Work

Dynamics-driven medical big data mining: dynamic approaches to early disease forecasting and individualized care

Year: 2025

Type: editorial

Abstract: This editorial examines dynamics-driven approaches for analyzing temporal patterns in medical big data to predict critical disease transitions and enable personalized medicine. We present Dynamic Network Biomarker (DNB) theory, which identifies disease tipping points through monitoring increased fluctuations and correlations within biomolecular networks, achieving prediction accuracies exceeding 80% in tumor progression and enabling early detection in influenza infections. Advanced extensions including individual-specific Edge Network Analysis (iENA) enable single-sample assessments with AUC values exceeding 0.9, eliminating the need for control groups and making real-time clinical application feasible. We discuss the integration of hybrid mechanistic-data-driven models that combine domain knowledge with deep learning capabilities, including physiology-informed LSTM networks achieving MAE of 35.0 mg/dL versus 79.7 mg/dL for traditional simulators in type 1 diabetes management, and temporal graph neural networks (TRANS) that improve diagnosis prediction accuracy on MIMIC-III datasets. Key challenges include data heterogeneity causing false positives in critical transition detection, the fundamental difficulty of distinguishing correlation from causation without experimental validation, interpretability limitations of black-box models that erode clinical trust, and ethical concerns regarding privacy in federated learning and algorithmic bias in underrepresented populations. Future directions emphasize multimodal integration combining omics profiles, imaging sequences, and wearable sensor streams through advanced Transformers and graph neural networks, alongside causal inference methods employing instrumental variables and counterfactual simulations. These dynamics-driven approaches aim to augment rather than replace clinical expertise, providing early warning signals for proactive intervention while preserving irreplaceable human judgment in complex medical decision-making, ultimately transforming healthcare from reactive treatment to preventive strategies.

Source: Intelligent Medicine

DOI: 10.1016/j.imed.2025.10.001

Authors: Lu Wang, Han Lyu, Bin Sheng

Institutions: Tianjin Medical University, Shanghai Jiao Tong University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,100