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Amiri R, Razmara J, Parvizpour S, Izadkhah H. A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks. BMC Bioinformatics 2023; 24:442. [PMID: 37993777 PMCID: PMC10664633 DOI: 10.1186/s12859-023-05572-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
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Affiliation(s)
- Ramin Amiri
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Habib Izadkhah
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
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Datta A, Flynn NR, Barnette DA, Woeltje KF, Miller GP, Swamidass SJ. Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort. PLoS Comput Biol 2021; 17:e1009053. [PMID: 34228716 PMCID: PMC8284671 DOI: 10.1371/journal.pcbi.1009053] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 07/16/2021] [Accepted: 05/08/2021] [Indexed: 01/14/2023] Open
Abstract
Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).
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Affiliation(s)
- Arghya Datta
- Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, Missouri, United States of America
| | - Noah R. Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Dustyn A. Barnette
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Keith F. Woeltje
- Department of Internal Medicine, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Center for Clinical Excellence at BJC HealthCare, Saint Louis, Missouri, United States of America
| | - Grover P. Miller
- Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - S. Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
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Moreland-Head LN, Coons JC, Seybert AL, Gray MP, Kane-Gill SL. Use of Disproportionality Analysis to Identify Previously Unknown Drug-Associated Causes of Cardiac Arrhythmias Using the Food and Drug Administration Adverse Event Reporting System (FAERS) Database. J Cardiovasc Pharmacol Ther 2021; 26:341-348. [PMID: 33403858 DOI: 10.1177/1074248420984082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Drug-induced QTc-prolongation is a well-known adverse drug reaction (ADR), however there is limited knowledge of other drug-induced arrhythmias. PURPOSE The objective of this study is to determine the drugs reported to be associated with arrhythmias other than QTc-prolongation using the FAERS database, possibly identifying potential drug causes that have not been reported previously. METHODS FAERS reports from 2004 quarter 1 through 2019 quarter 1 were combined to create a dataset of approximately 11.6 million reports. Search terms for arrhythmias of interest were selected from the Standardized MedDRA Queries (SMQ) Version 12.0. Frequency of the cardiac arrhythmias were determined for atrial fibrillation, atrioventricular block, bradyarrhythmia, bundle branch block, supraventricular tachycardia, and ventricular fibrillation and linked to the reported causal medications. Reports were further categorized by prior evidence associations using package inserts and established drug databases. A reporting odds ratio (ROR) and confidence interval (CI) were calculated for the ADRs for each drug and each of the 6 cardiac arrhythmias. RESULTS Of the 11.6 million reports in the FAERS database, 68,989 were specific to cardiac arrhythmias of interest. There were 61 identified medication-reported arrhythmia pairs for the 6 arrhythmia groups with 33 found to have an unknown reported association. Rosiglitazone was the most frequently medication reported across all arrhythmias [ROR 6.02 (CI: 5.82-6.22)]. Other medications with significant findings included: rofecoxib, digoxin, alendronate, lenalidomide, dronedarone, zoledronic acid, adalimumab, dabigatran, and interferon beta-1b. CONCLUSION Upon retrospective analysis of the FAERS database, the majority of drug-associated arrhythmias reported were unknown suggesting new potential drug causes. Cardiac arrhythmias other than QTc prolongation are a new area of focus for pharmacovigilance and medication safety. Consideration of future studies should be given to using the FAERS database as a timely pharmacovigilance tool to identify unknown adverse events of medications.
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Affiliation(s)
| | - James C Coons
- Department of Pharmacy, 6595UPMC Presbyterian Hospital, Pittsburgh, PA, USA.,Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA, USA
| | - Amy L Seybert
- Department of Pharmacy, 6595UPMC Presbyterian Hospital, Pittsburgh, PA, USA.,Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA, USA
| | - Matthew P Gray
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA, USA
| | - Sandra L Kane-Gill
- Department of Pharmacy, 6595UPMC Presbyterian Hospital, Pittsburgh, PA, USA.,Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, PA, USA
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Ibrahim H, El Kerdawy AM, Abdo A, Sharaf Eldin A. Similarity-based machine learning framework for predicting safety signals of adverse drug–drug interactions. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100699] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
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Adverse Event Circumstances and the Case of Drug Interactions. Healthcare (Basel) 2019; 7:healthcare7010045. [PMID: 30893930 PMCID: PMC6473808 DOI: 10.3390/healthcare7010045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 01/04/2023] Open
Abstract
Adverse events are a common and for the most part unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs but also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets, and efficacious combination therapies. Inspired by the potential applications of this approach, we first examined adverse event circumstances reported in FAERS and then performed a molecular level interrogation of cancer patient adverse events to investigate the prevalence of drug-drug interactions in the context of patient responses. We discuss avoidable and/or preventable cases and how molecular analytics can help optimize therapeutic use of co-medications. While up to one out of three adverse events in this dataset might be explicable by iatrogenic, patient, and product/device related factors, almost half of the patients in FAERS received multiple drugs and one in four may have experienced effects attributable to drug interactions.
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Song D, Chen Y, Min Q, Sun Q, Ye K, Zhou C, Yuan S, Sun Z, Liao J. Similarity-based machine learning support vector machine predictor of drug-drug interactions with improved accuracies. J Clin Pharm Ther 2018; 44:268-275. [PMID: 30565313 DOI: 10.1111/jcpt.12786] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 10/29/2018] [Accepted: 11/18/2018] [Indexed: 12/12/2022]
Affiliation(s)
- Dalong Song
- Guizhou University; Guiyang China
- Department of Urology; GuiZhou Provincial People’s Hospital; Guiyang China
| | - Yao Chen
- School of Science; China Pharmaceutical University; Nanjing China
| | - Qian Min
- School of Science; China Pharmaceutical University; Nanjing China
| | - Qingrong Sun
- School of Science; China Pharmaceutical University; Nanjing China
| | - Kai Ye
- MandalaT Software Corporation, F5; Wuxi China
| | - Changjiang Zhou
- School of Science; China Pharmaceutical University; Nanjing China
| | - Shengyue Yuan
- School of Science; China Pharmaceutical University; Nanjing China
| | - Zhaolin Sun
- Department of Urology; GuiZhou Provincial People’s Hospital; Guiyang China
| | - Jun Liao
- School of Science; China Pharmaceutical University; Nanjing China
- Key Laboratory of Drug Quality Control and Pharmacovigilance (China Pharmaceutical University); Ministry of Education; Nanjing China
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Vilar S, Friedman C, Hripcsak G. Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Brief Bioinform 2018; 19:863-877. [PMID: 28334070 PMCID: PMC6454455 DOI: 10.1093/bib/bbx010] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 12/28/2016] [Indexed: 11/13/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University, New York, USA
- Department of Organic Chemistry, University of Santiago de Compostela, Spain
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
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Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 2017; 22:1680-1685. [PMID: 28881183 DOI: 10.1016/j.drudis.2017.08.010] [Citation(s) in RCA: 262] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 07/13/2017] [Accepted: 08/30/2017] [Indexed: 01/29/2023]
Abstract
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.
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Affiliation(s)
- Lu Zhang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China
| | - Jianjun Tan
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China.
| | - Dan Han
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China
| | - Hao Zhu
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA; The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA.
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Vilar S, Hripcsak G. The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions. Brief Bioinform 2017; 18:670-681. [PMID: 27273288 PMCID: PMC6078166 DOI: 10.1093/bib/bbw048] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Revised: 04/18/2016] [Indexed: 12/30/2022] Open
Abstract
Explosion of the availability of big data sources along with the development in computational methods provides a useful framework to study drugs' actions, such as interactions with pharmacological targets and off-targets. Databases related to protein interactions, adverse effects and genomic profiles are available to be used for the construction of computational models. In this article, we focus on the description of biological profiles for drugs that can be used as a system to compare similarity and create methods to predict and analyze drugs' actions. We highlight profiles constructed with different biological data, such as target-protein interactions, gene expression measurements, adverse effects and disease profiles. We focus on the discovery of new targets or pathways for drugs already in the pharmaceutical market, also called drug repurposing, in the interaction with off-targets responsible for adverse reactions and in drug-drug interaction analysis. The current and future applications, strengths and challenges facing all these methods are also discussed. Biological profiles or signatures are an important source of data generation to deeply analyze biological actions with important implications in drug-related studies.
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Affiliation(s)
- Santiago Vilar
- Corresponding author: Santiago Vilar, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA. E-mail: ; George Hripcsak, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA. E-mail:
| | - George Hripcsak
- Corresponding author: Santiago Vilar, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA. E-mail: ; George Hripcsak, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY 10032, USA. E-mail:
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Hameed PN, Verspoor K, Kusljic S, Halgamuge S. Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes. BMC Bioinformatics 2017; 18:140. [PMID: 28249566 PMCID: PMC5333429 DOI: 10.1186/s12859-017-1546-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 02/13/2017] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development.
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Affiliation(s)
- Pathima Nusrath Hameed
- Department of Mechanical Engineering, University of Melbourne, Parkville, Melbourne, 3010, Australia. .,Data61, Victoria Research Lab, West Melbourne, 3003, Australia. .,Department of Computer Science, University of Ruhuna, Matara, 81000, Sri Lanka.
| | - Karin Verspoor
- Department of Computing and Information Systems, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Snezana Kusljic
- Department of Nursing, University of Melbourne, Parkville, Melbourne, 3010, Australia.,The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Melbourne, 3010, Australia
| | - Saman Halgamuge
- Research School of Engineering, College of Engineering and Computer Science, The Australian National University, Canberra, 2601, ACT, Australia
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Jannot AS, Zapletal E, Avillach P, Mamzer MF, Burgun A, Degoulet P. The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience. Int J Med Inform 2017; 102:21-28. [PMID: 28495345 DOI: 10.1016/j.ijmedinf.2017.02.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 02/11/2017] [Indexed: 12/25/2022]
Abstract
BACKGROUND When developed jointly with clinical information systems, clinical data warehouses (CDWs) facilitate the reuse of healthcare data and leverage clinical research. OBJECTIVE To describe both data access and use for clinical research, epidemiology and health service research of the "Hôpital Européen Georges Pompidou" (HEGP) CDW. METHODS The CDW has been developed since 2008 using an i2b2 platform. It was made available to health professionals and researchers in October 2010. Procedures to access data have been implemented and different access levels have been distinguished according to the nature of queries. RESULTS As of July 2016, the CDW contained the consolidated data of over 860,000 patients followed since the opening of the HEGP hospital in July 2000. These data correspond to more than 122 million clinical item values, 124 million biological item values, and 3.7 million free text reports. The ethics committee of the hospital evaluates all CDW projects that generate secondary data marts. Characteristics of the 74 research projects validated between January 2011 and December 2015 are described. CONCLUSION The use of HEGP CDWs is a key facilitator for clinical research studies. It required however important methodological and organizational support efforts from a biomedical informatics department.
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Affiliation(s)
- Anne-Sophie Jannot
- Paris Descartes Faculty of Medicine, Paris, France; INSERM UMR 1138-E22: Information Sciences to Support Personalized Medicine, Paris, France; Medical Informatics, Biostatistics and Public Health Department, Georges Pompidou University Hospital, Paris, France.
| | - Eric Zapletal
- Medical Informatics, Biostatistics and Public Health Department, Georges Pompidou University Hospital, Paris, France
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Marie-France Mamzer
- Paris Descartes Faculty of Medicine, Paris, France; INSERM EA 4569 Medical Ethics Department
| | - Anita Burgun
- Paris Descartes Faculty of Medicine, Paris, France; INSERM UMR 1138-E22: Information Sciences to Support Personalized Medicine, Paris, France; Medical Informatics, Biostatistics and Public Health Department, Georges Pompidou University Hospital, Paris, France
| | - Patrice Degoulet
- Paris Descartes Faculty of Medicine, Paris, France; INSERM UMR 1138-E22: Information Sciences to Support Personalized Medicine, Paris, France; Medical Informatics, Biostatistics and Public Health Department, Georges Pompidou University Hospital, Paris, France
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Rumsfeld JS, Joynt KE, Maddox TM. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol 2016; 13:350-9. [PMID: 27009423 DOI: 10.1038/nrcardio.2016.42] [Citation(s) in RCA: 177] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The potential for big data analytics to improve cardiovascular quality of care and patient outcomes is tremendous. However, the application of big data in health care is at a nascent stage, and the evidence to date demonstrating that big data analytics will improve care and outcomes is scant. This Review provides an overview of the data sources and methods that comprise big data analytics, and describes eight areas of application of big data analytics to improve cardiovascular care, including predictive modelling for risk and resource use, population management, drug and medical device safety surveillance, disease and treatment heterogeneity, precision medicine and clinical decision support, quality of care and performance measurement, and public health and research applications. We also delineate the important challenges for big data applications in cardiovascular care, including the need for evidence of effectiveness and safety, the methodological issues such as data quality and validation, and the critical importance of clinical integration and proof of clinical utility. If big data analytics are shown to improve quality of care and patient outcomes, and can be successfully implemented in cardiovascular practice, big data will fulfil its potential as an important component of a learning health-care system.
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Affiliation(s)
- John S Rumsfeld
- University of Colorado School of Medicine, 13001 East 17th Place, Aurora, Colorado 80045, USA.,VA Eastern Colorado Health System, Cardiology (111B), 1055 Clermont Street, Denver, Colorado 80220, USA
| | - Karen E Joynt
- Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts 02115, USA.,Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, USA
| | - Thomas M Maddox
- University of Colorado School of Medicine, 13001 East 17th Place, Aurora, Colorado 80045, USA.,VA Eastern Colorado Health System, Cardiology (111B), 1055 Clermont Street, Denver, Colorado 80220, USA
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