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      Federated Learning for Healthcare Informatics

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          Abstract

          With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies, and pharmaceutical industries, among others. This access provides an unprecedented opportunity for data science technologies to derive data-driven insights and improve the quality of care delivery. Healthcare data, however, are usually fragmented and private making it difficult to generate robust results across populations. For example, different hospitals own the electronic health records (EHR) of different patient populations and these records are difficult to share across hospitals because of their sensitive nature. This creates a big barrier for developing effective analytical approaches that are generalizable, which need diverse, “big data.” Federated learning, a mechanism of training a shared global model with a central server while keeping all the sensitive data in local institutions where the data belong, provides great promise to connect the fragmented healthcare data sources with privacy-preservation. The goal of this survey is to provide a review for federated learning technologies, particularly within the biomedical space. In particular, we summarize the general solutions to the statistical challenges, system challenges, and privacy issues in federated learning, and point out the implications and potentials in healthcare.

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          Most cited references26

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Is Open Access

            MIMIC-III, a freely accessible critical care database

            MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
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              Dissecting racial bias in an algorithm used to manage the health of populations

              Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.
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                Author and article information

                Contributors
                few2001@med.cornell.edu
                Journal
                J Healthc Inform Res
                J Healthc Inform Res
                Journal of Healthcare Informatics Research
                Springer International Publishing (Cham )
                2509-4971
                2509-498X
                12 November 2020
                : 1-19
                Affiliations
                [1 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Population Health Sciences, , Weill Cornell Medicine, ; New York, NY USA
                [2 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Institute for Digital Health, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [3 ]GRID grid.420391.d, ISNI 0000 0004 0478 6223, U.S. Department of Defense Joint Artificial Intelligence Center, ; Washington, D.C., USA
                [4 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Health Outcomes and Biomedical Informatics, College of Medicine, , University of Florida, ; Gainesville, FL USA
                Article
                82
                10.1007/s41666-020-00082-4
                7659898
                33204939
                9929c261-c064-4d21-a597-2b4c5b9613f3
                © Springer Nature Switzerland AG 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 19 August 2020
                : 21 October 2020
                : 30 October 2020
                Categories
                Research Article

                federated learning,healthcare,privacy
                federated learning, healthcare, privacy

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