Bell Eapen

Physician | HealthIT Developer | Digital Health Consultant

About Me

I deliver health IT artifacts!

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.

As a consultant, I deliver prototypes and models instead of reports and recommendations!

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.

Siobhán Grayson, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons

Embeddings in healthcare: TypingDNA and Skinmesh

Neural networks (NN) are everywhere, from image analytics to NLP to clinical decision support systems. Embeddings are a popular class of techniques that emerged out of NN with diverse applications. embedding is a low-dimensional translation of high dimensional space. For clinicians, embedding is nothing but a simple representation of complex data. Embeddings are typically used […]

Google  Nevit Dilmen  Slawek Borewicz  Commons-emblem-question blue / CC BY-SA (https://creativecommons.org/licenses/by-sa/3.0)

Rendering FHIR Questionnaire for data capture

Standardized data collection forms are vital for health information systems. This is particularly true in public health, where there is a host of data collection forms shared by various organizations. InterRAI is a typical example. Standardization is important for collaborative data analytics at various levels, a need that became painfully apparent during the recent COVID-19 […]

Clinical Query Language – Part 1

Clinical Query Language – Part 1

Clinical Query Language (CQL) is a high-level query language to represent and generate unambiguous quality measures or clinical decision rules. I am not a CQL expert. These are my notes from a system development perspective (not a clinical author perspective). I am trying to make sense of this emerging concept and add my notes here […]

OHDSI OMOP to FHIR mapper

OHDSI OMOP to FHIR mapper

TL;DR Below is an open-source common-line tool for converting an OHDSI OMOP cohort (defined in ATLAS) to a FHIR bundle and vice versa. OHDSI OMOP CDM is one of the most popular clinical data models for health data warehouses. The simple, but clinically motivated data structure is intuitively appealing to clinicians leading to its good […]

eHealth Programmer Girl

OHDSI OMOP CDM ETL Tools in Python, .Net and Go

TL;DR Here are few OHDSI OMOP CDM tools that may save you time if you are developing ETL tools! Python: pyomop | pypi.NET: omopcdmlib | NuGetGolang: gocdm The COVID-19 pandemic brought to light many of the vulnerabilities in our data collection and analytics workflows. Lack of uniform data models limits the analytical capabilities of public […]

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, […]