# Text2MDT: Extracting Medical Decision Trees from Medical Texts

arXiv: https://arxiv.org/abs/2401.02034 · HTML: https://mlsanity.com/paper/2401.02034/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [CMMLU: Measuring massive multitask language understanding in Chinese](https://mlsanity.com/paper/2306.09212/) (2023)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2106.08087/) (2021)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking](https://mlsanity.com/paper/2010.13415/) (2020)
- [CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2109.07589/) (2021)
- [A Survey on Semantic Parsing](https://mlsanity.com/paper/1812.00978/) (2018)
- [Translating a Math Word Problem to an Expression Tree](https://mlsanity.com/paper/1811.05632/) (2018)
- [PromptCBLUE: A Chinese Prompt Tuning Benchmark for the Medical Domain](https://mlsanity.com/paper/2310.14151/) (2023)
- [Transition-based Parsing with Stack-Transformers](https://mlsanity.com/paper/2010.10669/) (2020)
- [UniRE: A Unified Label Space for Entity Relation Extraction](https://mlsanity.com/paper/2107.04292/) (2021)
- [Automatic Model Selection with Large Language Models for Reasoning](https://mlsanity.com/paper/2305.14333/) (2023)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem](https://mlsanity.com/paper/2210.11694/) (2022)
- [AutoTrans: Automating Transformer Design via Reinforced Architecture Search](https://mlsanity.com/paper/2009.02070/) (2020)
- [AutoRC: Improving BERT Based Relation Classification Models via Architecture Search](https://mlsanity.com/paper/2009.10680/) (2020)
- [Sequence Generation with Label Augmentation for Relation Extraction](https://mlsanity.com/paper/2212.14266/) (2022)

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

- [Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely](https://mlsanity.com/paper/2409.14924/) (2024)
- [FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Method](https://mlsanity.com/paper/2510.04655/) (2025)
- [CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support](https://mlsanity.com/paper/2601.03475/) (2026)
- [Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs](https://mlsanity.com/paper/2604.02477/) (2026)
- [IAPT: Instruction-Aware Prompt Tuning for Large Language Models](https://mlsanity.com/paper/2405.18203/) (2024)
- [A Generative Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2602.08322/) (2026)

## Abstract (arXiv)

Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support systems. However, the current MDT construction methods rely heavily on time-consuming and laborious manual annotation. In this work, we propose a novel task, Text2MDT, to explore the automatic extraction of MDTs from medical texts such as medical guidelines and textbooks. We normalize the form of the MDT and create an annotated Text-to-MDT dataset in Chinese with the participation of medical experts. We investigate two different methods for the Text2MDT tasks: (a) an end-to-end framework which only relies on a GPT style large language models (LLM) instruction tuning to generate all the node information and tree structures. (b) The pipeline framework which decomposes the Text2MDT task to three subtasks. Experiments on our Text2MDT dataset demonstrate that: (a) the end-to-end method basd on LLMs (7B parameters or larger) show promising results, and successfully outperform the pipeline methods. (b) The chain-of-thought (COT) prompting method citeWei2022ChainOT can improve the performance of the fine-tuned LLMs on the Text2MDT test set. (c) the lightweight pipelined method based on encoder-based pretrained models can perform comparably with LLMs with model complexity two magnititudes smaller. Our Text2MDT dataset is open-sourced at urlhttps://tianchi.aliyun.com/dataset/95414, and the source codes are open-sourced at urlhttps://github.com/michael-wzhu/text2dt.