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  • 1
    Online Resource
    Online Resource
    Federated learning for preserving data privacy in collaborative healthcare research
    SAGE Publications ; 2022
    In:  DIGITAL HEALTH Vol. 8 ( 2022-01), p. 205520762211344-
    Details
    In: DIGITAL HEALTH, SAGE Publications, Vol. 8 ( 2022-01), p. 205520762211344-
    Abstract: Generalizability, external validity, and reproducibility are high priorities for artificial intelligence applications in healthcare. Traditional approaches to addressing these elements involve sharing patient data between institutions or practice settings, which can compromise data privacy (individuals’ right to prevent the sharing and disclosure of information about themselves) and data security (simultaneously preserving confidentiality, accuracy, fidelity, and availability of data). This article describes insights from real-world implementation of federated learning techniques that offer opportunities to maintain both data privacy and availability via collaborative machine learning that shares knowledge, not data. Local models are trained separately on local data. As they train, they send local model updates (e.g. coefficients or gradients) for consolidation into a global model. In some use cases, global models outperform local models on new, previously unseen local datasets, suggesting that collaborative learning from a greater number of examples, including a greater number of rare cases, may improve predictive performance. Even when sharing model updates rather than data, privacy leakage can occur when adversaries perform property or membership inference attacks which can be used to ascertain information about the training set. Emerging techniques mitigate risk from adversarial attacks, allowing investigators to maintain both data privacy and availability in collaborative healthcare research. When data heterogeneity between participating centers is high, personalized algorithms may offer greater generalizability by improving performance on data from centers with proportionately smaller training sample sizes. Properly applied, federated learning has the potential to optimize the reproducibility and performance of collaborative learning while preserving data security and privacy.
    Type of Medium: Online Resource
    ISSN: 2055-2076 , 2055-2076
    URL: Article
    DOI: 10.1177/20552076221134455
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2022
    detail.hit.zdb_id: 2819396-9
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  • 2
    Online Resource
    Online Resource
    Building an automated, machine learning-enabled platform for predicting post-operative complications
    IOP Publishing ; 2023
    In:  Physiological Measurement Vol. 44, No. 2 ( 2023-02-01), p. 024001-
    Details
    In: Physiological Measurement, IOP Publishing, Vol. 44, No. 2 ( 2023-02-01), p. 024001-
    Abstract: Objective . In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record. Approach . This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics. Main Results and Significance . This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.
    Type of Medium: Online Resource
    ISSN: 0967-3334 , 1361-6579
    URL: Article
    DOI: 10.1088/1361-6579/acb4db
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 2002076-4
    SSG: 11
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  • 3
    Online Resource
    Online Resource
    Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review
    JMIR Publications Inc. ; 2023
    In:  JMIR Medical Informatics Vol. 11 ( 2023-8-24), p. e48297-e48297
    Details
    In: JMIR Medical Informatics, JMIR Publications Inc., Vol. 11 ( 2023-8-24), p. e48297-e48297
    Abstract: Machine learning–enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable. Objective This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs. Methods Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system’s functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems. Results A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy. Conclusions Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.
    Type of Medium: Online Resource
    ISSN: 2291-9694
    URL: Article
    DOI: 10.2196/48297
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2023
    detail.hit.zdb_id: 2798261-0
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  • 4
    Online Resource
    Online Resource
    Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System
    Ovid Technologies (Wolters Kluwer Health) ; 2023
    In:  Journal of the American College of Surgeons Vol. 236, No. 2 ( 2023-02), p. 279-291
    Details
    In: Journal of the American College of Surgeons, Ovid Technologies (Wolters Kluwer Health), Vol. 236, No. 2 ( 2023-02), p. 279-291
    Abstract: In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system. STUDY DESIGN: This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs). RESULTS: Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K] , p 〈 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2] , p 〈 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p 〈 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K] ); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0] , p 〈 0.001). CONCLUSIONs: Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.
    Type of Medium: Online Resource
    ISSN: 1072-7515
    URL: Article
    DOI: 10.1097/XCS.0000000000000471
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2023
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