Biomedical Informatics (BIOINF) Courses

Spring Term, 2010 – subject to change

(as of October 14, 2009)

BIOINF 2011
Principles of Health Informatics (ISSP 2015 / 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: Shyam Visweswaran, MD, PhD
Days/Times: Mondays and Wednesdays, 10:00 a.m. to 11:25 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: None
Recitations: None
Expected class size: 20-25
This course is usually offered in the fall term.

BIOINF 2012
Problem-Oriented Programming in Medical Informatics (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: Brian E. Chapman, PhD
Days/Times: Thursdays, 9:00 a.m. to 12:00 noon.
Location: TBA
Prerequisites: One course in introductory Programming, or equivalent experience.
Recitations: None
Expected class size: 8-16
This course is usually offered in the fall term.

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 twomain 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 secondsection 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: Natalia Morone, MD, M.Sc. and Steven Handler, MD
Days/Times: Thursdays from 1:00 p.m.to 4:00 p.m.
Location: M-185 VALE, 200 Meyran Avenue and various clinical areas
Prerequisites: None
Recitations: None
Expected class size: 10-12
This course is offered in the fall term.

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:  TBD
Days/Times:  Tuesdays and Thursdays from 1:00 p.m. to 2:30 p.m.
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites: BIOINF 2011 – Introduction to Biomedical Informatics.
Recitations:  None
This course is offered during the spring term.

BIOINF 2015
Mathematical Foundations of 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: Brian E. Chapman, PhD
Days/Times: Monday, Tuesday, and Wednesdays, 2:05 p.m. to 3:00 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: None
Recitations: None
Expected class size: 10-16
This course is usually offered in the fall term.

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
Days/Times:  Fridays, 11:00 a.m. to 12:00 noon
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  None
Recitations:  None
Expected class size:  35
This course is offered in both fall and spring terms. 

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:  Brian Chapman, PhD
Days/Times:  Fridays, 10:00 a.m. to 11:00 a.m.
Location:  M-185 VALE, 200 Meyran Avenue
Prerequisites:  None
Recitations:  None
Expected class size:  35
This course is offered in the spring term.

BIOINF 2051
Principles 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, PhD
Days/Times: Mondays and Wednesdays 12:30 p.m. to 2:00 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: An introductory biology course and an undergraduate mathematics course.
Recitations: none
Expected class size: 10
This course is offered in the fall term.

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, PhD
Days/Times: Tuesdays and 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.
Recitations: none
Expected class size: 15
This course is offered in the spring term, every odd year.

BIOINF 2053
Sequence Analysis Laboratory (3 Credits)
This course will give students hands-on experience with sequence analysis software by involvement in an intensive workshop offered by the Pittsburgh Supercomputing Center. In addition, students will work on a study project directed by the instructor that will enable them to apply bioinformatics techniques to a challenging biomedical problem.
Instructor: Vanathi Gopalakrishnan, PhD
Days/Times: TBA
Location: M-184 VALE, 200 Meyran Avenue & The Pittsburgh Supercomputing Center
Prerequisites: BIOINF 2051 Introduction to Biomedical Informatics.
Recitations: none
Expected class size: 3
This course is offered in the summer term.

BIOINF 2054   
Statistical Foundations for Bioinformatics Data Mining  (BIOST 2018) (3 Credits)
This course introduces data analysis methods which are widely used or rapidly gaining use in bioinformatics.   Methods deal with prediction, classification, optimization, and clustering.  Methods covered include classification trees, flexible varieties of discriminant analysis including support vector machines, EM algorithm and Monte Carlo Markov chain, the bootstrap and bagging, boosting, and self-organizing maps. The methods are placed into the context of principles and models of statistical science, with emphasis on Bayesian methods.  Examples are drawn from microarrays, analysis of genetic networks, proteomics, computational pharmacology, and research text mining.
Instructor:  Roger S. Day, ScD
Days/Times:  Wednesdays and Fridays, 3:00 to 4:30 p.m.
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  An introductory statistics/biostatistics course.
Recitations:  none
Expected class size: 6-10
This course is offered in the spring term, every odd year.  Special permission from instructor is required for this course.

BIOINF 2055
Practical Analysis of High-Throughput Genomic and Proteomic Data Sources  (3 Credits)
This course provides an in-depth, comparative study of methods for the analysis and interpretation of high-throughput genomic and proteomic data sources.  Using a broad survey of the literature, the student will become familiar with approaches to normalization/transformation, finding predictive biomarkers, methods for classification, cross-validation, functional interpretation.  Ways to integrate diverse data sources, including clinical outcomes, will be explored.  Classroom activities will include lectures exercises in the use of publically-available software, and intensive experience in the analysis and interpretation of published data sets.  By the end of the semester, students will be able to think critically about the diverse strategies for analyzing high-throughput genomic and proteomic data sources.
Instructor:  James Lyons-Weiler, PhD
Days/Times:  Tuesdays and Thursdays, 3:00 to 4:30 p.m.
Location:  UPMC Cancer Pavilion (Shadyside UPMC), Room 304, 3rd Floor.
Prerequisites:  This course is open to seniors and graduate students from any school.  At least two semesters of statistics, any level, are required.
Recitations:  none
Expected class size: 10-12
This course is offered in the spring term, every even year. 

BIOINF 2057
Elements of Statistical Learning (BIOST 2015) (3 Credits)
The purpose of the course is to present the theory and practice of statistical learning algorithms, placing “statistical learning” or “data mining” techniques in the proper context with regard to their origins in simple classical methods like linear regression, to clarify the strengths and weaknesses from theoretical and practical sides. “Supervised learning” techniques studied include using regularization and Bayesian methods, kernel methods, basis function methods, neural networks, support vector machines, additive trees, boosting, bootstrap-based methods. Unsupervised learning techniques studied include cluster analysis, self-organizing maps, independent component analysis and projection pursuit.
Instructor: Roger S. Day, Sc.D.
Days/Times: Wednesdays and Fridays, 3:00 p.m. to 4:25 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: BIOST 2041, 2042, 2043, 2044 or permission of the instructor
Recitations: none
Expected class size: 6-10
This course is offered in the spring term, every even year.

BIOINF 2058
Bayesian & Empirical Bayes Computational Methods (BIOST 2064) (3 Credits)
This course provides the students with an understanding of both the theory and practice with regard to the EM algorithm, Markov-chain, sampling techniques, importance sampling, and the solution of decision trees. Students gain hands-on experience programming with S-Plus.
Instructor: Roger S. Day, Sc.D.
Days/Times: Tuesdays and Thursdays, 11:30 a.m. to 12:55 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: BIOST 2063
Recitations: none
Expected class size: 6-10
This course is offered in the fall term, every even year.

BIOINF 2059
Bayesian & Empirical Bayes Statistical Methods (BIOST 2063) (3 Credits)
The theoretical foundations of Bayesian and empirical Bayes statistical methods will be presented. The use of these methods in data analysis will be illustrated with specific examples and with discussions of common data analysis issues contrasts and similarities between Bayesian, empirical Bayesian, and classical methods will be evaluated.
Instructor: Roger S. Day, Sc.D.
Days/Times: Tuesdays and Thursdays, 11:30 a.m. – 12:55 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: BIOST 2042, BIOST 2044
Recitations: none
Expected class size: 6-10
This course is offered in the fall term, every odd year.

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 and Takis Benos
Days/Times: TBA
Location: TBA
Prerequisites: Students are expected to have successfully completed Machine Learning, or an equivalent class
Recitations: None
Expected class size: 35
This course is offered in the spring term.

BIOINF 2082
Bioinformatics Journal Club (1 Credit)
This course meets once each week for one hour.  The research being presented will be taken from recent journal papers, specific to the field of bioinformatics and related areas.
Instructor:  Various speakers
Days/Times:  Fridays, 10:00 a.m. to 11:00 a.m.
Location:  M-185 VALE, 200 Meyran Avenue
Prerequisites:  None
Recitations:  None
Expected class size:  35
This course is offered in the spring term.

BIOINF 2101
Probabilistic Methods for Computer-Based Decision Support (ISSP 2070 / INFSCI 2905) (3 Credits)
This seminar provides an introduction to computational approaches for probabilistic modeling and inference. A particular focus is placed on Bayesian networks, although other probabilistic models also will be studied. Medical applications are emphasized, however, the principles are general and no medical knowledge is needed to take the course. The course does not require knowledge of a computer programming language.
Instructor: Gregory F. Cooper, MD, PhD
Days/Times: Tuesdays and Thursdays, 1:30 to 3:00 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: An understanding of basic probability theory would be helpful, but is not required.
Recitations: None
Expected class size: 15
This course is usually offered in the fall term, every even year.

BIOINF 2109  
The Internet and Health Informatics  (NURSP 2087) (3 Credits)
This course is designed to survey applications and technologies for health informatics. Web-based  and multimedia approaches will be emphasized.  The focus will be on the design, development and evaluation of applications that support training, education and access to health information.  The course is taught in a web-enhanced format.
Instructor:  Gilan Saadawi, MD, MS
Days/Times:  Mondays, 1:00 to 4:00 p.m.
Location:  School of Nursing, Rm. 114 Victoria Hall
Prerequisites:  None
Recitations:  none
Expected class size: 10-12
This course is offered in the spring term, every even year. 

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, MS
Days/Times: Mondays and Wednesdays, 5:00-7:55 p.m.
Location: 6048 Forbes Tower.
Prerequisites: No prerequisites.
Recitations: none
Expected class size: 30
This course is offered in the summer term. Special permission from instructor is required for this course. (e-mail mis18@pitt.edu, obtain permission, then obtain signed permission slip from Toni, M-190 VALE)

BIOINF 2111
Cognitive Studies for Health Informatics (3 credits)
This course is intended to serve as an intensive introduction to Human Information Processing and a survey of its applications to Health Care Informatics. The first four weeks present an overview of the basic architecture of the human information processing system. For each of the last twelve weeks of the course, we alternate classes concentrating on underlying basic cognitive science issues and principles, with classes focusing on how these principles and issues apply in medicalinformatics domains, such as medical decision support, design of information systems, and computer-based education for health professionals. Students will learn and applymethods for studying cognitive tasks, such as verbal protocol analysis and cognitive modeling.
Instructor: Rebecca S. Crowley, MD, MS
Days/Times: Tuesdays, 9:00-12:00 noon
Location: 304 UPMC Cancer Pavilion.
Prerequisites: No prerequisites.
Recitations: none
Expected class size: 10-12
This course is offered in the spring term, every even year.

BIOINF 2113
Realtime Outbreak and Disease Surveillance (3 Credits)
Many countries are constructing real-time public health surveillance systems. This work--which is proceeding in an accelerated manner due to the threats of emerging diseases, bioterrorism, and common infectious diseases--can benefit greatly from the expertise of the medical informatics community.
This course on the theory and practice of outbreak detection will present up-to-the minute information about the theory and practice of real-time public health surveillance. This course will cover key topics ranging from the network level to the application level to the organizational level. Specific topics will include functional requirements (e.g., for data, for analysis, for performance), terminology standards, data models, and messaging standards. We will cover algorithms for the automatic detection of epidemics including natural language processing techniques with an emphasis on methods for validation. The experience gained from field deployments of real-time detection systems in Utah, Ohio, Taiwan, New Jersey, Georgia, the Commonwealth of Pennsylvania and other locations will be presented. There will be demonstrations of a surveillance system in operation.
Instructor: Michael M. Wagner, MD, PhD
Days/Times: Tuesdays and Thursdays, 3:30 to 5:00 p.m.
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: Introductory statistics course. The course can be followed by anyone with medical, medical informatics, or public health background. Ideally, the student will already understand the basic concepts of ROC curve analysis, sensitivity, specificity, positive predictive value, statistical significance testing.
Recitations: none
Expected class size: 12-25
This course is offered in the spring term, every even year.

BIOINF 2114  
Introduction to Medical Language Processing (3 Credits)
Biomedical informatics applications may be used for decision support, quality assurance, assistance in research studies, and resource allocation. However, much of the information that drives these applications in free-text format and cannot be manipulated by a computer. Natural language processing (NLP) attempts to automatically retrieve, classify, summarize, or extract information from text.
This class will introduce students to NLP in the medical domain with an emphasis on techniques, applications, and evaluation strategies used in medical language processing (MLP). The class will overlap slightly with the NLP class taught by Diane Litman in Computer Science in that we will spend some time learning about the building blocks of NLP, including syntax, semantics, and pragmatics. However, a large portion of the class will focus on NLP topics peculiar to the medical domain and to evaluation techniques.
Instructor:  Wendy W. Chapman, PhD
Days/Times:  Tuesdays and Thursdays, 10:30 a.m. to 12:00 p.m.
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  BIOINF 2011 Introduction to Biomedical Informatics.
Recitations:  none
Expected class size: 15-20
This course is offered in the spring term, every odd year. 

BIOINF 2116
Algorithms for Computational & Predictive Biomedicine (1 or 3 credits)
This course teaches computational approaches from disparate fields, specifically, machine learning, signal and graph theory.  Fundamental algorithms for pattern recognition, classification, modeling and inference will be presented from each of these fields to provide the students with the ability to identify the best computational approach to solve a biomedical problem at hand.  Each algorithm will be discussed in application to one or more area(s) of computational biomedicine and predictive medicine.  Computational examples would demonstrate modeling and prediction of macromolecular (protein and gene) structures, functions and interactions.  Predictive medicine is an emerging area that studies patterns in genome sequence, and gene and protein expression phenotypes that serve as biomarkers for early detection of disease such as cancer.  Examples will be drawn from this area to demonstrate inference of the genotypic and phonotypic biomarkers through application of relevant algorithms.
Instructor:  Madhavi Ganapathiraju, PhD
Days/Times:  Monday and Wednesdays, 10:00 a.m. to 11:30 a.m.
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  Working knowledge of probability theory, differential calculus and linear algebra.  Students who do not have the prerequisites are also encouraged to attend this course by registering for only 1 credit.
Recitations:  none
Expected class size:  15-20
This course will be offered in the spring term. 

BIOINF 2117
Applied Medical Informatics (2 Credits)
This course is designed to provide an overview of the field of Applied Medical Informatics. Students will learn about the myriad issues that arise when deploying information technology into clinical environments. Various clinical, social, organizational, legal, and technical challenges make deployment a challenge. Learning how others have addressed these challenges will equip the student for applied informatics roles.
Instructor: Richard Ambrosino, MD, PhD, William Hogan, MD, and Steve Hasley, MD
Days/Times:  Wednesdays, 9:00 a.m. to 11:00 a.m.
Location:  M-185 VALE, 200 Meyran Avenue
Prerequisites:  There are no prerequisites.
Recitations:  none
Expected class size:  10-15
This course will be offered in the spring term.

BIOINF 2118
Probability and Statistics for 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, ScD
Days/Times:  Tuesdays/Thursdays, 9:00 a.m. to 10:30 a.m.
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  There are no prerequisites.
Recitations:  none
Expected class size:  10-15
This course will be offered in the spring term.

BIOINF 2119
Artificial Intelligence Foundations of Biomedical Informatics 1 (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 plus guest lecturers
Days/Times: Monday/Wednesday from 1:00-2:30
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: There are no prerequisites.
Recitations: none
Expected class size: 15-20
This course will be offered in the spring term.

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 Center for Biomedical Informatics.
Instructor:  Department of Biomedical Informatics Faculty and Staff
Days/Times:  TBA
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  Discuss with Instructor
Recitations:  None
Expected class size:  20
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)
Days/Times:  TBA
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  Discuss with Instructor
Recitations:  None
Expected class size:  20
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)
Days/Times:  TBA
Location:  M-184 VALE, 200 Meyran Avenue
Prerequisites:  Discuss with Instructor
Recitations:  None
Expected class size:  20
This course could be offered in any given term -- check with Toni Porterfield (tls18@pitt.edu).

BIOINF 2134
Research Writing Practicum (3 credits)
This course will provide a practical overview of how to write a research manuscript. Students will be required to successfully submit a manuscript for publication in order to complete the course.
Instructor: ÊRebecca Crowley, MD, MS
Days/Times:Ê Mondays and Fridays, 1:30 p.m. to 3:00 p.m.
Location: ÊTBA
Prerequisite: Completed data collection for study in research project with approval of both research advisor and course instructor.
Recitations: None
Expected Class Size: 5
This course will be offered during the fall term.

BIOINF 2200
Introduction to Dental Informatics Research (3 Credits)
This course is intended to expose trainees to the breadth and depth of dental and craniofacial research problems. The course will center on the six research areas of the National Institute of Dental and Craniofacial Research: Craniofacial Anomalies & Injuries; Infectious Diseases & Immunity; Neoplastic Diseases; Chronic Diseases; Biomaterials, Biomimetics, & Tissue Engineering; and Clinical, Behavior, & Health Promotion Research. By learning about specific research problems in dentistry, trainees will be able to identify which informatics-related solutions would be most helpful to solve them. In this course, we expect trainees to develop several ideas for their Master's Thesis or other research projects.
Instructor:  Titus K.L. Schleyer, DMD, PhD, and Heiko Spallek, PhD
Days/Times:  TBA
Location:  Salk Hall
Prerequisites:  None
Recitations:  None
Expected class size:  2-4
This course is offered in fall or spring term (as per instructor decision). 

BIOINF 2201
Dental Information Systems Infrastructures (3 Credits)
This series of seminars and assignments is centered on the management of large-scale information technology infrastructures, with practical experience provided in management of large user bases, help desk management, systems management, end user training, disaster prevention and recovery, and computer security. The course will emphasize practical exercises within the information systems infrastructure at the School of Dental Medicine. The course also includes several special topics related to dental informatics in academia and industry.
Instructor:  Titus K.L. Schleyer, DMD, PhD, and Heiko Spallek, PhD
Days/Times:  TBA
Location:  Salk Hall
Prerequisites:  None
Recitations:  None
Expected class size:  2-4
This course is offered in fall or spring term (as per instructor decision). 

BIOINF 2202
Dental Informatics Seminar (3 Credits)
In this course, students will review current research projects of the Center of Dental Informatics and seminal research in dental/medical informatics. Participants will critically evaluate studies, methodologies, and results. During the course, students will prepare and conduct a joint research project of limited scale and scope resulting in a publishable paper or report. The course also includes other topics of interest for dental informaticians in academic careers.
Instructor:  Titus K.L. Schleyer, DMD, PhD, and Heiko Spallek, PhD
Days/Times:  TBA
Location:  Salk Hall
Prerequisites:  None
Recitations:  None
Expected class size:  2-4
This course is offered in fall or spring term.  

BIOINF 2203
Dental Informatics Masters Thesis Research (3 Credits)
Dental informatics trainees will be expected to register for this mentored research experience with dental informatics faculty while they are working on their research project/thesis. This course emphasizes interdisciplinary projects that integrate several domains. Research topics may include information needs and retrieval, decision support, intelligent agents, computer-based patient records and educational applications. Special emphasis is placed on applying informatics research methods to ongoing research projects at the School of Dental Medicine.

(Note:  For Health Services Research (HSR) concentration, see specific curriculum at http://www.dbmi.pitt.edu/trainingprogram/hsr-ms.html.

BIOINF 2480 (1-6 credits)
Master's Thesis/Project Research

BIOINF 2990 (1-14 credits)  
Master's Independent Study

BIOINF 2992
Information in Radiological Imaging (3 credits)
This course provides an introduction to the nature of information in radiological images. Standard clinical techniques will be examined as well as current radiology research topics. Three basic units will be covered: image formation, image postprocessing, and image quality assessment.
Instructor: Brian E. Chapman, PhD
Days/Times: TBA
Location: M-184 VALE, 200 Meyran Avenue
Prerequisites: None
Recitations: None
Expected class size: 20-25
This course is usually offered in the spring term, every odd year.

BIOINF 2993 (1-9 credits)
Master's Directed Study

BIOINF 3990 (1-14 credits)
Doctoral Independent Study

BIOINF 3995 (1-9 credits)
Doctoral Directed Study

BIOINF 3998 (3 credits)
Doctoral Teaching Practicum

BIOINF 3999 (1-9 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)