PQHS 416 Computing in Biomedical Health Informatics

Course Instructors

  1. Satya S. Sahoo, Ph.D.(satya.sahoo@case.edu)
  2. David Kaelber, M.D., Ph.D., MPH, FAAP, FACP, FACMI(dkaelber@metrohealth.org)

Office Hours

By Appointment

Course Overview

PQHS 416 is course that introduces students to computational techniques and concepts that underpin biomedical and health informatics data management and analysis. In particular, the course will focus on the three topics of: (1) Biomedical terminologies and formal logic used in building knowledge models such as ontologies; (2) Natural language processing (NLP), and (3) Big Data technologies, including components of Hadoop stack and Apache Spark. This is a lecture-based course that relies on both materials covered in class and out-of-class readings of published literature. Students will be assigned reading assignments, homework exercise assignments and they are expected to complete homework assignment for each class. The students will be involved in a team project and they will be expected to prepare a project report at the end of the semester.

Course Objectives

# Competency Learning Outcomes Assignments and Assessment
1 Understand the fundamentals of using biomedical ontologies for integration of biomedical and health data
  • Can use and contribute to the development of biomedical terminologies, ontologies in biomedical and health data
  • Can identify relevant biomedical terminological system for data integration
In-class lectures will cover ontology engineering to develop formal knowledge models of biomedical domains and principles of data integration. Students are required to complete homework assignments for each class topic and review published articles describing the use of biomedical ontologies in data integration applications
2 Understand the components of natural language processing workflow for unstructured biomedical text
  • Can use and extend components of natural language processing tools for biomedical and health text
  • Can integrate biomedical knowledge models in natural language processing tools
In-class lectures will cover fundamentals of natural language processing. The students are required to complete homework assignments for each class topic and review published articles describing the use of natural language processing tools for unstructured and semi-structured biomedical text
3 Understand the concept of Big Data and use of Big Data technologies to address data processing and analysis challenges Can use Big Data tool library for processing and analyzing data In-class lectures will cover concepts of Big Data technologies and demonstrate the use of these technologies for data processing as well as analysis

Course Prerequisites

Undergraduate and Graduate students are expected to completed coursework in programming, data processing, and data management with an understanding of biomedical and health systems, including electronic health record systems

Course Registration

Based on the university policy, students need to register and drop/add the course within a one-week period at the start of the course

Course Format

The focus of the course is on hands-on training of students in three core components of computing techniques in biomedical and health informatics. This is an active-learning class and readings, class exercises, and homework assignments will be assigned. Students are expected to complete the assignments before coming to class. Class participation is an essential component of this class and this is part of the student assessment in this class. In addition, homework assignments, mid-term examination, and final project report as well as presentation will be used to assess students. In class exercises may be assigned before class and students are expected to follow directions in Canvas to complete these exercises. The final exam will be based on the team project and involve presentation and a project report for evaluation.