Summary of Assessing the Significance Of Longitudinal Data in Alzheimer’s Disease Forecasting, by Batuhan K. Karaman et al.
Assessing the significance of longitudinal data in Alzheimer’s Disease forecasting
by Batuhan K. Karaman, Mert R. Sabuncu
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study employs a transformer encoder model to forecast Alzheimer’s Disease (AD) progression using longitudinal patient data. The Longitudinal Forecasting Model for Alzheimer’s Disease (LongForMAD) incorporates multimodal data and temporal information from sequences of patient visits, providing a deeper understanding of disease progression than single-visit data alone. An empirical analysis across two patient groups-Cognitively Normal (CN) and Mild Cognitive Impairment (MCI)-over five follow-up years reveals that models incorporating more extended patient histories outperform those relying solely on present information. The findings support the incorporation of longitudinal data in clinical settings to enhance early detection and monitoring of AD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses a special computer model to predict how Alzheimer’s Disease will progress over time, based on past experiences and medical records of patients. They compared two groups: people with normal cognitive abilities and those with mild cognitive impairment. The results show that considering more past information helps make better predictions than only looking at current data. This research supports the use of old patient data to help diagnose Alzheimer’s early and track its progression. |
Keywords
» Artificial intelligence » Encoder » Transformer
