Using Language Models to Generate Patient Clinical Letters

Team

E/19/253 Narasinghe N.K.B.I.U. - Email
E/19/431 Wickramaarachchi I.W.A.P.D. - Email
E/19/465 Dilshan R.M.S. - Email

Supervisors

Dr. Asitha Bandaranayake - Email
Dr. Damayanthi Herath - Email
Prof. Shenal Thalgahagoda - Email

Abstract
Related Works
Methodology
Experiment Setup and Implementation
Results and Analysis
Conclusion
Publications
Links

Abstract

Language models (LMs) have transformed numerous industries due to their ability to generate human-like text. However, their implementation in healthcare remains a challenge due to concerns regarding data privacy, security, and computational constraints. Clinical documentation is a time-intensive task for physicians, making automated assistance a potential solution for reducing workload while enhancing efficiency.

This research introduces an AI-powered system that utilizes a small language model (SLM) to assist healthcare professionals in generating clinical letters. By leveraging an optimized model for low-resource environments and implementing robust privacy-preserving techniques, our approach ensures secure and efficient clinical documentation. The system strikes a balance between performance, privacy, and accessibility, making it an ideal solution for healthcare professionals in diverse settings.

The development of AI-assisted clinical documentation encompasses several critical components, including model architectures, privacy techniques, data processing, fine-tuning strategies, and evaluation methodologies.

Model Architectures and System Designs

Transformer-based models such as EriBERTa and Longformer efficiently handle long-form text generation.
Selective State Space Models (SSMs) offer computational efficiency by leveraging state-space representations.
Retrieval-Augmented Generation (RAG) models enhance context retention and accuracy.

Privacy-Preserving Techniques

Implementation of differential privacy, federated learning, and local model deployment ensures compliance with regulatory standards such as HIPAA and GDPR.

Prompting Techniques

Utilizing zero-shot, one-shot, and few-shot prompting for structured and contextually relevant outputs.
Advanced methods such as iterative refinement, prompt chaining, and prompt ensembling improve quality.

Data Processing and Fine-Tuning

Data augmentation, normalization, and knowledge base construction (KBC) enhance input quality.
Parameter-Efficient Fine-Tuning (PEFT) techniques such as LoRA and QLoRA optimize computational efficiency.

Evaluation Criteria

Automated metrics: ROUGE, BLEU, BERTScore.
Human evaluation: Expert clinician reviews.

Methodology

Our research follows a structured approach to ensure accuracy, privacy, and efficiency in clinical letter generation.

Key Steps:

Synthetic Data Generation: Using large language models (LLMs) to generate diverse medical case data.
Data Quality Enhancement: Standardizing terminology, correcting spelling errors, and structuring input text.
Privacy Protection: Implementing anonymization and differential privacy measures.
Bias Mitigation: Applying prefix tuning to minimize model bias.
Efficient Model Training: Leveraging LoRA for parameter-efficient fine-tuning.
Quality Assurance: Utilizing output refinement and multi-model evaluations to ensure accuracy.

Experiment Setup and Implementation

(Details to be added, including dataset descriptions, model configurations, and training parameters.)

Results and Analysis

(Details to be added, including performance metrics, qualitative assessments, and comparative analysis.)

Conclusion

(Details to be added, summarizing findings and future work.)

Publications

(Will include links to reports, research papers, and conference proceedings once published.)