All Courses

BIOMEDICAL INFORMATICS (BIOINF) COURSES

(as of November 2015)

 

BIOINF 2011

Foundations of Clinical and Public Health Informatics (ISSP 2015/INFSCI 2821/HRS 2429) (3 credits)

A survey of fundamental concepts and activities on information technology applied to health care. Topics include computer-based medical records, knowledge-based systems, telehealth, decision theory and decision support, human-computer interfaces, systems integration, the digital library, bioinformatics, and educational applications.  Department-specific applications such as pathology, radiology, psychiatry and intensive care are also discussed.

Instructor: Rich Tsui, Ph.D.

Term:  Fall

Days/Times: Mondays/Wednesdays, 10:30 a.m. to 11:55 p.m.          

Location: 407A BAUM, 5607 Baum Blvd.

Expected class size: 20-25

 

BIOINF 2012

Problem-Oriented Programming (ISSP 2062) (3 credits)

This course is designed to extend students' programming abilities through review of current program design and coding techniques, including fourth-generation languages, the Unified Modeling Language (UML), Object-oriented Programming and Extreme Programming. The course includes a strong practical programming component based on the Python language that includes in-class laboratories, weekly practical programming problems, and midterm and final programming projects. Programming assignments are drawn from areas relevant to medical informatics such as structured text and image processing, network communications, database management, natural language processing, expert systems, etc. Through the course, students learn to understand the programming process at a practical level and gain the ability to independently create useful software tools.

Instructor: TBA

Term:  TBA

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: One course in introductory programming, or equivalent experience.

Expected class size: 8-16

 

BIOINF 2013

Introduction to Patient Care and Clinical Environments (3 credits; optional for U.S. trained clinicians)

This three credit course is designed for students who have no significant clinical experience with the U.S. healthcare system. The course is divided into two main sections. In the first section, we will cover medical and health care concepts and terms, and discuss observational techniques derived from the Toyota Production System. In the second section of the course, students will shadow physicians in a variety of clinical settings and report back to the class on their observations using the skills learned in the first half of the course. No previous clinical experience is assumed. Students will be expected to attend lectures and will spend a significant portion of their time observing and reporting on different clinical settings throughout the semester.

Instructor:  TBA

Term:  TBA

Days/Times:  TBA

Location:  407B BAUM, 5607 Baum Blvd. and various clinical areas

Expected class size:  10-12

 

BIOINF 2014

Biomedical Informatics Project Course (3 credits)

This course provides an opportunity for students to apply concepts that they learned in BIOINF 2011 to carry out a one-term research project. They will be asked to identify, plan, develop, carry out, and report on such a project. This hands-on course will encourage students to think more deeply and concretely about the concepts and methods presented in BIOINF 2011 and in doing so to develop a better understanding of that material. This course will also serve as an early, mentored introduction to performing biomedical informatics research.

Instructor:  Gregory F. Cooper, M.D.

Term:  Spring

Days/Times:  Tuesdays/Thursdays from 1:00 p.m. to 2:30 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites: BIOINF 2011 – Introduction to Biomedical Informatics.

Recitations:  None

 

BIOINF 2015

Mathematics for Biomedical Informatics (3 credits)

The purpose of this class is to review mathematical techniques that underly biomedical informatics. Knowledge of these mathematical subjects will be assumed in many subsequent biomedical informatics courses (e.g. statistics and machine learning). The course is will emphasize conceptual understanding and applications rather than formal proofs. Each mathematical subject will be illustrated with problems from within biomedical informatics.

Instructor: TBD

Term:  TBA

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Expected class size: 10-16

 

BIOINF 2016

Foundations of Translational Bioinformatics (3 credits)

The course goals are to gain familiarity of data produced with current biotechnologies, such as DNA arrays (e.g. SNP data), microarrays (transcriptional profiles), proteomics (mass spectrometry data), epigenomics (methylation profiles). Understand what can be done with such data to infer relations between genome, epigenome, and phenome, in order to discover molecular mechanisms of diseases, or identify biomarkers, or discover novel therapies for diseases.

Instructor: Xinghua Lu, M.D., Ph.D. and Madhavi Ganapathiraju, Ph.D.

Term:  TBA

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Expected class size: 10-16

 

Biomedical Informatics Colloquium (Lecture Series) (This is not a formal course.)

This course meets once each week for one hour.  The current research of Biomedical Informatics faculty and senior fellows will be presented.

Instructor:  Various speakers

Term:  Fall and Spring

Days/Times:  Fridays, 9:00 a.m. to 10:00 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Expected class size:  35

 

BIOINF 2032

Biomedical Informatics Journal Club (ISSP 2083) (1 credit)

Biomedical informatics is a broad field encompassing many different research domains. What all of the domains have in common is the need to review and publish scientific papers and to give talks that present research to different audiences. The aim of this journal club is to expose students to recent research in various topics of biomedical informatics and to teach students how to critique a research article, present research from a research study; and critique a verbal presentation of research.

Instructor:  Richard Boyce, Ph.D. and/or Xinghua Lu, M.D., Ph.D.

Term:  Fall and Spring

Days/Times:  Fridays, 10:00 a.m. to 11:00 a.m.

Location:  536B BAUM, 5607 Baum Blvd.

Expected class size:  35

 

BIOINF 2051

Foundations of Bioinformatics  (ISSP 2081) (3 credits) 

Provides an introduction to selected topics of bioinformatics also known as computational biology. In this course, the difficult computational problems involving different types of biological information are identified using case studies from current literature.  Emphasis is on genomic aspects of computational biology with some overview of proteomics and structural aspects.  The course is structured as a seminar course intending to draw students into participating in discussions related to both problems and existing solutions. 

Instructor:  Vanathi Gopalakrishnan, Ph.D.

Term:  Fall

Days/Times:  Mondays/Wednesdays 12:30 p.m. to 2:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  An introductory biology course and an undergraduate mathematics course.

Expected class size: 10

 

BIOINF 2052

Introduction to Computational Structural Biology (CMPBIO 2030 / MSBIO 2030) (3 credits)

This course is a general introduction to current theories and methods used in computational structural biology.   Fundamental concepts of probability, statistics, statistical thermodynamics and polymer physics will be considered as well as a general description of our current knowledge of biomolecular structure and dynamics for modeling and simulations of biological interactions and function.  The Protein Data Bank and software commonly used in computational structural biology will be used for modeling and simulations of structure and dynamics.

Instructor:  Ivet Bahar, Ph.D.

Term:  Spring, every odd year

Days/Times:  Tuesdays/Thursdays, 9:30 a.m. to 10:45 a.m.

Location:  BST-3, Room 3073

Prerequisites:  An introductory biology course and an undergraduate mathematics course.

Expected class size: 15

 

BIOINF 2060

Computational Genomics (MSCBIO 2070) (3 credits)

In this course, we will discuss classical approaches and latest methodological advances in the context of the following biological problems: 1) Computational genomics, focusing on gene finding, motif detection and sequence evolution. 2) Analysis of high throughput biological data, such as gene expression data,

focusing on issues ranging from data acquisition to pattern recognition and classification. 3) Molecular

and regulatory evolution, focusing on phylogenetic inference and regulatory network evolution, and 4)

Systems biology, concerning how to combine sequence, expression and other biological data sources to

infer the structure and function of different systems in the cell. From the computational side this course

focuses on modem machine learning methodologies for computational problems in molecular biology

and genetics, including probabilistic modeling, inference and learning algorithms, pattern recognition,

data integration, time series analysis, active learning, etc.

Instructor:  Ziv Bar-Joseph, Ph.D. and Takis Benos, Ph.D.

Term:  Spring

Days/Times:  TBA

Location:  TBA

Prerequisites:  Students are expected to have successfully completed Machine Learning, or an equivalent class

Expected class size:  35

 

BIOINF 2101  

Probabilistic Methods for Computer-Based Decision Support  (ISSP 2070) (INFSCI 2135)  (3 credits)

This course is now being offered as a graduate-student seminar. It covers more advanced computational approaches for probabilistic modeling and inference than the previous version of the course. A particular focus is placed on Bayesian networks, although other probabilistic models are studied. Healthcare applications are emphasized, however, the principles are general and no medical knowledge is needed to take the seminar. 

Instructor: Gregory F. Cooper, M.D., Ph.D.

Term:  TBA

Days/Times: TBA

Location: 407A BAUM, 5607 Baum Blvd.

Prerequisites: Students should have either taken Introduction to Health Informatics (BIOINF 2011) or have a basic understanding of probability theory and Bayesian networks.

Expected class size: 10

 

BIOINF 2110

Concepts of Software Project Engineering in Health Care (HRS 2428) (3 credits)

This course examines how health care organization implement both clinical and financial information systems. The course will study the implementation process and how to integrate systems to create the computerized patient record (CPR). Students will also have the opportunity to learn about the industry-wide implementation data standards and how to manage them.

Instructor:  Melissa Saul, M.S.

Term:  Summer                                                                      

Days/Times:  Mondays/Wednesdays, 5:00-7:55 p.m.     

Location:  6048 Forbes Tower.

Expected class size: 30

Special permission from instructor is required for this course.

 

BIOINF 2118

Statistical Foundations of Biomedical Informatics (3 credits)

This is an introductory probability and statistics course intended primarily for biomedical informatics students. The first part of the course covers probability, including basic probability, random variables, univariate and multivariate distributions, transformations, expectation, numerical integration, and approximations. The second part of the course covers statistics, including study design, classical parametric inference, hypothesis testing, Bayesian inference, non-parametric methods, classification, ANOVA, and regression. We will use R for statistical computing and applications. Examples and applications will focus on biomedical informatics and related discipline.

Instructor:  Roger Day, Sc.D.

Term:  Spring

Days/Times:  Tuesdays/Thursdays, 2:30 p.m. to 4:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Expected class size:  10-15

 

BIOINF 2119

Probabilistic Methods in Artificial Intelligence (3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations artificial intelligence methods in search, probabilistic knowledge representation and reasoning, and machine learning with applications to biomedical informatics. Prerequisites for this course include introductory mathematics and programming.

Instructor:  Shyam Visweswaran, MD, PhD, Xia Jiang, Ph.D., Madhavi Ganapathiraju, Ph.D.

Term:  Spring

Days/Times:  Monday/Wednesday from 12:30-2:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Expected class size:  15-20

 

BIOINF 2120

Symbolic Methods in Artificial Intelligence (3 credits)

This course is designed for students who do not necessarily have a background in computer science and want to learn and apply methods in artificial intelligence to problems in biomedicine. The course will introduce and provide the foundations of artificial intelligence methods in logical knowledge representation and reasoning, biomedical ontologies and terminologies and information retrieval. Prerequisites for this course include introductory mathematics and programming.

Instructor:  Richard Boyce, Ph.D.

Term:  Fall

Days/Times:  Tuesday/Thursdays 9:00 a.m.-10:30 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  BIOINF 2119

Expected class size:  15-20

 

BIOINF 2121

Human Computer Interaction and Evaluation Methods (3 credits)

This course is designed to provide informatics students with the knowledge necessary to take an applied role in the design, implementation and evaluation of healthcare information systems. In this course, students will apply principles of usability and evaluation theory to informatics projects.  Topics include:  critical success factors, test plan development and user interface design.

Instructor:  Harry Hochheiser, PhD

Term:  Fall

Days/Times:  Monday/Wednesday 8:30 am-10:00 a.m.

Location:  407A BAUM, 5607 Baum Blvd.

Expected class size:  15-20

 

BIOINF 2124

Principles of Global Health Informatics (3 credits)

This course explores challenges and opportunities in developing and supporting health information systems in developing-world settings by examining differences, and ways to both integrate and sustain systems in an appropriate way in low-resource settings. The course will review the current "state-of-the-art" in this field by looking at examples of systems currently deployed in the developing world, and explore opportunities for advancing this work through a series of case studies and hands-on exercises based on real-world scenarios.

Instructor:  Gerald Douglas, PhD

Term:  Spring, every odd year                                                           

Days/Times:  Monday/Wednesday from 2:30 p.m.-4:00 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  BIOINF 2011 or permission from instructor

Expected class size:  15-20

 

BIOINF 2125

Informatics and Industry (1 credits)

This class will be held once a week, for 1 hour.  The focus of the class is to provide an opportunity for students to interact with leading industry representatives and to learn techniques/tools that would enable them to market their skills in non-academic environments. We will invite speakers from various local, regional, national, and international industry relationships that we have established

Instructor:  Richard Boyce, Ph.D. and Michael Becich, M.D., Ph.D.

Term:  Spring, every even year

Days/Times:  Thursdays, 12:00 p.m. to 12:55 p.m.

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  BIOINF 2011

Expected class size:  15-20

 

BIOINF 2129

Internship in Global Health Informatics (3 credits)

The Summer Internship in Global Health Informatics will be expanded to accommodate 5 students from the US. Students will travel to Malawi to study Global Health Informatics in low-resource settings alongside Malawian health and technology professionals. Students will have an opportunity to propose, design and develop a product or intervention relevant to solving a particular problem the group has identified.

Instructor:  Gerald Douglas, Ph.D.

Term:  Summer

Days/Times:  TBA

Location:  Malawi, Africa

Prerequisites:  BIOINF 2124

Expected class size:  4-5

 

BIOINF 2131

Practicum in Advanced Biomedical Information Technology (ISSP 2090)  (1-6 credits)

This course is designed for people who want a practical experience in working with advanced information technology in the Department of Biomedical Informatics.

Instructor:  Department of Biomedical Informatics Faculty and Staff

Term:  TBA

Days/Times:  TBA

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  Discuss with Instructor

Expected class size:  5

This course could be offered in any given term -- check with Toni Porterfield (tls18@pitt.edu).

 

BIOINF 2132

Special Topic Seminar in Medical Informatics (3 credits)

This course is designed for faculty to offer small groups of students a study course on a topic of mutual interest and concern in the faculty member’s area of expertise.

Instructor:  Department of Biomedical Informatics Faculty (will vary)

Term:  TBA

Days/Times:  TBA

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  Discuss with Instructor

Expected class size:  10

This course could be offered in any given term -- check with Toni Porterfield (tls18@pitt.edu)..

 

BIOINF 2133

Practicum in Advanced Infectious Disease and Public Health Surveillance (Biosurveillance) Technology (1-6 credits)

This course is designed for people who want a practical experience in working with advanced biosurveillance technology in the realtime outbreak and disease surveillance (RODS) laboratory.

Instructor:  Department of Biomedical Informatics Faculty (will vary)

Term:  TBA

Days/Times:  TBA

Location:  407A BAUM, 5607 Baum Blvd.

Prerequisites:  Discuss with Instructor

Expected class size:  20

This course could be offered in any given term – check with Toni Porterfield (tls18@pitt.edu).

 

BIOINF 2134

Publication & Presentation in Biomedical Informatics (3 credits)

This course provides a practical overview of how to write a research manuscript and how to give a scientific talk. It is usually taken after completing the Project Course (BIOINF 2014). Students taking this course must have a completed research project that can be used to complete the course exercises. Each week, we will target a specific section of the manuscript or scientific talk. Didactic sessions describing common problems and approaches will alternate with student presentation and peer critique. The course also covers the details of the publication process. At the end of the course, a special presentation workshop gives students the opportunity to improve their talks using videotaping and debriefing methods. By the end of the course, students will have completed a research paper and a finalized colloquium presentation.

Instructor:  Rebecca Jacobson, M.D., M.S.

Term:  Fall

Days/Times:  Wednesdays from 9:00 a.m. – 11:55 a.m.

Location:  407B BAUM, 5607 Baum Blvd.

Prerequisite: Completed data collection for study in research project with approval of both research advisor and course instructor.

Expected Class Size: 5

 

BIOINF 2480 (1-6 credits)

Masters Thesis/Project Research

 

BIOINF 2990 (1-6 credits)  

Masters Independent Study

 

BIOINF 2993 (1-6 credits)

Masters Directed Study

 

BIOINF 3990 (1-6 credits)

Doctoral Independent Study

 

BIOINF 3995 (1-6 credits)

Doctoral Directed Study

 

BIOINF 3998 (3 credits)

Doctoral Teaching Practicum

 

BIOINF 3999 (18 credits)

Doctoral Dissertation Research

 

NOTE:  Students registering for Full-time Dissertation Study must register under the School of Medicine’s Course Number:   FTDS 0000 (0 credits)

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