ArticlePeerJ2019
An interpretable machine learning model for diagnosis of Alzheimer's disease.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- Development of Alzheimer's disease risk score for future integrated primary care: a white-box approach.Frontiers in aging neuroscience · 2026Article
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Development of Alzheimer's Disease Risk Score for Future Primary Care: A White-Box Approach.medRxiv : the preprint server for health sciences · 2025Article
- Task-Related EEG as a Biomarker for Preclinical Alzheimer's Disease: An Explainable Deep Learning Approach.Biomimetics (Basel, Switzerland) · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.Scientific reports · 2025Article
- Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations.Entropy (Basel, Switzerland) · 2025Article
- Hepatic encephalopathy post-TIPS: Current status and prospects in predictive assessment.Computational and structural biotechnology journal · 2024Review
- Explainable Machine Learning Techniques to Predict Muscle Injuries in Professional Soccer Players through Biomechanical Analysis.Sensors (Basel, Switzerland) · 2023Article
- Revealing the Roles of Part-of-Speech Taggers in Alzheimer Disease Detection: Scientific Discovery Using One-Intervention Causal Explanation.JMIR formative research · 2023Article
- Value assessment of artificial intelligence in medical imaging: a scoping review.BMC medical imaging · 2022Article
- An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.Sensors (Basel, Switzerland) · 2022Article
- In-depth insights into Alzheimer's disease by using explainable machine learning approach.Scientific reports · 2022Article
- Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.Communications biology · 2022Article
- Applications of Explainable Artificial Intelligence in Diagnosis and Surgery.Diagnostics (Basel, Switzerland) · 2022Review
- Development and External Validation of a Simple-To-Use Dynamic Nomogram for Predicting Breast Malignancy Based on Ultrasound Morphometric Features: A Retrospective Multicenter Study.Frontiers in oncology · 2022Article
- Profiling hearing aid users through big data explainable artificial intelligence techniques.Frontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.