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      Will artificial intelligence widen the therapeutic gap between children and adults?

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      Author(s): 1 , , 2 , 3 , 4 , 1 , 5
      Publication date (Electronic):
      Journal: Pediatric Investigation
      Publisher: John Wiley and Sons Inc.

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          Dissecting racial bias in an algorithm used to manage the health of populations

          Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)
          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.
          Article 0 comments Cited 893 times – based on 0 reviews     Review now
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            Scalable and accurate deep learning with electronic health records

            Alvin Rajkomar, Eyal Oren, Kai Chen … (2018)
            Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart.
            Article 0 comments Cited 406 times – based on 0 reviews     Review now
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              Privacy in the age of medical big data

              W Nicholson Price, I. Glenn Cohen (2019)
              Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system.
              Article 0 comments Cited 201 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Matthew Nagy:
                ORCID: https://orcid.org/0000-0001-5087-6674
                mrn32@case.edu
                Journal
                Journal ID (nlm-ta): Pediatr Investig
                Journal ID (iso-abbrev): Pediatr Investig
                Journal ID (doi): 10.1002/(ISSN)2574-2272
                Journal ID (publisher-id): PED4
                Title: Pediatric Investigation
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Print): 2096-3726
                ISSN (Electronic): 2574-2272
                Publication date (Electronic): 01 December 2023
                Publication date Collection: March 2024
                Volume: 8
                Issue: 1 ( doiID: 10.1002/ped4.v8.1 )
                Pages: 1-6
                Affiliations
                [ 1 ] Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio USA
                [ 2 ] Department of Pediatrics Division of Hematology/Oncology Washington University School of Medicine St. Louis Missouri USA
                [ 3 ] Department of Medicine Bioethics Research Center Washington University School of Medicine St. Louis Missouri USA
                [ 4 ] Institute for Informatics Washington University School of Medicine St. Louis Missouri USA
                [ 5 ] Department of Pediatric Hematology Oncology and Blood and Marrow Transplantation Cleveland Clinic Children's Cleveland Ohio USA
                Author notes
                [*] [* ] Correspondence

                Matthew Nagy, Cleveland Clinic Lerner College Medicine, Case Western Reserve University, Cleveland, OH, USA.

                Email: mrn32@ 123456case.edu

                Author information
                https://orcid.org/0000-0001-5087-6674
                Article
                Publisher ID: PED412407
                DOI: 10.1002/ped4.12407
                PMC ID: 10951493
                PubMed ID: 38516139
                SO-VID: da7474a1-d876-4884-9af1-666443530082
                Copyright © © 2023 Chinese Medical Association. Pediatric Investigation published by John Wiley & Sons Australia, Ltd on behalf of Futang Research Center of Pediatric Development.
                License:

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                Date received : 23 May 2023
                Date accepted : 31 October 2023
                Page count
                Figures: 2, Tables: 0, Pages: 6, Words: 3588
                Categories
                Subject: Commentary
                Subject: Commentary
                Custom metadata
                source-schema-version-number 2.0
                cover-date March 2024
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:20.03.2024

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