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- W2991652255 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." @default.
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- W2991652255 date "2019-11-24" @default.
- W2991652255 modified "2023-10-14" @default.
- W2991652255 title "Machine Learning for Clinical Decision-Making: Challenges and Opportunities" @default.
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- W2991652255 doi "https://doi.org/10.20944/preprints201911.0278.v1" @default.
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