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.

Machine Learning Dermatology CDSS from text and images.

Data warehousing and health data analytics for healthcare organizations.

Designing HIS that fit into regional frameworks.

Customize OSCAR, OpenMRS, DHIS2 and RedCap

Support ML and AI research in healthcare

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%
TensorFlow
85%

Blog

My thoughts.

FHIR and public health data warehouses

First posted on CanEHealth.com The provincial government is building a connected health care system centred around patients, families and caregivers through the newly established OHTs. As disparate healthcare and public health teams move towards a unified structure, there is a growing need to reconsider our information system strategy. Most off the shelf solutions are pricey, […]

natural language processing

NLP for Clinical Notes – Tools and Techniques

Clinicians add clinical notes to the EMR on each visit. The clinical notes are unstructured in most cases and can benefit from NLP (natural language processing) tools and techniques. Some are created by dictation software or by medical scribes. Family physicians and family practice-centric EMRs like OSCAR EMR rely on unstructured clinical notes. Clinical notes, […]

natural language processing

Kickstart NLP with UMLS

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

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!

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

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 […]