Version 1
: Received: 22 November 2019 / Approved: 24 November 2019 / Online: 24 November 2019 (13:26:16 CET)
How to cite:
Sanchez-Martinez, S.; Camara, O.; Piella, G.; Cikes, M.; Gonzalez Ballester, M.A.; Miron, M.; Vellido, A.; Gomez, E.; Fraser, A.; Bijnens, B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities. Preprints2019, 2019110278. https://doi.org/10.20944/preprints201911.0278.v1.
Sanchez-Martinez, S.; Camara, O.; Piella, G.; Cikes, M.; Gonzalez Ballester, M.A.; Miron, M.; Vellido, A.; Gomez, E.; Fraser, A.; Bijnens, B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities. Preprints 2019, 2019110278. https://doi.org/10.20944/preprints201911.0278.v1.
Cite as:
Sanchez-Martinez, S.; Camara, O.; Piella, G.; Cikes, M.; Gonzalez Ballester, M.A.; Miron, M.; Vellido, A.; Gomez, E.; Fraser, A.; Bijnens, B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities. Preprints2019, 2019110278. https://doi.org/10.20944/preprints201911.0278.v1.
Sanchez-Martinez, S.; Camara, O.; Piella, G.; Cikes, M.; Gonzalez Ballester, M.A.; Miron, M.; Vellido, A.; Gomez, E.; Fraser, A.; Bijnens, B. Machine Learning for Clinical Decision-Making: Challenges and Opportunities. Preprints 2019, 2019110278. https://doi.org/10.20944/preprints201911.0278.v1.
Abstract
The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making. The success of these tools is subjected to the understanding of the intrinsic processes being used during the classical pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous step to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with each of these tasks, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes.
Keywords
Machine learning; clinical decision-making; personalized medicine; digital health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
