Bell Eapen MD, PhD.

Bringing Digital health & Gen AI research to life!

About Me

Translational research in health IT is my passion.

Bell Eapen MD, PhD.

Digital Health & Gen AI Consultant
I am Bellraj (Bell) Eapen, a techie MD (dermatologist) with a PhD in Information Systems from McMaster University. As an R&D lead engineer at Mayo clinic, I specialize in cloud architecture for Gen AI and multimodal machine learning using FHIR.

I maintain several software libraries on GitHub. I facilitate the adoption of Generative AI and FHIR in healthcare organizations. As a consultant, I deliver prototypes & models instead of reports and recommendations!

Checkout my GitHub repo and Contact Me for your next project!

Services

Things that I work on.

Deploy healthcare machine learning pipelines on the cloud

Facilitate ML and AI research in healthcare

Chatbot & Conversational AI for clinical workflows

Design AI applications that fits enterprise architecture

Data warehousing and health data analytics for healthcare (FHIR & OHDSI OMOP)

Customize OSCAR, OpenMRS, DHIS2 and RedCap

Skills

Things that I'm good at.

FHIR
90%
GCP
80%
AI
90%
JAVA
80%
Python
70%
OSCAR EMR
100%
DERMATOLOGY
100%
TensorFlow
85%

Blog

My thoughts.

Medprompt: How to architect LLM solutions for healthcare.

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. Architecting LLM solutions in the healthcare domain is challenging […]

Named Entity Recognition using LLMs: a cTakes alternative?

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 […]

Distilling LLMs to small task-specific models

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 refers to the process of […]

Kedro for multimodal machine learning 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 […]

Using OpenFaaS containers in Kubeflow 

Using OpenFaaS containers in Kubeflow 

OpenFaas OpenFaaS is an open-source framework for building serverless functions with containers. Serverless functions are pieces of code that are executed in response to a specific event, such as an HTTP request or a message being added to a queue. These functions are typically short-lived and only run when needed, which makes them a cost-effective […]

Six things data scientists in healthcare should know

Healthcare, like most other fields, is eager to get on the data science bandwagon. Data scientists can make a huge difference in the way big data is utilized for clinical decision-making. However, there are paradigmatic differences in the way data scientists from quantitative fields view the world, compared to their clinical counterparts. This is especially […]