Bell Eapen MD, PhD.

Bringing Digital health & Gen AI research to life!

Medprompt: How to architect LLM solutions for healthcare.

Leveraging the power of advanced machine learning, particularly large language models (LLMs), has increasingly become a transformative element in healthcare and medicine. The applications of LLMs in healthcare are multifaceted, showing immense potential to improve patient outcomes, streamline administrative tasks, and foster medical research and innovation.

Medprompt: How to architect LLM solutions for healthcare.
Image Credit: David S. Soriano, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons

Architecting LLM solutions in the healthcare domain is challenging because of the intricacies associated with healthcare data and the complex nature of healthcare applications. In this post, I will give some recommendations based on the widely popular LangChain library, giving some examples.

The first step is to define the overarching problem you are trying to solve. It can be broad as in getting the right information about a patient to the doctor. Next, subdivide the problem into subproblems that can be tackled separately. For example in the above case, we need to find the patient’s health record, convert it into an easily searchable form (embedding), find areas of interest in the record and generate a summary or an answer to the specific question. Next, find solutions for each problem that may or may not require an LLM. Finally, design the orchestrator that can stitch everything together.

LangChain has some useful abstractions that will help in the last two steps. If the solution does not involve an LLM and mostly involves data retrieval and transformations, use the tool abstraction. If you need one or more LLM calls to achieve it, use the chain abstraction. Agents are the orchestrators that can stitch everything together. It is important to carefully craft the prompts for the chains and agents. Rigorous testing is vital. This includes technical performance and validation of the model’s recommendations by healthcare professionals to ensure they are accurate and clinically relevant.

Named Entity Recognition using LLMs: a cTakes alternative?

TLDR: The targeted distillation method described may be useful for creating an LLM-based cTakes alternative for Named Entity Recognition. However, the recipe is not available yet. 

Image credit: Wikimedia

Named Entity Recognition is essential in clinical documents because it enhances patient safety, supports efficient healthcare workflows, aids in research and analytics, and ensures compliance with regulations. It enables healthcare organizations to harness the valuable information contained in clinical documents for improved patient care and outcomes. 

Though Large Language Models (LLMs) can perform Named Entity Recognition (NER), the capability can be improved by fine-tuning, where you provide the model with input text that contains named entities and their associated labels. The model learns to recognize these entities and classify them into predefined categories. However, as described before fine-tuning Large Language Models (LLMs) is challenging due to the need for substantial, high-quality labelled data, the risk of overfitting on limited datasets, complex hyperparameter tuning, the requirement for computational resources, domain adaptation difficulties, ethical considerations, the interpretability of results, and the necessity of defining appropriate evaluation metrics. 

Targeted distillation of Large Language Models (LLMs) is a process where a smaller model is trained to mimic the behaviour of a larger, pre-trained LLM but only for specific tasks or domains. It distills the essential knowledge of the LLM, making it more efficient and suitable for particular applications, reducing computational demands.  

This paper described targeted distillation with mission-focused instruction tuning to train student models that can excel in a broad application class. The authors present a general recipe for such targeted distillation from LLMs and demonstrate that for open-domain NER. Their recipe may be useful for creating efficient distilled models that can perform NER on clinical documents, a potential alternative to cTakes. Though the authors have open-sourced their generic UniversalNER model, they haven’t released the distillation recipe code yet. 

REF: Zhou, W., Zhang, S., Gu, Y., Chen, M., & Poon, H. (2023). UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition. ArXiv. /abs/2308.03279 

Distilling LLMs to small task-specific models

Deploying large language models (LLMs) can be difficult because they require a lot of memory and computing power to run efficiently. Companies want to create smaller task-specific LLMs that are cheap and easy to deploy. Such small models may even be more interpretable, an important consideration in healthcare.

Distilling LLMs

Distilling LLMs refers to the process of training a smaller, more efficient model to mimic the behaviour of a larger, more complex LLM. This is done by training the smaller model on the same task as the larger model but using the predictions of the larger model as “soft targets” or guidance during training. The goal of distillation is to transfer the knowledge and capabilities of the larger model to the smaller model, without requiring the same level of computational resources.

Distilling step-by-step is an efficient distillation method proposed by Google that requires less amount of training data. The intuition is that the use of rationale generated by a chain of thought prompting along with labels during training, thereby framing it as multi-task learning, improves distillation performance. We can use ground truth labels or use a teacher LLM to generate the labels and rationale. Ground truth labels are the correct labels for the data, and they are typically obtained from human annotators. The rationale for each label can be generated by using the model to generate a short explanation for why the model predicted that label.

The paper on the method is here and the repository is here. I have converted the code from the original repository into a tool that can be used to distill any seq2seq model into a smaller model based on a generic schema. See the repository below. The original paper uses Google’s T5-v1 model, which is a large-scale language model that was developed by Google. It is part of the T5 (Text-to-Text Transfer Transformer) family of models and is based on the Transformer architecture. You can find more open-source base models for distilling on huggingface. The next plan is to use this method to create a model that can predict the FHIR filter for this repository.

Distilling LLMs step by step!

I will update this post regularly with my findings and notes on distilling models. Also, please check out my post on NLP tools in healthcare.

Kedro for multimodal machine learning in healthcare 

Healthcare data is heterogenous with several types of data like reports, tabular data, and images. Combining multiple modalities of data into a single model can be challenging due to several reasons. One challenge is that the diverse types of data may have different structures, formats, and scales which can make it difficult to integrate them into a single model. Additionally, some modalities of data may be missing or incomplete, which can make it difficult to train a model effectively. Another challenge is that different modalities of data may require different types of pre-processing and feature extraction techniques, which can further complicate the integration process. Furthermore, the lack of large-scale, annotated datasets that have multiple modalities of data can also be a challenge. Despite these challenges, advances in deep learning, multi-task learning and transfer learning are making it possible to develop models that can effectively combine multiple modalities of data and achieve reliable performance. 

Pipelines Kedro for multimodal machine learning

Kedro for multimodal machine learning

Kedro is an open-source Python framework that helps data scientists and engineers organize their code, increase productivity and collaboration, and make it easier to deploy their models to production. It is built on top of popular libraries such as Pandas, TensorFlow and PySpark, and follows best practices from software engineering, such as modularity and code reusability. Kedro supplies a standardized structure for organizing code, handling data and configuration, and running experiments. It also includes built-in support for version control, logging, and testing, making it easy to implement reproducible and maintainable pipelines. Additionally, Kedro allows to easily deploy the pipeline on cloud platforms like AWS, GCP or Azure. This makes it a powerful tool for creating robust and scalable data science and data engineering pipelines. 

I have built a few kedro packages that can make multi-modal machine learning easy in healthcare. The packages supply prebuilt pipelines for preprocessing images, tabular and text data and build fusion models that can be trained on multi-modal data for easy deployment. The text preprocessing package currently supports BERT and CNN-text models. There is also a template that you can copy to build your own pipelines making use of the preprocessing pipelines that I have built. Any number and combination of data types are supported. Additionally, like any other kedro pipeline, these can be deployed on kubeflow and VertexAI. Do comment below if you find these tools useful in your research. 

Link to the repository below.

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kedro-multimodal (this link opens in a new window) by dermatologist (this link opens in a new window)

Template for multi-modal machine learning in healthcare using Kedro. Combine reports, tabular data and image using various fusion methods.