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.

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) […]

natural language processing

Natural language processing (NLP) tools for health analytics

- October 5, 2018

Natural language processing (NLP) is the process of using computer algorithms to identify key elements in language and extract meaning from unstructured spoken or written text. NLP combines artificial intelligence, computational linguistics, and other machine learning disciplines. In the healthcare industry, NLP has many applications such as interpreting clinical documents in an electronic health record. […]

DHIS2 and longitudinal health records: Connecting systems to get the best of both worlds!

- September 18, 2018

DHIS2 is a health information system that revolutionized the way healthcare data is managed. It is open source and is a byproduct of a multinational action research project initiated from Oslo and first implemented in India. 1Currently, DHIS2 is the world’s largest health management information system (HMIS) platform, in use by 67 low and middle-income […]

Random forest model for predicting the total length of hospital stay (TLOS)

- August 29, 2018

TL;DR here is the Random Forest classifier code: And an (obvious) upfront disclaimer: This is a learning project. This is not for actual use. DAD is a database consisting of patient demographics, comorbidities, interventions and the length of stay for the de-identified 10% sample of hospital admissions. DAD (2014-15) has an enhanced dataset with variables […]