# Collaborative Synthesis of Patient Records through Multi-Visit Health State Inference

arXiv: https://arxiv.org/abs/2312.14646 · HTML: https://mlsanity.com/paper/2312.14646/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models](https://mlsanity.com/paper/2106.13353/) (2021)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination](https://mlsanity.com/paper/1809.01852/) (2018)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [Incremental Transformer with Deliberation Decoder for Document Grounded Conversations](https://mlsanity.com/paper/1907.08854/) (2019)
- [An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining](https://mlsanity.com/paper/2005.02799/) (2020)
- [EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders](https://mlsanity.com/paper/2012.10020/) (2020)
- [Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues](https://mlsanity.com/paper/2012.09673/) (2020)
- [PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning](https://mlsanity.com/paper/2211.01761/) (2022)
- [Generating Electronic Health Records with Multiple Data Types and Constraints](https://mlsanity.com/paper/2003.07904/) (2020)
- [Multi-Label Clinical Time-Series Generation via Conditional GAN](https://mlsanity.com/paper/2204.04797/) (2022)
- [Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks](https://mlsanity.com/paper/1901.01592/) (2019)
- [CONAN: Complementary Pattern Augmentation for Rare Disease Detection](https://mlsanity.com/paper/1911.13232/) (2019)
- [Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches](https://mlsanity.com/paper/1903.03985/) (2019)
- [A Deep Learning Pipeline for Patient Diagnosis Prediction Using Electronic Health Records](https://mlsanity.com/paper/2006.16926/) (2020)

## Built on by (most cited first, 6 of 6)

- [Generating Clinically Realistic EHR Data via a Hierarchy- and Semantics-Guided Transformer](https://mlsanity.com/paper/2502.20719/) (2025)
- [Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires](https://mlsanity.com/paper/2510.25384/) (2025)
- [Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data](https://mlsanity.com/paper/2509.11078/) (2025)
- [SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbidities](https://mlsanity.com/paper/2602.08254/) (2026)
- [Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment](https://mlsanity.com/paper/2506.10877/) (2025)
- [From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectories](https://mlsanity.com/paper/2603.06720/) (2026)

## Abstract (arXiv)

Electronic health records (EHRs) have become the foundation of machine learning applications in healthcare, while the utility of real patient records is often limited by privacy and security concerns. Synthetic EHR generation provides an additional perspective to compensate for this limitation. Most existing methods synthesize new records based on real EHR data, without consideration of different types of events in EHR data, which cannot control the event combinations in line with medical common sense. In this paper, we propose MSIC, a Multi-visit health Status Inference model for Collaborative EHR synthesis to address these limitations. First, we formulate the synthetic EHR generation process as a probabilistic graphical model and tightly connect different types of events by modeling the latent health states. Then, we derive a health state inference method tailored for the multi-visit scenario to effectively utilize previous records to synthesize current and future records. Furthermore, we propose to generate medical reports to add textual descriptions for each medical event, providing broader applications for synthesized EHR data. For generating different paragraphs in each visit, we incorporate a multi-generator deliberation framework to collaborate the message passing of multiple generators and employ a two-phase decoding strategy to generate high-quality reports. Our extensive experiments on the widely used benchmarks, MIMIC-III and MIMIC-IV, demonstrate that MSIC advances state-of-the-art results on the quality of synthetic data while maintaining low privacy risks.