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Pham A, Edelson M, Nouri A, Kuo TT. Distributed management of patient data-sharing informed consents for clinical research. Comput Biol Med 2024; 180:108956. [PMID: 39121682 PMCID: PMC11380755 DOI: 10.1016/j.compbiomed.2024.108956] [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: 02/20/2024] [Revised: 07/21/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND The consent protocol is now a critical part in the overall orchestration of clinical research. We aimed to demonstrate the feasibility of an Ethereum-based informed consent system, which includes an immutable and automated channel of consent matching, to simultaneously assure patient privacy and increase the efficiency of researchers' data access. METHOD We simulated a multi-site scenario, each assigned 10000 consent records. A consent record contained one patient's data-sharing preference with regards to seven data categories. We developed a blockchain-based infrastructure with a smart contract to record consents on-chain, and to query consenting patients corresponding to specific criteria. We measured our system's recording efficiency against a baseline design and verified accuracy by testing an exhaustive list of possible queries. RESULTS Our method achieved ∼3-4% lead with an average insertion speed of ∼2 s per record per node on either a 3-, 4- or 5-node network, and 100 % accuracy. It also outperformed other solutions in external validation. DISCUSSION The speed we achieved is reasonable in a real-world system under the realistic assumption that patients may not change their minds too frequently, with the added benefit of immutability. Furthermore, the per-insertion time did improve slightly as the number of network nodes increased, attesting to the benefit of node parallelism as it suggests no attrition of insertion efficiency due to scale of nodes. CONCLUSIONS Our work confirms the technical feasibility of a blockchain-based consent mechanism, assuring patients with an immutable audit trail, and providing researchers with an efficient way to reach their cohorts.
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Affiliation(s)
- Anh Pham
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | - Maxim Edelson
- UCSD Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Armin Nouri
- Department of Biomedical Engineering, Fu Foundation School of Engineering, Columbia University, New York, NY, USA
| | - Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA; Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT, USA; Department of Surgery, School of Medicine, Yale University, New Haven, CT, USA.
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2
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Hallaj S, Chuter BG, Lieu AC, Singh P, Kalpathy-Cramer J, Xu BY, Christopher M, Zangwill LM, Weinreb RN, Baxter SL. Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives. Ophthalmol Glaucoma 2024:S2589-4196(24)00143-1. [PMID: 39214457 DOI: 10.1016/j.ogla.2024.08.004] [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: 06/11/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
CLINICAL RELEVANCE Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of universal diagnostic tools. Developing robust artificial intelligence (AI) models for glaucoma screening is essential for early detection and treatment but faces significant obstacles. Effective deep learning algorithms require large, well-curated datasets from diverse patient populations and imaging protocols. However, creating centralized data repositories is hindered by concerns over data sharing, patient privacy, regulatory compliance, and intellectual property. Federated Learning (FL) offers a potential solution by enabling data to remain locally hosted while facilitating distributed model training across multiple sites. METHODS A comprehensive literature review was conducted on the application of Federated Learning in training AI models for glaucoma screening. Publications from 1950 to 2024 were searched using databases such as PubMed and IEEE Xplore with keywords including "glaucoma," "federated learning," "artificial intelligence," "deep learning," "machine learning," "distributed learning," "privacy-preserving," "data sharing," "medical imaging," and "ophthalmology." Articles were included if they discussed the use of FL in glaucoma-related AI tasks or addressed data sharing and privacy challenges in ophthalmic AI development. RESULTS FL enables collaborative model development without centralizing sensitive patient data, addressing privacy and regulatory concerns. Studies show that FL can improve model performance and generalizability by leveraging diverse datasets while maintaining data security. FL models have achieved comparable or superior accuracy to those trained on centralized data, demonstrating effectiveness in real-world clinical settings. CONCLUSIONS Federated Learning presents a promising strategy to overcome current obstacles in developing AI models for glaucoma screening. By balancing the need for extensive, diverse training data with the imperative to protect patient privacy and comply with regulations, FL facilitates collaborative model training without compromising data security. This approach offers a pathway toward more accurate and generalizable AI solutions for glaucoma detection and management. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Shahin Hallaj
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Benton G Chuter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Alexander C Lieu
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California
| | - Praveer Singh
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado
| | - Benjamin Y Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California.
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Kim K, Kim SM, Park Y, Lee E, Jung S, Kang J, An D, Min K, Shim SR, Yu HW, Han HW. A blockchain-based healthcare data marketplace: prototype and demonstration. JAMIA Open 2024; 7:ooae029. [PMID: 38617993 PMCID: PMC11013391 DOI: 10.1093/jamiaopen/ooae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/17/2024] [Accepted: 03/25/2024] [Indexed: 04/16/2024] Open
Abstract
Objectives This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.
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Affiliation(s)
- KangHyun Kim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - Sung-Min Kim
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - YoungMin Park
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
| | - EunSol Lee
- Department of Development, Medibloc co. Ltd, Seoul, South Korea
| | - SungJae Jung
- Department of Development, Medibloc co. Ltd, Seoul, South Korea
| | - Jeongyong Kang
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - DongUk An
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - Kyungil Min
- Department of Strategic Development, Misoinfo co. Ltd, Seoul, South Korea
| | - Sung Ryul Shim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Hyeong Won Yu
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam-si, 13620, South Korea
| | - Hyun Wook Han
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam-si, 13488, South Korea
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Lacson R, Yu Y, Kuo TT, Ohno-Machado L. Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review. J Am Med Inform Assoc 2024; 31:1423-1435. [PMID: 38726710 PMCID: PMC11105130 DOI: 10.1093/jamia/ocae084] [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: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVE Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics. MATERIALS AND METHODS This systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized. RESULTS Following screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility. DISCUSSION Ethereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other. CONCLUSION While blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.
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Affiliation(s)
- Roger Lacson
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT 06510, United States
| | - Yufei Yu
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
| | - Tsung-Ting Kuo
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT 06510, United States
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
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George W, Al-Ansari T. GM-Ledger: Blockchain-Based Certificate Authentication for International Food Trade. Foods 2023; 12:3914. [PMID: 37959033 PMCID: PMC10648726 DOI: 10.3390/foods12213914] [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: 09/05/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 11/15/2023] Open
Abstract
Maritime transportation plays a critical role for many Arab countries and their food security and has evolved into a complex system that involves a plethora of supply chain stakeholders spread around the globe. This inherent complexity brings huge security challenges, including cargo loss and high burdens in cargo document inspection. The emerging blockchain technology provides a promising tool to build a unified maritime cargo tracking system critical for cargo security. This is because blockchains are a tamper-proof distributed ledger technology that can store and track data in a secure and transparent manner. Using the State of Qatar as a case study, this research introduces the Global Maritime Ledger (GM-Ledger), which will aid authorities in verifying, signing and transacting food certificates in an efficient manner. The methodology of this research includes reviewing past publications, identifying the requirements of various players in the Qatari food import-export industry and then creating a smart contract framework that will efficiently manage the work with necessary human intervention as and when required. The result of this work is the formation of a solid framework that can be employed in future works. This work realized that employing web3 solutions for the food import sector is highly viable and that with the right social, economic and policy reforms, it is possible to transform the entire food system to bear healthy transparency and power balance in global supply chains. In conclusion, this study argues that BCT has the ability to assist the government and other players to minimize fraud and maximize food supply chain stakeholder participation.
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Affiliation(s)
| | - Tareq Al-Ansari
- College of Science and Engineering, Hamad bin Khalifa University, Doha P.O. Box 34110, Qatar;
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Kuo TT, Pham A, Edelson ME, Kim J, Chan J, Gupta Y, Ohno-Machado L. Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions. J Am Med Inform Assoc 2023; 30:1167-1178. [PMID: 36916740 PMCID: PMC10198529 DOI: 10.1093/jamia/ocad049] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/15/2023] Open
Abstract
OBJECTIVE We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION Blockchain technology can be used to support federated data analysis among multiple institutions.
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Affiliation(s)
- Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Anh Pham
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Maxim E Edelson
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
| | - Jihoon Kim
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Jason Chan
- Poway High School, Poway, California, USA
| | - Yash Gupta
- Canyon Crest Academy, San Diego, California, USA
| | - Lucila Ohno-Machado
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
- Division of Health Services Research & Development, VA San Diego Healthcare System, San Diego, California, USA
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA
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Kuo TT, Pham A. Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts. Int J Med Inform 2023; 169:104924. [PMID: 36402113 PMCID: PMC9984225 DOI: 10.1016/j.ijmedinf.2022.104924] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/13/2022] [Accepted: 11/04/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy. OBJECTIVE We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism. METHODS We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets. RESULTS When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001). CONCLUSION By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.
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Affiliation(s)
- Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA.
| | - Anh Pham
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
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Kuo TT, Jiang X, Tang H, Wang X, Harmanci A, Kim M, Post K, Bu D, Bath T, Kim J, Liu W, Chen H, Ohno-Machado L. The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition. J Am Med Inform Assoc 2022; 29:2182-2190. [PMID: 36164820 PMCID: PMC9667175 DOI: 10.1093/jamia/ocac165] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 01/11/2023] Open
Abstract
Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.
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Affiliation(s)
- Tsung-Ting Kuo
- Corresponding Author: Tsung-Ting Kuo, PhD, UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA;
| | | | | | | | - Arif Harmanci
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Miran Kim
- Department of Mathematics, Hanyang University, Seoul, Republic of Korea,Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Kai Post
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Diyue Bu
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, Indiana, USA
| | - Tyler Bath
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Jihoon Kim
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Weijie Liu
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, Indiana, USA
| | - Hongbo Chen
- Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, Indiana, USA
| | - Lucila Ohno-Machado
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA,Division of Health Services Research & Development, Veteran Affairs San Diego Healthcare System, San Diego, California, USA
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