# DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

arXiv: https://arxiv.org/abs/1602.00357 · HTML: https://mlsanity.com/paper/1602.00357/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2016.

## Built on by (most cited first, 30 of 32)

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2108.03625/) (2021)
- [TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data](https://mlsanity.com/paper/2406.10061/) (2024)
- [DeepJoint: Robust Survival Modelling Under Clinical Presence Shift](https://mlsanity.com/paper/2205.13481/) (2022)
- [Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series](https://mlsanity.com/paper/2411.12824/) (2024)
- [MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning](https://mlsanity.com/paper/2107.09288/) (2021)
- [sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion](https://mlsanity.com/paper/2211.17121/) (2022)
- [DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home](https://mlsanity.com/paper/2606.07542/) (2026)
- [Medical Profile Model: Scientific and Practical Applications in Healthcare](https://mlsanity.com/paper/2107.03913/) (2021)
- [Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation](https://mlsanity.com/paper/2305.08139/) (2023)
- [Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture](https://mlsanity.com/paper/2508.05472/) (2025)
- [Predicting Opioid Use Disorder from Longitudinal Healthcare Data using Multi-stream Transformer](https://mlsanity.com/paper/2103.08800/) (2021)
- [Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning](https://mlsanity.com/paper/2105.02580/) (2021)
- [Machine Learning Models Disclosure from Trusted Research Environments (TRE), Challenges and Opportunities](https://mlsanity.com/paper/2111.05628/) (2021)
- [Task-Synchronized Recurrent Neural Networks](https://mlsanity.com/paper/2204.05192/) (2022)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [Time-dependent Probabilistic Generative Models for Disease Progression](https://mlsanity.com/paper/2311.09369/) (2023)
- [PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics](https://mlsanity.com/paper/2602.19661/) (2026)
- [DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning](https://mlsanity.com/paper/2603.10180/) (2026)
- [DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data](https://mlsanity.com/paper/2608.05375/) (2026)
- [Predicting Disease Progress with Imprecise Lab Test Results](https://mlsanity.com/paper/2107.03620/) (2021)
- [Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding](https://mlsanity.com/paper/2111.09098/) (2021)
- [GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data](https://mlsanity.com/paper/2205.04892/) (2022)
- [A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions](https://mlsanity.com/paper/2205.13639/) (2022)
- [FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR](https://mlsanity.com/paper/2207.05750/) (2022)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records](https://mlsanity.com/paper/2310.10187/) (2023)
- [Temporal Patterns of Multiple Long-Term Conditions in Individuals with Intellectual Disability Living in Wales: An Unsupervised Clustering Approach to Disease Trajectories](https://mlsanity.com/paper/2411.08894/) (2024)
- [MMM: Quantum-Chemical Molecular Representation Learning for Combinatorial Drug Recommendation](https://mlsanity.com/paper/2510.07910/) (2025)
- [Risk Horizons: Structured Hypothesis Spaces for Longitudinal Clinical Prediction](https://mlsanity.com/paper/2602.12828/) (2026)

## Abstract (arXiv)

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.