Evaluating the Potential of GPT-3.5 in Generating and Coding Discharge Summaries for Data Augmentation

Concetti Chiave

GPT-3.5 can generate synthetic discharge summaries that, when combined with real data, improve the performance of local neural models on rare ICD-10 codes, but the generated documents lack the authenticity and narrative coherence required for clinical use.

Sintesi

This study investigates the potential of GPT-3.5, a large language model, in generating and coding discharge summaries for data augmentation in automated ICD-10 coding tasks.

The researchers first selected a set of low-population ICD-10 codes from the MIMIC-IV dataset and generated 9,606 synthetic discharge summaries based on their descriptions using GPT-3.5. These synthetic documents were then combined with the original MIMIC-IV training set to create an augmented dataset.

Local neural network models (CAML, LAAT, and Multi-Res CNN) were trained on both the baseline and augmented datasets and evaluated on a held-out test set. The results show that while the overall performance of the augmented models slightly decreased compared to the baseline, their performance on the low-population "generation" codes and their families improved, including correctly predicting one code absent from the original training data. The augmented models also exhibited lower out-of-family error rates, indicating that the synthetic data helped reduce mispredictions outside the relevant code families.

The researchers also evaluated GPT-3.5's ability to directly code discharge summaries, both on real MIMIC-IV data and on the self-generated synthetic data. While GPT-3.5 performed reasonably well on the synthetic data when provided with the code descriptions, its performance on the real MIMIC-IV data was significantly lower, suggesting that the model struggles to identify codes without explicit prompting.

Finally, four clinical experts evaluated the quality of the GPT-3.5-generated discharge summaries. They found that the synthetic documents correctly described the prompted medical conditions and procedures but lacked variety, supporting information, and narrative coherence compared to real discharge summaries. The experts highlighted the need for improvements in generating realistic patient histories, prioritizing critical diagnoses, and maintaining coherence between different aspects of the clinical note.

In conclusion, this study demonstrates the potential of using GPT-3.5 to generate synthetic discharge summaries for data augmentation in automated ICD-10 coding, particularly for improving performance on rare codes. However, the generated documents still fall short of the standards required for clinical use, highlighting the need for further advancements in large language model-based medical text generation.

Statistiche

The MIMIC-IV dataset contains 110,442 training documents, 4,017 validation documents, and 7,851 test documents.
The researchers generated 9,606 synthetic discharge summaries using GPT-3.5.

Citazioni

"While GPT-3.5 alone given our prompt setting is unsuitable for ICD-10 coding, it supports data augmentation for training neural models."
"Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors."
"Documents generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives."

Approfondimenti chiave tratti da

Can GPT-3.5 Generate and Code Discharge Summaries?

by Matú... alle arxiv.org 09-17-2024

https://arxiv.org/pdf/2401.13512.pdf

Domande più approfondite

How could the prompt design be improved to generate more clinically realistic and coherent discharge summaries using GPT-3.5 or other large language models?

To enhance the generation of clinically realistic and coherent discharge summaries using GPT-3.5 or similar large language models, several improvements in prompt design can be implemented:

Structured Input: Instead of providing a simple list of diagnoses and procedures, the prompt could include a structured format that mimics real discharge summaries. This could involve sections for patient history, examination findings, treatment plans, and follow-up instructions, allowing the model to generate a more comprehensive narrative.

Contextual Information: Incorporating contextual information about the patient's medical history, current medications, and specific clinical scenarios can help the model generate summaries that reflect the complexity of real patient cases. For example, prompts could specify the patient's age, gender, and relevant comorbidities.

Chronological Order: Organizing the prompt to present information in a chronological manner can guide the model to produce summaries that reflect the progression of the patient's condition and treatment. This could involve specifying timestamps for key events in the patient's care.

Examples of Real Discharge Summaries: Providing examples of high-quality, real discharge summaries as part of the prompt can serve as a reference for the model. This in-context learning approach can help the model understand the expected structure, language, and level of detail.

Emphasis on Clinical Relevance: The prompt should explicitly instruct the model to prioritize critical diagnoses and relevant clinical details while omitting extraneous information. This can help ensure that the generated summaries are concise and focused on the most important aspects of the patient's care.

Feedback Loop: Implementing a feedback mechanism where clinicians review and provide feedback on generated summaries can help refine the prompt over time. This iterative process can lead to continuous improvements in the quality and realism of the outputs.

What other techniques, such as retrieval-augmented generation, could be explored to enhance the quality of synthetic medical text generated by large language models?

Several techniques can be explored to enhance the quality of synthetic medical text generated by large language models, including:

Retrieval-Augmented Generation (RAG): This technique combines the strengths of retrieval-based and generative models. By retrieving relevant clinical documents or notes from a database and using them as context for generation, RAG can produce more accurate and contextually relevant summaries. This approach can help the model ground its outputs in real-world data, improving coherence and clinical relevance.

Fine-Tuning on Domain-Specific Data: Fine-tuning large language models on a curated dataset of clinical texts, including discharge summaries, can enhance their understanding of medical terminology, context, and narrative structure. This specialized training can lead to improved performance in generating clinically acceptable outputs.

Incorporating Ontologies and Knowledge Graphs: Utilizing medical ontologies and knowledge graphs can provide structured information about diseases, treatments, and relationships between medical concepts. Integrating this structured knowledge into the generation process can help the model produce more accurate and contextually appropriate summaries.

Multi-Modal Inputs: Exploring multi-modal inputs, such as combining text with structured data (e.g., lab results, imaging reports), can provide a richer context for the model. This approach can help the model generate summaries that incorporate quantitative data alongside qualitative descriptions.

Human-in-the-Loop Approaches: Involving clinicians in the generation process can enhance the quality of outputs. Clinicians can provide real-time feedback, suggest modifications, and help curate training data, ensuring that the generated summaries meet clinical standards.

Prompt Engineering Techniques: Advanced prompt engineering techniques, such as using few-shot or zero-shot learning paradigms, can be employed to guide the model more effectively. By providing specific examples of desired outputs, the model can better understand the nuances of clinical language and structure.

Given the limitations of GPT-3.5 in this task, what other types of AI models or architectures could be more suitable for generating high-quality, clinically acceptable discharge summaries?

Given the limitations of GPT-3.5 in generating high-quality, clinically acceptable discharge summaries, several alternative AI models and architectures could be more suitable:

Domain-Specific Language Models: Models specifically trained on medical texts, such as BioBERT or ClinicalBERT, can leverage domain-specific knowledge and terminology. These models are designed to understand the nuances of clinical language, making them more adept at generating accurate and contextually relevant summaries.

Hierarchical Models: Hierarchical models that incorporate multi-level representations of medical concepts can be beneficial. These models can capture the relationships between different levels of medical information (e.g., symptoms, diagnoses, treatments) and generate summaries that reflect these relationships more effectively.

Encoder-Decoder Architectures: Encoder-decoder architectures, such as those used in sequence-to-sequence models, can be effective for generating structured outputs like discharge summaries. These models can encode the input information and then decode it into a coherent narrative, allowing for better control over the output structure.

Graph Neural Networks (GNNs): GNNs can be employed to model relationships between medical concepts and patient data. By representing clinical information as a graph, these models can capture complex interdependencies and generate summaries that reflect the interconnected nature of medical information.

Reinforcement Learning Approaches: Reinforcement learning can be used to optimize the generation process based on specific quality metrics, such as coherence, informativeness, and clinical relevance. By training the model to maximize these metrics, it can produce higher-quality outputs.

Ensemble Methods: Combining multiple models through ensemble methods can enhance performance. By leveraging the strengths of different architectures, an ensemble approach can lead to more robust and accurate generation of discharge summaries.

Attention Mechanisms: Utilizing attention mechanisms can help models focus on relevant parts of the input data when generating summaries. This can improve the coherence and relevance of the generated text by ensuring that the model emphasizes critical information.

By exploring these alternative models and techniques, researchers can work towards developing AI systems that are better equipped to generate high-quality, clinically acceptable discharge summaries.