Bell Eapen

Physician | HealthIT Developer | Digital Health Consultant

About Me

Here are some things that you should know about me.

Bell Eapen

Digital Health Consultant
Bellraj (Bell) Eapen is a techie physician with expertise in JAVA, Python, .NET, Web, and Mobile development platforms. He is currently pursuing a Ph.D. in information systems at McMaster University. He has expertise in Big Data, Machine Learning and Artificial Intelligence applications in healthcare. He is a proponent of open-source software and has extensive knowledge of the OSCAR and OpenMRS EMR platforms. He has experience with frameworks such as Keras and Apache Spark. His research interests include Clinical Decision Support Systems, Cutaneous Imaging, mHealth and Public Health Informatics. He has several peer-reviewed publications, and he is a reviewer and editorial board member for few journals.

Services

Things that I work on.

Clinical Decision Support Systems

Machine Learning Dermatology CDSS from text and images.

Data Warehousing

Data warehousing and health data analytics for healthcare organizations.

Health Information System Design

Designing HIS that fit into regional frameworks.

Customize Health Information Systems

Customize OSCAR, OpenMRS, DHIS2 and RedCap

ML & AI Research in Healthcare

Support ML and AI research in healthcare

Knowledge Management

Support Knowledge Management in healthcare using FHIR

Skills

Things that I'm good at.

FHIR
90%
ML
80%
AI
90%
JAVA
80%
Python
70%
OSCAR
100%
OPENMRS
100%
R
81%

Blog

My thoughts.

natural language processing

Kickstart NLP with UMLS

- January 15, 2020

The UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. Natural Language Processing (NLP) on the vast amount of data captured by electronic medical records (EMR) is gaining popularity. The recent advances in machine learning (ML) algorithms […]

eHealth Programmer Girl

How to deploy an h2o ai model using OpenFaaS on Digitalocean in 2 minutes

- November 13, 2019

H2O is an open-source, distributed and scalable machine learning platform written in JAVA. H2O supports many of the statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, deep learning and more.  OpenFaaS® (Functions as a Service) is a framework for building Serverless functions easily with Docker. Read my previous post to learn more about OpenFaaS […]

Deploy a fastai image classifier using OpenFaaS for serverless on DigitalOcean in 5 easy steps!

- September 25, 2019

Fastai is a python library that simplifies training neural nets using modern best practices. See the fastai website and view the free online course to get started. Fastai lets you develop and improve various NN models with little effort. Some of the deployment strategies are mentioned in their course, but most are not production-ready. OpenFaaS® […]

OSCAR in a BOX – Virtualized and fault-tolerant OSCAR EMR

- August 20, 2019

TL;DR: OSCAR in a BOX is a fault-tolerant OSCAR instance that you can use out of the box and is virtually maintenance-free! OSCAR EMR is an open-source Electronic Medical Record (EMR) for the Canadian family physicians. The official OSCAR repository is available here: https://bitbucket.org/oscaremr/ OSCAR is a spring java application deployed in a tomcat container with MySQL […]

Drishti: An mHealth platform for pervasive health monitoring

- August 13, 2019

TL;DR: Here is an open-source mHealth framework based on FHIR! and here is the paper and my presentation at ICSE! Pervasive health monitoring is becoming less and less intrusive with better sensors, and more and more useful with machine learning and predictive analytics. mHealth (mobile health) could play an important part in pervasive health monitoring. […]

Machine Learning on Diabetic Retinopathy Images

- July 2, 2019

Artificial intelligence (AI) and Machine Learning (ML) are having a profound impact on the way medicine is being practiced. AI/ML algorithms and techniques fit imaging applications easily and can help with automation. Radiology is the specialty that has benefitted the most from the AI/ML revolution. Melanoma detection in Dermatology is another obvious winner. Many of […]