense data generation for decision making with Big data analytics.
Collapse
Affiliation(s)
- Marco Aiello
- IRCCS SDN, Via Gianturco 113, Napoli 80143, Italy.
| | | | | | | |
Collapse
|
175
|
Hulsen T. An overview of publicly available patient-centered prostate cancer datasets. Transl Androl Urol 2019; 8:S64-S77. [PMID: 31143673 PMCID: PMC6511704 DOI: 10.21037/tau.2019.03.01] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 02/27/2019] [Indexed: 02/05/2023] Open
Abstract
Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.
Collapse
Affiliation(s)
- Tim Hulsen
- Department of Professional Health Solutions & Services, Philips Research, Eindhoven, The Netherlands
| |
Collapse
|