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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

Haresh Rengaraj Rajamohan¹, Yanqi Xu¹, Weicheng Zhu¹

¹Center for Data Science, New York University, New York, New York, United States of America.

|May 6, 2026

Summary

Deep learning models pretrained on large diagnostic datasets improve disease prognosis prediction. A sequential learning strategy with experience replay prevents

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Deep Learning for Healthcare

Background:

Accurate disease prognosis is crucial but limited by scarce longitudinal data.
Deep learning models can leverage large diagnostic datasets for prognostic tasks.
Naive fine-tuning risks 'catastrophic forgetting,' degrading diagnostic accuracy.

Purpose of the Study:

To explore deep learning training strategies for enhanced disease prognosis.
To mitigate catastrophic forgetting in prognostic models.
To improve prediction of disease progression in knee osteoarthritis, Alzheimer's disease, and breast cancer.

Main Methods:

Utilized large diagnostic datasets for pretraining deep learning models.
Employed a sequential learning strategy with experience replay.
Applied models to predict knee osteoarthritis worsening, Alzheimer's cognitive decline, and breast cancer diagnosis.

Main Results:

Diagnostic pretraining significantly improved prognostic performance (AUROC, AUPRC) compared to baselines.
Sequential learning with experience replay matched single-task model prognostic accuracy.
This approach preserved diagnostic ability, unlike simpler multitask methods prone to catastrophic forgetting.

Conclusions:

Leveraging large diagnostic datasets is effective for enhancing prognostic models.
Sequential learning with experience replay offers a data-efficient solution for prognosis.
This strategy reliably improves predictive accuracy while maintaining essential diagnostic skills.