# Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

arXiv: https://arxiv.org/abs/2108.04174 · HTML: https://mlsanity.com/paper/2108.04174/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 98 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Secondary Use of Electronic Health Record: Opportunities and Challenges](https://mlsanity.com/paper/2001.09479/) (2020)
- [A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data](https://mlsanity.com/paper/1909.03890/) (2019)
- [Deep learning for comprehensive forecasting of Alzheimer's Disease progression](https://mlsanity.com/paper/1807.03876/) (2018)
- [Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm](https://mlsanity.com/paper/1903.07510/) (2019)
- [Predicting Conversion of Mild Cognitive Impairments to Alzheimer's Disease and Exploring Impact of Neuroimaging](https://mlsanity.com/paper/1807.11228/) (2018)
- [Deep ensemble learning for Alzheimers disease classification](https://mlsanity.com/paper/1905.12827/) (2019)
- [Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes](https://mlsanity.com/paper/1904.09370/) (2019)
- [Predicting Rate of Cognitive Decline at Baseline Using a Deep Neural Network with Multidata Analysis](https://mlsanity.com/paper/2002.10034/) (2020)
- [A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome](https://mlsanity.com/paper/1803.05011/) (2018)
- [Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data](https://mlsanity.com/paper/1812.00546/) (2018)
- [Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study](https://mlsanity.com/paper/1909.00271/) (2019)

## Built on by (most cited first, 13 of 13)

- [Consolidated learning -- a domain-specific model-free optimization strategy with examples for XGBoost and MIMIC-IV](https://mlsanity.com/paper/2201.11815/) (2022)
- [Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models](https://mlsanity.com/paper/2405.16413/) (2024)
- [Automated detection of Alzheimer disease using MRI images and deep neural networks- A review](https://mlsanity.com/paper/2209.11282/) (2022)
- [HiMAL: A Multimodal Hierarchical Multi-task Auxiliary Learning framework for predicting and explaining Alzheimer disease progression](https://mlsanity.com/paper/2404.03208/) (2024)
- [Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers](https://mlsanity.com/paper/2404.05748/) (2024)
- [Identifying Dementia Subtypes with Electronic Health Records](https://mlsanity.com/paper/2202.00009/) (2022)
- [Artificial Intelligence for Dementia Research Methods Optimization](https://mlsanity.com/paper/2303.01949/) (2023)
- [AI and Non AI Assessments for Dementia](https://mlsanity.com/paper/2307.01210/) (2023)
- [Latent Factor Point Processes for Patient Representation in Electronic Health Records](https://mlsanity.com/paper/2508.20327/) (2025)
- [Predictive Analytics for Dementia: Machine Learning on Healthcare Data](https://mlsanity.com/paper/2601.07685/) (2026)
- [Multimodal normative modeling in Alzheimers Disease with introspective variational autoencoders](https://mlsanity.com/paper/2602.08077/) (2026)
- [Construction of a classification model for dementia among Brazilian adults aged 50 and over](https://mlsanity.com/paper/2602.16887/) (2026)
- [Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories](https://mlsanity.com/paper/2605.16319/) (2026)

## Abstract (arXiv)

Objective Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. Materials and Methods: We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. Results: There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). Discussion: Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.