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Trunnell M, Frankenberger C, Hota B, Hughes T, Martinov P, Ravichandran U, Shah NS, Grossman RL. The Pandemic Response Commons. JAMIA Open 2024; 7:ooae025. [PMID: 38617994 PMCID: PMC11009464 DOI: 10.1093/jamiaopen/ooae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/25/2023] [Accepted: 04/05/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives A data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons (PRC) is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. Methods The PRC was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the PRC. Results The consortium developed a general PRC and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures, and controls for a NIST SP 800-53 revision 4 Moderate system. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Discussion and conclusions Given the various legal and data agreements required to operate a data commons, a PRC is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a PRC can also be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing regional data.
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Affiliation(s)
| | | | - Bala Hota
- Rush University Medical Center, Chicago, IL 60612, United States
| | - Troy Hughes
- Center for Translational Data Science, University of Chicago, Chicago, IL 60615, United States
| | | | | | - Nirav S Shah
- NorthShore University HealthSystem, Evanston, IL 60201, United States
| | - Robert L Grossman
- Center for Translational Data Science, University of Chicago, Chicago, IL 60615, United States
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ELBERS DC, FILLMORE NR, LA J, TOSI HM, AJJARAPU S, DHOND R, MURRAY K, VALLEY D, SHANNON C, BROPHY MT, DO NV. Building Research Infrastructure to Develop Greater Learning Efficiencies (BRIDGE). Stud Health Technol Inform 2024; 310:1131-1135. [PMID: 38269991 PMCID: PMC11166042 DOI: 10.3233/shti231141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
In this manuscript, we outline our developed version of a Learning Health System (LHS) in oncology implemented at the Department of Veterans Affairs (VA). Transferring healthcare into an LHS framework has been one of the spearpoints of VA's Central Office and given the general lack of evidence generated through randomized control clinical trials to guide medical decisions in oncology, this domain is one of the most suitable for this change. We describe our technical solution, which includes a large real-world data repository, a data science and algorithm development framework, and the mechanism by which results are brought back to the clinic and to the patient. Additionally, we propose the need for a bridging framework that requires collaboration between informatics specialists and medical professionals to integrate knowledge generation into the clinical workflow at the point of care.
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Affiliation(s)
- Danne C ELBERS
- VA Boston Healthcare System, Boston MA, USA
- Harvard Medical School, Boston MA, USA
| | - Nathanael R FILLMORE
- VA Boston Healthcare System, Boston MA, USA
- Harvard Medical School, Boston MA, USA
| | | | | | - Samuel AJJARAPU
- VA Boston Healthcare System, Boston MA, USA
- Harvard Medical School, Boston MA, USA
| | - Rupali DHOND
- VA Boston Healthcare System, Boston MA, USA
- Boston University School of Medicine, Boston MA, USA
| | | | | | | | - Mary T BROPHY
- VA Boston Healthcare System, Boston MA, USA
- Boston University School of Medicine, Boston MA, USA
| | - Nhan V DO
- VA Boston Healthcare System, Boston MA, USA
- Boston University School of Medicine, Boston MA, USA
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Hernandez KM, Venkat A, Elbers DC, Bihn JR, Brophy MT, Do NV, La J, Liu Q, Prokhorenkov A, Metoki-Shlubsky N, Sung FC, Paller CJ, Fillmore NR, Grossman RL. Prostate cancer patient stratification by molecular signatures in the Veterans Precision Oncology Data Commons. Cold Spring Harb Mol Case Stud 2023; 9:a006298. [PMID: 38050021 PMCID: PMC10815278 DOI: 10.1101/mcs.a006298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/31/2023] [Indexed: 12/06/2023] Open
Abstract
Veterans are at an increased risk for prostate cancer, a disease with extraordinary clinical and molecular heterogeneity, compared with the general population. However, little is known about the underlying molecular heterogeneity within the veteran population and its impact on patient management and treatment. Using clinical and targeted tumor sequencing data from the National Veterans Affairs health system, we conducted a retrospective cohort study on 45 patients with advanced prostate cancer in the Veterans Precision Oncology Data Commons (VPODC), most of whom were metastatic castration-resistant. We characterized the mutational burden in this cohort and conducted unsupervised clustering analysis to stratify patients by molecular alterations. Veterans with prostate cancer exhibited a mutational landscape broadly similar to prior studies, including KMT2A and NOTCH1 mutations associated with neuroendocrine prostate cancer phenotype, previously reported to be enriched in veterans. We also identified several potential novel mutations in PTEN, MSH6, VHL, SMO, and ABL1 Hierarchical clustering analysis revealed two subgroups containing therapeutically targetable molecular features with novel mutational signatures distinct from those reported in the Catalogue of Somatic Mutations in Cancer database. The clustering approach presented in this study can potentially be used to clinically stratify patients based on their distinct mutational profiles and identify actionable somatic mutations for precision oncology.
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Affiliation(s)
| | - Aarti Venkat
- Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
- Center for Translational Data Science, University of Chicago, Chicago, Illinois 60637, USA
| | - Danne C Elbers
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - John R Bihn
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
| | - Mary T Brophy
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Nhan V Do
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
- Chobanian and Avedisian School of Medicine, Boston University, Boston, Massachusetts 02118, USA
| | - Jennifer La
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Qiong Liu
- Frederick National laboratory for Cancer Research, Frederick, Maryland 21701, USA
| | - Andrew Prokhorenkov
- Center for Translational Data Science, University of Chicago, Chicago, Illinois 60637, USA
| | - Noah Metoki-Shlubsky
- Center for Translational Data Science, University of Chicago, Chicago, Illinois 60637, USA
| | - Feng-Chi Sung
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA
| | - Channing J Paller
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA;
| | - Nathanael R Fillmore
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts 02130, USA;
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Robert L Grossman
- Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA
- Center for Translational Data Science, University of Chicago, Chicago, Illinois 60637, USA
- Open Commons Consortium, Chicago, Illinois 60611, USA
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Placido D, Yuan B, Hjaltelin JX, Zheng C, Haue AD, Chmura PJ, Yuan C, Kim J, Umeton R, Antell G, Chowdhury A, Franz A, Brais L, Andrews E, Marks DS, Regev A, Ayandeh S, Brophy MT, Do NV, Kraft P, Wolpin BM, Rosenthal MH, Fillmore NR, Brunak S, Sander C. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med 2023; 29:1113-1122. [PMID: 37156936 PMCID: PMC10202814 DOI: 10.1038/s41591-023-02332-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 03/31/2023] [Indexed: 05/10/2023]
Abstract
Pancreatic cancer is an aggressive disease that typically presents late with poor outcomes, indicating a pronounced need for early detection. In this study, we applied artificial intelligence methods to clinical data from 6 million patients (24,000 pancreatic cancer cases) in Denmark (Danish National Patient Registry (DNPR)) and from 3 million patients (3,900 cases) in the United States (US Veterans Affairs (US-VA)). We trained machine learning models on the sequence of disease codes in clinical histories and tested prediction of cancer occurrence within incremental time windows (CancerRiskNet). For cancer occurrence within 36 months, the performance of the best DNPR model has area under the receiver operating characteristic (AUROC) curve = 0.88 and decreases to AUROC (3m) = 0.83 when disease events within 3 months before cancer diagnosis are excluded from training, with an estimated relative risk of 59 for 1,000 highest-risk patients older than age 50 years. Cross-application of the Danish model to US-VA data had lower performance (AUROC = 0.71), and retraining was needed to improve performance (AUROC = 0.78, AUROC (3m) = 0.76). These results improve the ability to design realistic surveillance programs for patients at elevated risk, potentially benefiting lifespan and quality of life by early detection of this aggressive cancer.
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Affiliation(s)
- Davide Placido
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bo Yuan
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jessica X Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chunlei Zheng
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Amalie D Haue
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Piotr J Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chen Yuan
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jihye Kim
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Renato Umeton
- Dana-Farber Cancer Institute, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Weill Cornell Medicine, New York City, NY, USA
| | | | | | - Alexandra Franz
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Boston, MA, USA
- Genentech, Inc., South San Francisco, CA, USA
| | | | - Mary T Brophy
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Nhan V Do
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Peter Kraft
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brian M Wolpin
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Michael H Rosenthal
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Nathanael R Fillmore
- Harvard Medical School, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- VA Boston Healthcare System, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
| | - Chris Sander
- Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Boston, MA, USA.
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Bridging gaps between images and data: a systematic update on imaging biobanks. Eur Radiol 2022; 32:3173-3186. [PMID: 35001159 DOI: 10.1007/s00330-021-08431-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/01/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND OBJECTIVE The systematic collection of medical images combined with imaging biomarkers and patient non-imaging data is the core concept of imaging biobanks, a key element for fuelling the development of modern precision medicine. Our purpose is to review the existing image repositories fulfilling the criteria for imaging biobanks. METHODS Pubmed, Scopus and Web of Science were searched for articles published in English from January 2010 to July 2021 using a combination of the terms: "imaging" AND "biobanks" and "imaging" AND "repository". Moreover, the Community Research and Development Information Service (CORDIS) database ( https://cordis.europa.eu/projects ) was searched using the terms: "imaging" AND "biobanks", also including collections, projects, project deliverables, project publications and programmes. RESULTS Of 9272 articles retrieved, only 54 related to biobanks containing imaging data were finally selected, of which 33 were disease-oriented (61.1%) and 21 population-based (38.9%). Most imaging biobanks were European (26/54, 48.1%), followed by American biobanks (20/54, 37.0%). Among disease-oriented biobanks, the majority were focused on neurodegenerative (9/33, 27.3%) and oncological diseases (9/33, 27.3%). The number of patients enrolled ranged from 240 to 3,370,929, and the imaging modality most frequently involved was MRI (40/54, 74.1%), followed by CT (20/54, 37.0%), PET (13/54, 24.1%), and ultrasound (12/54, 22.2%). Most biobanks (38/54, 70.4%) were accessible under request. CONCLUSIONS Imaging biobanks can serve as a platform for collecting, sharing and analysing medical images integrated with imaging biomarkers, biological and clinical data. A relatively small proportion of current biobanks also contain images and can thus be classified as imaging biobanks. KEY POINTS • Imaging biobanks are a powerful tool for large-scale collection and processing of medical images integrated with imaging biomarkers and patient non-imaging data. • Most imaging biobanks retrieved were European, disease-oriented and accessible under user request. • While many biobanks have been developed so far, only a relatively small proportion of them are imaging biobanks.
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Dhond R, Elbers D, Majahalme N, Dipietro S, Goryachev S, Acher R, Leatherman S, Anglin-Foote T, Liu Q, Su S, Seerapu R, Hall R, Ferguson R, Brophy MT, Ferraro J, DuVall SL, Do NV. ProjectFlow: a configurable workflow management application for point of care research. JAMIA Open 2021; 4:ooab074. [PMID: 34485848 DOI: 10.1093/jamiaopen/ooab074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 05/21/2021] [Accepted: 08/16/2021] [Indexed: 11/12/2022] Open
Abstract
Objective To best meet our point-of-care research (POC-R) needs, we developed ProjectFlow, a configurable, clinical research workflow management application. In this article, we describe ProjectFlow and how it is used to manage study processes for the Diuretic Comparison Project (DCP) and the Research Precision Oncology Program (RePOP). Materials and methods The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. ProjectFlow is a flexible web-based workflow management tool specifically created to facilitate conduct of our clinical research initiatives within the VHA. The application was developed using the Grails web framework and allows researchers to create custom workflows using Business Process Model and Notation. Results As of January 2021, ProjectFlow has facilitated management of study recruitment, enrollment, randomization, and drug orders for over 10 000 patients for the DCP clinical trial. It has also helped us evaluate over 3800 patients for recruitment and enroll over 370 of them into RePOP for use in data sharing partnerships and predictive analytics aimed at optimizing cancer treatment in the VHA. Discussion The POC-R study design embeds research processes within day-to-day clinical care and leverages longitudinal electronic health record (EHR) data for study recruitment, monitoring, and outcome reporting. Software that allows flexibility in study workflow creation and integrates with enterprise EHR systems is critical to the success of POC-R. Conclusions We developed a flexible web-based informatics solution called ProjectFlow that supports custom research workflow configuration and has ability to integrate data from existing VHA EHR systems.
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Affiliation(s)
- Rupali Dhond
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Danne Elbers
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | | | - Ryan Acher
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | | | - Qingzhu Liu
- VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
| | - Shaoyu Su
- VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
| | - Ramana Seerapu
- VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
| | - Robert Hall
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Ryan Ferguson
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Mary T Brophy
- VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Jeff Ferraro
- VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
| | - Scott L DuVall
- VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA
| | - Nhan V Do
- VA Boston Healthcare System, Boston, Massachusetts, USA
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Yang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Healthcare. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5812499. [PMID: 34527076 PMCID: PMC8437645 DOI: 10.1155/2021/5812499] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/09/2021] [Indexed: 01/07/2023]
Abstract
Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.
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Affiliation(s)
- Yan Cheng Yang
- Foreign Language Department, Luoyang Institute of Science and Technology, Luoyang, Henan, China
- Foreign Language Department/Language and Cognition Center, Hunan University, Changsha, Hunan, China
| | - Saad Ul Islam
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Asra Noor
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Sadia Khan
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Waseem Afsar
- Department of Computer Science, University of Swabi, Swabi, Pakistan
| | - Shah Nazir
- Department of Computer Science, University of Swabi, Swabi, Pakistan
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