PhD Science - Medical Informatics

The PhD program in Medical Informatics is based on extensive research work to allow for the better understanding of the subject. The candidates conducts research and find out about the practicality of the field in order to pursue advancing roles in the same. This is the reason professionals in medical informatics are most likely to take up on this PhD program.

Queensville University offers a viable chance of exploring methodologies both current and emerging in the health IT systems. It helps them in developing a better understanding of the program in all and induces critical learning which helps in the further improving of the processes when the candidate completes the course and steps out in the practical environment.

 

What will you learn from this course

Through this PhD program you will be able to do the following:

  • You will work on research based project.
  • You will able to apply engineering principles within and outside the classroom
  • Develop skill set relevant to employers need
  • Students are able to take leadership roles and move up the career ladder more quickly
General studies
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Information Research Strategies 2 Introduction to information research including electronic resources. This course is designed to help researchers locate, evaluate, and use information. It includes exploration of the research process, search strategies, locating resources, source documentation, and organization of research.
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Statistical Modeling and Analysis for Complex Data Problems 2 reviews some of today's more complex problems, and reflects some of the important research directions in the field. Work in areas of theoretical statistics, applied statistics, probability theory, and shastic processes – present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.
Optimal Experimental Design 2 Introduces the philosophy of experimental design, provides an easy process for constructing experimental designs, calculating necessary sample size using R programs and teaches by example using a custom made R program package
Mathematical Modeling 2 complete range of basic modeling techniques: it provides a consistent transition from simple algebraic analysis methods to simulation methods used for research. Such an overview of the spectrum of modeling techniques is very helpful for the understanding of how a research problem considered can be appropriately addressed.
Research Methods and Design 2 Learners gain a thorough understanding of statistical tests appropriate to their dissertation topic and design, how to interpret the results of the tests and how to conduct follow-up analyses, as appropriate. This course includes guidelines and "best practices" for collecting data. Power analysis, what it is, why do it, and how to use available software is covered. Data preparation, use of software to analyze data, and understanding the calculated results are covered. Experience with computer-based statistical analysis techniques is stressed. Emphasizes what is applicable to the Learner's proposed research questions, design, construct/variable definitions and properties of measurements. Satisfactory/Unsatisfactory grade only.
Dissertation Planning, Writing, and Defending 2 step-by-step through the dissertation process, with checklists, illustrations, sample forms, and updated coverage of ethics, technology, and the literature review.
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Statistical Modeling and Analysis for Complex Data Problems 2 Students will learn statistical methods and skills for analyzing large-scale gene expression data resulting from high-throughput technologies, become familiar with various bioinformatics tools and resources, and develop useful working knowledge of how to analyze genetic data.
Data Analysis and Graphics Using R 2 This course is designed to teach the essential skills for analyzing experimental data, and in particular, generating informative graphics that can be used directly in reports, theses, dissertations, and manuscripts.
Tools of Bioinformatics 2 Computer applications in molecular biology. Hands-on experience with using popular computer programs for DNA, RNA and protein sequence analysis, database management, data editing, assembly, and organization, multiple sequence comparisons, protein structural analysis, evolutionary relationships of genes, use of Internet for data retrieval, comparison and analysis.
Biometeorology 2 Studies the quantitative exchange of radiation, heat, mass and momentum between the atmosphere, vegetation, and soils with an emphasis on forest processes. Other topics include the physical and biological controls of water vapor exchange and carbon dioxide exchange, models of stand-scale evaporation, transpiration, photosynthesis and respiration.
Ethical, Legal and Social Issues in Biotechnology 2 Covers Ethics and concepts of dealing with Biological data and medical information including gene detailing and controles of information handling and release.
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Bioinformatics 3 Presents mathematical models in bioinformatics and describes the biological problems that inspire the computer science tools used to handle the enormous data sets involved and covers the mathematical and computational methods, the practical applications, and mathematical presentation, with emphasis on motivation through biological problems and cross applications.
Fundamentals of Healthcare Programming 3 Demonstrates that biomedical professionals with fundamental programming knowledge can master any kind of data collection and provides access to data, nomenclatures, and programming scripts and languages that are all free and publicly available. Describes the structure of data sources used, with instructions for downloading, Includes a clearly written explanation of each algorithm, Offers equivalent scripts in Perl, Python, and Ruby, for each algorithm, Shows how to write short, quickly learned scripts, using a minimal selection of commands, Teaches basic informatics methods for retrieving, organizing, merging, and analyzing data sources, Provides case studies that detail the kinds of questions that biomedical scientists can ask and answer with public data and an open source programming language, Requiring no more than a working knowledge of Perl, Python, or Ruby, this subject will have students writing powerful programs in just a few minutes. Within the course, students will find descriptions of the basic methods and implementations needed to complete many of the projects they will encounter in their biomedical career.
Informatics in Medical Imaging 3 This course provides a comprehensive survey of the field of medical imaging informatics. In addition to radiology, it also addresses other specialties such as pathology, cardiology, dermatology, and surgery, which have adopted the use of digital images. The course discusses basic imaging informatics protocols, picture archiving and communication systems, and the electronic medical record. It details key instrumentation and data mining technologies used in medical imaging informatics as well as practical operational issues, such as procurement, maintenance, teleradiology, and ethics.
Probabilistic Modeling in Medical Informatics 3 Provides a self-contained introduction to the methodology of Bayesian networks, demonstrates how these methods are applied in bioinformatics and medical informatics with an introduction, tutorials, advanced applications and case studies to the methodology of probabilistic modeling, bioinformatics, and medical informatics.
Clinical Information Systems 3 Covers The Evolution of Health Information Systems; Frameworks: A Collection of Business Objects; Frameworks: A Collaboration of Objects; The Patient Component; The Act Component; The Medical Record Component; The Knowledge Components; The Resource Management Component; The Security Component and Imaging Management and Integration.
Health Information Technology & Management 3 Provides a comprehensive understanding of the history, theory, and potential benefits of health information management systems, this course helps students understand the connectivity and applications that make up the health information systems of today and of tomorrow. Focuses on the contents of patient's medical record, automated systems, filing systems, health data mining, digital retrieval, international coding, and medical abstract.
Comparative Health Information Management 3 Covers health care practice and information management in a wide variety of settings, from free-standing and hospital-based ambulatory clinics to veterinary offices and correctional facilities, and the challenges associated with managing the information flow among various sites.
Medical Devices Design for Six-Sigma 3 Integrates concept and design methods such as Pugh Controlled Convergence approach, QFD methodology, parameter optimization techniques like Design of Experiment (DOE), Taguchi Robust Design method, Failure Mode and Effects Analysis (FMEA), Design for X, Multi-Level Hierarchical Design methodology, and Response Surface methodology, Covers contemporary and emerging design methods, including Axiomatic Design Principles, Theory of Inventive Problem Solving (TRIZ), and Tolerance Design, Provides a detailed, step-by-step implementation process for each DFSS tool included, Covers the structural, organizational, and technical deployment of DFSS within the medical device industry, Includes a DFSS case study describing the development of a new device and Presents a global prospective of medical device regulations
Essentials of Health Information Management 3 Focuses on the description of health care delivery and contents of medical records, provides skills and professional training at the field of Health Information Technology, hands on familiarity with health care facilities settings, with a comprehensive study of automated health and medical information systems, and detailed study of health information at ambulatory services, diagnostic departments and inpatient floors. It also provides hands on training on numbering, coding, medical abstract and records analysis and medical auditing.
Knowledge Management and Data Mining in Biomedicine 3 Mapping Medical Informatics Research.- Bioinformatics Challenges.- Medical Concept Representation.- Standards in Medical Informatics.- Information Retrieval and Digital Library.- Genomics Information Retrieval.- Managing Information Security and Privacy in Health Care.- Ethical and Social Challenges in Medical Informatics.- Characterizing Biomedical Concept Relationships.- Anatomic Images for the Public -3D Medical Informatics.- Medical Oncology .- Semantic Parsing and Knowledge Representation in Biomedicine.- Semantic Text Parsing for Patient Records.- Identification of Biological Relationships from Text Documents.- Creating, Modeling and Visualizing Metabolic Networks: FC Modeler and Path-Binder for Network Modeling and Creation.- Gene Pathway Text Mining and Visualization.- The Genomic Data Mine.- Exploratory Genomic Data Analysis.- Joint Learning Using Data and Text Mining.- Disease Informatics and Outbreak Detection.
General studies
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Concept Paper 2 Ethical issues in research are studied and the Learner evaluates the research plan developed in modules RSH8951-RSH8953 against accepted ethical principles and practices in the field. The material developed in the modules is integrated into a summarizing document called the Dissertation Research Proposal. The proposal is comprised of Chapter I (Introduction), Chapter II (Literature Review), and Chapter III (Methodology). The Learner develops the Dissertation Research Proposal under the supervision of the faculty mentor, with a focus on the conceptual and methodological clarity of the research plan for the Learner's dissertation topic. Once acceptable to the Learner and the faculty mentor, the draft of the Research Proposal is reviewed by the Learner's Dissertation Committee and the University's Ethics Committee. Satisfactory/Unsatisfactory grade only.
Doctoral Comprehensive Examination 2 Assures that the Learner has mastered knowledge of his or her discipline, specialization, and can demonstrate applications of that knowledge before formal candidacy status is granted and research in support of the dissertation is initiated. Satisfactory/Unsatisfactory grade only.
Doctoral Dissertation Research l 2 Continuation of RSH8954-P. The draft of the Dissertation Research Proposal is finalized and approved by the Learner's Dissertation Committee and the University's Ethics Committee. All steps necessary to begin data collection, including any necessary pilot testing, are completed. Candidates for the Ph.D. must maintain continuous enrollment. Satisfactory/Unsatisfactory grade only.
Doctoral Dissertation Research ll 2 Dissertation data are collected and analyzed. Candidates for the Ph.D. must maintain continuous enrollment. Satisfactory/Unsatisfactory grade only.
Doctoral Dissertation Research lll 2 the dissertation process is completed. The manuscript is prepared, accepted by the Learner's Dissertation Committee, and the oral defense is conducted. Candidates for the Ph.D. must maintain continuous enrollment. Candidates must have satisfied all financial obligations to the University and be enrolled at the time the oral defense is conducted. Satisfactory/Unsatisfactory grade only.