Estimate the hidden deployment cost of predictive models to improve patient care

Estimate the hidden deployment cost of predictive models to improve patient care

Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the...

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Veröffentlicht in:Nature medicine 2020-01, Vol.26 (1), p.18-19
Hauptverfasser: Morse, Keith E., Bagley, Steven C., Shah, Nigam H.
Format: Artikel
Sprache:eng
Schlagworte:
631/114/1305
631/114/2397
631/114/2413
631/114/794
Algorithms
Artificial intelligence
Big Data
Biomedical and Life Sciences
Biomedicine
Cancer
Cancer Research
Care and treatment
Clinical Decision-Making
Comment
Costs
Deep Learning
Efficiency
Feminism
Gender
Humans
Infectious Diseases
Informatics
Learning algorithms
Machine learning
Metabolic Diseases
Models, Theoretical
Molecular Medicine
Neurosciences
Patient Care
Patients
Prediction models
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Beschreibung
Zusammenfassung:Although examples of algorithms designed to improve healthcare delivery abound, for many, clinical integration will not be achieved. The deployment cost of machine learning models is an underappreciated barrier to success. Experts propose three criteria that, assessed early, could help estimate the deployment cost.
ISSN:1078-8956
1546-170X
DOI:10.1038/s41591-019-0651-8