Titlebook: Artificial Intelligence and Machine Learning in Health Care and Medical Sciences; Best Practices and P Gyorgy J. Simon,Constantin Aliferis

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Fecundity

評(píng)論性

Diatribe

Reporting Standards, Certification/Accreditation, and Reproducibility,rks; (b) recent efforts for accrediting health care provider organizations for AI readiness and maturity; (c) professional certification; and (d) education and related accreditation in the space of educational programs of data science and biomedical informatics specific to AI/ML.

Glutinous

BLAZE

https://doi.org/10.1007/978-3-540-85017-5ormal vs. heuristic systems: computability, incompleteness theorem, space and time complexity, exact vs. asymptotic complexity, complexity classes and how to establish complexity of problems even in the absence of known algorithms that solve them, problem complexity vs. algorithm and program complex

ALIBI

eCustomer Relationship Management,ders who may already know about some or all of these methods. The former will find here a useful introduction and review. The latter will find additional insights as we critically revisit the key concepts and add summary guidance on whether and when each technique is applicable (or not) in healthcar

invulnerable

eCustomer Relationship Management,ity of biomedical ML focuses on predictive modeling and does not address causal methods, their requirements and properties. Yet these are essential for determining and assisting patient-level or healthcare-level interventions toward improving a set of outcomes of interest. Moreover causal ML techniq

殺子女者

eCustomer Relationship Management,AI/ML methods that can address them. The stages are explained and grounded using existing methods?examples. The process discussed equates to a generalizable Best Practice guideline applicable across all of AI/ML. An equally important use of this Best Practice is as a guide for understanding and eval

DNR215

eCustomer Relationship Management, feasibility, exploratory, or pre-clinical ones. The steps outlined span from requirements engineering to deployment and monitoring and also emphasize a number of contextual factors determining success such as clinical and health economic considerations. AI’s “knowledge cliff” is discussed and the n