Abstract

We present an interpretable machine learning model for medical diagnosis called sparse high-order interaction model with rejection option (SHIMR). A decision tree explains to a patient the diagnosis with a long rule (i.e., conjunction of many intervals), while SHIMR employs a weighted sum of short rules. Using proteomics data of 151 subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, SHIMR is shown to be as accurate as other non-interpretable methods (Sensitivity, SN = 0.84 ± 0.1, Specificity, SP = 0.69 ± 0.15 and Area Under the Curve, AUC = 0.86 ± 0.09). For clinical usage, SHIMR has a function to abstain from making any diagnosis when it is not confident enough, so that a medical doctor can choose more accurate but invasive and/or more costly pathologies. The incorporation of a rejection option complements SHIMR in designing a multistage cost-effective diagnosis framework. Using a baseline concentration of cerebrospinal fluid (CSF) and plasma proteins from a common cohort of 141 subjects, SHIMR is shown to be effective in designing a patient-specific cost-effective Alzheimer’s disease (AD) pathology. Thus, interpretability, reliability and having the potential to design a patient-specific multistage cost-effective diagnosis framework can make SHIMR serve as an indispensable tool in the era of precision medicine that can cater to the demand of both doctors and patients, and reduce the overwhelming financial burden of medical diagnosis.

Highlights

  • Alzheimer’s disease (AD) is a progressive disease affecting memory and other mental functionalities with deteriorating symptoms over time

  • To validate the effectiveness of our method, we conducted a diagnostic classification of AD from normal control (NC) using an Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and presented a cost-effective AD pathology

  • We have shown that it is possible to design a patient-specific systematic multistage cost-effective AD pathology using a low-cost plasma profile followed by more advanced screening such as cerebrospinal fluid (CSF)

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Summary

Introduction

Alzheimer’s disease (AD) is a progressive disease affecting memory and other mental functionalities with deteriorating symptoms over time. The goal of ADNI core research (Henriksen et al, 2014) is to find a cost-effective way (e.g., blood based biomarkers or cognitive assessment) that can serve as the first step in a multistage diagnostic or prognostic process followed by most advanced and expensive pathologies such as CSF or MRI screening. Another important aspect to this issue is that it is not feasible for a medical practitioner, even as an expert in this domain, to exploit such a vast and diverse datasets manually.

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