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.

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 […]

artificial intelligence

Serverless on FHIR: Management guidelines for the semi-technical clinician!

- February 12, 2019

Serverless is the new kid on the block with services such as AWS Lambda, Google Cloud Functions or Microsoft Azure Functions. Essentially it lets users deploy a function (Function As A Service or FaaS) on the cloud with very little effort. Requirements such as security, privacy, scaling, and availability are taken care of by the […]

Hephestus: Health data warehousing tool for public health and clinical research

- November 3, 2018

Health data warehousing is becoming an important requirement for deriving knowledge from the vast amount of health data that healthcare organizations collect. A data warehouse is vital for collaborative and predictive analytics. The first step in designing a data warehouse is to decide on a suitable data model. This is followed by the extract-transform-load (ETL) […]