Onco-Retriever: Generative Classifier for Efficient Retrieval of Oncology Data from Electronic Health Records

Core Concepts

A lightweight, cost-effective methodology for creating an oncology-specific retrieval model that outperforms conventional embedding-based and large language models in precision, recall, and efficiency.

Abstract

The content introduces Onco-Retriever, a novel approach to efficiently retrieving relevant information from Electronic Health Records (EHRs) for oncology-related use cases. The key highlights are:

Synthetic Dataset Generation: The authors utilize GPT-3 to generate a synthetic dataset from real-world EHR data, enabling the distillation of a specialized retriever model.

Onco-Retriever Model Development: Three variants of the Onco-Retriever model are developed - Small, Optimized, and Large. The Small and Optimized models are 500M parameter derivatives of the Qwen1.5 model, while the Large model is built on the 2B parameter Gemma model.

Superior Performance: The Onco-Retriever models consistently outperform baseline models like Ada, Mistral, and fine-tuned PubMedBERT in terms of precision and recall across 13 key oncology concepts. The Optimized variant achieves the best balance of performance and efficiency.

Latency Analysis: The authors conduct extensive latency analysis, demonstrating the Onco-Retriever's readiness for integration into production environments, a critical consideration for real-world healthcare applications.

Limitations and Future Work: The authors acknowledge the model's specificity to oncology concepts and the need for further optimization to enable real-time clinical use. They also discuss the potential to expand the framework to encompass generic EHR retrieval queries.

Stats

"The sheer volume and complexity of this data hinder efficient summarization of patient journeys (Diaz et al. (2020); Batra et al. (2021); Alsentzer and Kim (2018)), impede the search for pertinent information (Ruppel et al. (2020); Natarajan et al. (2010); Yang et al. (2011); Roman et al. (2017)), and complicate the task of answering critical questions, such as those necessary for clinical trial matching (Kharrazi et al. (2018); Hernandez-Boussard et al. (2016); Raghavan et al. (2014))."
"This not only delays care delivery but also contributes to clinical burnout, as healthcare professionals end up spending considerable time navigating through EHRs to locate relevant data (Street et al. (2018); Arndt et al. (2017); Sinsky et al. (2016); Tai-Seale et al. (2017); Toll (2012))."
"The authors utilized real-world Electronic Health Records (EHRs) from 290 oncology patients of Medical College of Wisconsin EHR system, with each patient having an average of 200 documents."

Quotes

"Retrieving information from EHR systems is essential for answering specific questions about patient journeys and improving the delivery of clinical care. Despite this fact, most EHR systems still rely on keyword-based searches."
"With the advent of generative large language models (LLMs), retrieving information can lead to better search and summarization capabilities. Such retrievers can also feed Retrieval-augmented generation (RAG) pipelines to answer any query."
"Our method results in a retriever that is 30-50 F-1 points better than propriety counter-parts such as Ada and Mistral for oncology data elements."

Key Insights Distilled From

Onco-Retriever

by Shashi Kant ... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06680.pdf

Deeper Inquiries

How can the Onco-Retriever framework be extended to handle a broader range of EHR data beyond oncology-specific concepts?

The Onco-Retriever framework can be extended to handle a broader range of Electronic Health Record (EHR) data by incorporating a more diverse set of medical concepts and terminology. This extension would involve expanding the predefined concepts beyond oncology-specific terms to encompass a wider array of medical specialties and conditions.
One approach to achieve this extension is to collaborate with domain experts from various medical fields to identify key concepts and categories relevant to their respective specialties. By incorporating input from experts in areas such as cardiology, neurology, pediatrics, and others, the framework can be enriched with a more comprehensive set of concepts that cover a broader spectrum of healthcare data.
Additionally, the framework can be enhanced by integrating additional language models or fine-tuning existing models to cater to the specific vocabulary and nuances of different medical domains. This would involve training the model on a diverse range of medical texts and datasets to ensure that it can effectively retrieve and summarize information across various medical specialties.
Furthermore, the framework can be adapted to support multi-label classification, allowing it to handle complex queries that involve multiple medical concepts simultaneously. By expanding the model's capabilities to address a wider range of medical topics, the Onco-Retriever framework can evolve into a versatile tool for information retrieval and summarization across diverse healthcare domains.

How can the Onco-Retriever be further optimized to enable real-time clinical use, such as during patient consultations or in decision-making processes?

To optimize the Onco-Retriever for real-time clinical use, several key strategies can be implemented:

Model Efficiency: Streamlining the model architecture and optimizing the retrieval algorithms to reduce latency and improve response times. This may involve fine-tuning the model parameters, optimizing the inference process, and leveraging hardware acceleration to enhance performance.

Caching Mechanisms: Implementing caching mechanisms to store frequently accessed data and precomputed results, reducing the need for repeated computations and speeding up response times for commonly queried information.

Parallel Processing: Utilizing parallel processing techniques to distribute the workload across multiple computing resources, enabling faster retrieval and analysis of EHR data during real-time clinical interactions.

Incremental Learning: Implementing incremental learning strategies to continuously update and refine the model based on new data and feedback from clinical users. This adaptive learning approach ensures that the Onco-Retriever remains up-to-date and relevant in dynamic clinical environments.

By incorporating these optimization strategies, the Onco-Retriever can be tailored to meet the stringent requirements of real-time clinical use, providing healthcare professionals with timely and accurate information retrieval capabilities during patient consultations and decision-making processes.