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.

Cloud Architecture for ML

Deploy healthcare machine learning pipelines on the cloud

ML & AI Research in Healthcare

Facilitate ML and AI research in healthcare

Generative AI

Chatbot & Conversational AI for clinical workflows

AI Application Design

Design AI applications that fits enterprise architecture

FHIR & Analytics

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

Customize Health Information Systems

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.

Mind map of LLM techniques, methods and tools

To or not to LangChain

- April 27, 2024

LangChain is a free and accessible coordination framework for building applications that rely on large language models (LLMs). Although it is widely used, it sometimes receives critiques such as being complex, insecure, unscalable, and hard to maintain. As a novel framework, some of these critiques might be valid, but they might also be a strategy […]

Translational Research in Digital Health and Gen AI 

Translational Research in Digital Health and Gen AI 

- March 23, 2024

Translational research is the process of turning scientific discoveries into practical applications that can benefit society. It involves bridging the gap between different stages of research, from basic to applied, and between different stakeholders, such as researchers, clinicians, policy makers, and industry. Translational research aims to accelerate the transfer of knowledge and technology from the […]

LLM notations and symbols

Architecting LLM solutions for healthcare – Part II

- March 16, 2024

Healthcare data and applications are complex and require careful design choices. In a previous post, I have outlined some examples and tools for architecting LLM solutions. One challenge of developing LLM applications for healthcare is the complexity and diversity of the architectures involved. LLMs can be used for different purposes, such as information retrieval, text […]

Navigating the Complexities of Gen AI in Medicine: 5 Development Blunders to Avoid

Navigating the Complexities of Gen AI in Medicine: 5 Development Blunders to Avoid

- March 9, 2024

Below, I have listed five critical missteps that you should steer clear of to ensure the successful integration of Gen AI in Medicine. This post is primarily for healthcare professionals managing a software team developing a Gen AI application. Image credit: Nicolas Rougier, GPL via Wikimedia Commons #1 Focus on requirements Gen AI is an […]

Medprompt: How to architect LLM solutions for healthcare.

Medprompt: How to architect LLM solutions for healthcare.

- December 6, 2023

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?

- September 1, 2023

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 […]