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Overview

I am a clinical data scientist and educator. As a data scientist, I apply methods including natural language processing and machine learning to support clinical research and improve patient care. As an educator, my goal is to introduce students of diverse backgrounds to Informatics and empower them to learn and apply core data science skills.

What I'm working on

Educational Work

I work as an adjunct professor at Utah Valley University, where I teach INFO-3700: Foundations of Healthcare Informatics. I've also taught at various summer schools and online workshops. Whenever possible, I try to make my materials available publicly. Here are some links to online courses or tutorials which I've contributed to.

  • Foundations of Healthcare Informatics, Fall 2020: Materials include analyzing clinical data with MIMIC, training a machine learning classifier to predict diabetes, and using NLP to extract information from clinical text
  • MIMIC34MD: A crash course in clinical data science at the University of Melbourne

Alec Chapman's Projects

bmi_foundations_diabetes_ml icon bmi_foundations_diabetes_ml

Demonstration of using machine learning to predict diabetes for University of Utah DBMI Foundations of Healthcare Informatics class

cycontext_old icon cycontext_old

A Python implementation of the ConText algorithm for clinical text concept assertion using the spaCy framework

dss icon dss

This repository contains the package for the Data Science Scholars course.

fcfinder icon fcfinder

fcFinder package for identifying fluid collections utilizing pyConText

fhi_hands_on icon fhi_hands_on

A demonstration project of using pyConText to identify the patients with family history of breast cancer and/or colon cancer. It includes sentence segmentation, pyConText, document classification(rule-based) and visualization.

fiiesct_nlp icon fiiesct_nlp

Natural Language Processing system for extracting food insecurity from clinical notes.

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