From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-09-15

AUTHORS

Sujay Nagaraj, Vinyas Harish, Liam G. McCoy, Felipe Morgado, Ian Stedman, Stephen Lu, Erik Drysdale, Michael Brudno, Devin Singh

ABSTRACT

Purpose of reviewMachine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technical, and ethical nuances limiting the direct translation of ML tools developed for adults to pediatric populations. To our knowledge, no work has yet focused on outlining the unique considerations that need to be taken into account when designing and implementing ML in pediatrics.Recent findingsThe nature of varying developmental stages and the prominence of family-centered care lead to vastly different data-generating processes in pediatrics. Data heterogeneity and a lack of high-quality pediatric databases further complicate ML research. In order to address some of these nuances, we provide a common pipeline for clinicians and computer scientists to use as a foundation for structuring ML projects, and a framework for the translation of a developed model into clinical practice in pediatrics. Throughout these pathways, we also highlight ethical and legal considerations that must be taken into account when working with pediatric populations and data.SummaryHere, we describe a comprehensive outline of special considerations required of ML in pediatrics from project ideation to implementation. We hope this review can serve as a high-level guideline for ML scientists and clinicians alike to identify applications in the pediatric setting, generate effective ML solutions, and subsequently deliver them to patients, families, and providers. More... »

PAGES

336-349

References to SciGraph publications

  • 2006-08-09. Barriers and facilitators to implementing shared decision-making in clinical practice: a systematic review of health professionals' perceptions in IMPLEMENTATION SCIENCE
  • 2013-11-29. “Many miles to go …”: a systematic review of the implementation of patient decision support interventions into routine clinical practice in BMC MEDICAL INFORMATICS AND DECISION MAKING
  • 2019-04-04. A qualitative review of the design thinking framework in health professions education in BMC MEDICAL EDUCATION
  • 2019-10-29. Key challenges for delivering clinical impact with artificial intelligence in BMC MEDICINE
  • 2020-01-13. Treating health disparities with artificial intelligence in NATURE MEDICINE
  • 2020-01-13. PIC, a paediatric-specific intensive care database in SCIENTIFIC DATA
  • 2020-10-14. Addendum: International evaluation of an AI system for breast cancer screening in NATURE
  • 2019-08-19. Do no harm: a roadmap for responsible machine learning for health care in NATURE MEDICINE
  • 2019-09-19. Author Correction: Do no harm: a roadmap for responsible machine learning for health care in NATURE MEDICINE
  • 2018-10-22. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care in NATURE MEDICINE
  • 2019-01-07. High-performance medicine: the convergence of human and artificial intelligence in NATURE MEDICINE
  • 2013-10-13. Healthcare technologies, quality improvement programs and hospital organizational culture in Canadian hospitals in BMC HEALTH SERVICES RESEARCH
  • 2019-02-11. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence in NATURE MEDICINE
  • 2020-01-01. International evaluation of an AI system for breast cancer screening in NATURE
  • 2017-01-30. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts in NATURE BIOMEDICAL ENGINEERING
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    DIMENSIONS

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