Personalized prognosis & treatment using Ledley-Jaynes machines: An example study on conversion from Mild Cognitive Impairment to Alzheimer's Disease

PierGianLuca Porta Mana, Ingrid Rye, Alexandra Vik, Marek Kociński, Astri Johansen Lundervold, Arvid Lundervold and Alexander Selvikvåg Lundervold
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PierGianLuca Porta Mana: HVL Western Norway University of Applied Sciences
Arvid Lundervold: University of Bergen
Alexander Selvikvåg Lundervold: Western Norway University of Applied Sciences

No 8nr56, OSF Preprints from Center for Open Science

Abstract: The present work presents a statistically sound, rigorous, and model-free algorithm – the Ledley-Jaynes machine – for use in personalized medicine. The Ledley-Jaynes machine is designed first to learn from a dataset of clinical with relevant predictors and predictands, and then to assist a clinician in the assessment of prognosis & treatment for new patients. It allows the clinician to input, for each new patient, additional patient-dependent clinical information, as well as patient-dependent information about benefits and drawbacks of available treatments. We apply the algorithm in a realistic setting for clinical decision-making, incorporating clinical, environmental, imaging, and genetic data, using a data set of subjects suffering from mild cognitive impairment and Alzheimer’s Disease. We show how the algorithm is theoretically optimal, and discuss some of its major advantages for decision-making under risk, resource planning, imputation of missing values, assessing the prognostic importance of each predictor, and more.

Date: 2023-01-26
New Economics Papers: this item is included in nep-big
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:8nr56

DOI: 10.31219/osf.io/8nr56

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