# AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration

arXiv: https://arxiv.org/abs/1911.12205 · HTML: https://mlsanity.com/paper/1911.12205/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 30 of 51)

- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs](https://mlsanity.com/paper/2305.12788/) (2023)
- [Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series](https://mlsanity.com/paper/2306.09368/) (2023)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)
- [ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2410.02551/) (2024)
- [Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data](https://mlsanity.com/paper/2402.01713/) (2024)
- [Hierarchical Pretraining on Multimodal Electronic Health Records](https://mlsanity.com/paper/2310.07871/) (2023)
- [SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2110.04943/) (2021)
- [A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care](https://mlsanity.com/paper/2209.07805/) (2022)
- [PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations](https://mlsanity.com/paper/2309.04160/) (2023)
- [SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction](https://mlsanity.com/paper/2405.09039/) (2024)
- [ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks](https://mlsanity.com/paper/2407.18525/) (2024)
- [Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models](https://mlsanity.com/paper/2406.13942/) (2024)
- [CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis](https://mlsanity.com/paper/2411.00696/) (2024)
- [Predict and Interpret Health Risk using EHR through Typical Patients](https://mlsanity.com/paper/2312.10977/) (2023)
- [Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records](https://mlsanity.com/paper/2403.04086/) (2024)
- [Multi-task Heterogeneous Graph Learning on Electronic Health Records](https://mlsanity.com/paper/2408.07569/) (2024)
- [Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs](https://mlsanity.com/paper/2502.13555/) (2025)
- [No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery](https://mlsanity.com/paper/2505.16288/) (2025)
- [MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation](https://mlsanity.com/paper/2310.02520/) (2023)
- [Multi-Ontology Integration with Dual-Axis Propagation for Medical Concept Representation](https://mlsanity.com/paper/2508.21320/) (2025)
- [Bridging Stepwise Lab-Informed Pretraining and Knowledge-Guided Learning for Diagnostic Reasoning](https://mlsanity.com/paper/2410.19955/) (2024)
- [Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction](https://mlsanity.com/paper/2502.10689/) (2025)
- [AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment](https://mlsanity.com/paper/2203.02446/) (2022)
- [Large Language Model as a Universal Clinical Multi-task Decoder](https://mlsanity.com/paper/2406.12738/) (2024)
- [Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness](https://mlsanity.com/paper/2505.11802/) (2025)
- [ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions](https://mlsanity.com/paper/2508.18313/) (2025)
- [MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification](https://mlsanity.com/paper/2509.17809/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs](https://mlsanity.com/paper/2308.09896/) (2023)

## Abstract (arXiv)

Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays a vital role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using prediction models as the black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability. We conduct a health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability, which is verifiable by clinical experts.