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Personalized prognosis & treatment using an optimal predictor machine: An example study on conversion from Mild Cognitive Impairment to Alzheimer's Disease

Author

Listed:
  • Porta Mana, PierGianLuca

    (HVL Western Norway University of Applied Sciences)

  • Rye, Ingrid
  • Vik, Alexandra
  • Kociński, Marek
  • Lundervold, Astri Johansen
  • Lundervold, Arvid

    (University of Bergen)

  • Lundervold, Alexander Selvikvåg

    (Western Norway University of Applied Sciences)

Abstract

The present work presents a statistically sound, rigorous, and model-free algorithm for use in personalized medicine. The algorithm is designed first to learn from a set of clinical data 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. For this reason we call it an "optimal predictor machine". We apply this machine 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 further uses.

Suggested Citation

  • Porta Mana, PierGianLuca & Rye, Ingrid & Vik, Alexandra & Kociński, Marek & Lundervold, Astri Johansen & Lundervold, Arvid & Lundervold, Alexander Selvikvåg, 2023. "Personalized prognosis & treatment using an optimal predictor machine: An example study on conversion from Mild Cognitive Impairment to Alzheimer's Disease," OSF Preprints 8nr56_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:8nr56_v1
    DOI: 10.31219/osf.io/8nr56_v1
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