Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections

• Code for paper Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections
• paper: cite Accept to BioNLP ACL'24
• Authors: Yunzhen He^{*, 1}, Hiroaki Yamagiwa^{*, 1}, Hidetoshi Shimodaira^{1, 2}
• ^* The first two authors contributed mainly and equally to this work
• ¹ Kyoto University, ² RIKEN AIP
• Workshop page: https://stanford-aimi.github.io/discharge-me/
• Our fine-tuned model page 🤗:

Overview💡

Overview of our pipeline. To create input text, we extract sections from the EHR, add explanatory prompts, and then concatenate them with <sep> tokens. We then generate discharge summaries using ClnicalT5-large, which has been fine-tuned for each target.

Setups🛠️

• For coding environment

pip install transformers==4.39.3
pip install torch==2.2.1

• Get data
  In order to get EHR data, you must have a credentialed PhysioNet account. Details are available from this page
• For raw data preprocessing
  Please use the script data_factory/cv_split.py to make input data. Remember to use data_factory/make_text_without_target.py to remove the target from the train, validation, and test datasets❗️
• For Implementing Scoring Metrics
  We utilize the official scoring metrics available at Stanford-AIMI/discharge-me/scoring. To accurately calculate scores, please first review and implement the required components as specified in the guidelines.

Experiments🔬

We employ the ClinicalT5-Large[1] model for our tasks. To fine-tune this model, utilize the provided train.py script as demonstrated below:

python train.py --fold=1 --batch_size=2 --target="brief_hospital_course" --model_name="luqh/ClinicalT5-large"

After fine-tuned model, you can use generate text like:

import pandas as pd
from generation import generate_summary

# Please provide the path for the input file.
df_input = ""

# Generate summary for "brief hospital course"
# Please provide the path of the fine-tuned model for brief hospital course.
model_name = ""
summary1 = generate_summary(df_input=df_input, model_name=model_name, target="brief_hospital_course")

# Generate summary for "discharge instructions"
# Please provide the path of the fine-tuned model for discharge instructions.
model_name = ""
summary2 = generate_summary(df_input=df_input, model_name=model_name, target="discharge_instructions")

Evaluation📊

The over-all score is calculated by first averaging the eight scores for each target, then averaging the values for the two targets.

You can use this to calculate you score:

from metrics import compute_overall_score
 
# Please provide the data of the generated summaries
# Please provide the data of the reference text
score = compute_overall_score(rows_ref=, rows_gen=):
print(score)

If you didn't implement the official scoring metrics, please take a look at https://github.com/Stanford-AIMI/discharge-me/blob/main/scoring.

Reference

[1] Qiuhao Lu, Dejing Dou, Thien Huu Nguyen: ClinicalT5: A Generative Language Model for Clinical Text. EMNL(Findings) 2022: 5436-5443

Citation

Please cite our paper if it's help for your work! @