MSc in Biomedical Informatics (by Coursework)

BMI5101 - Advanced Biomedical Informatics

Units: 4
Prerequisite(s): Nil

This module covers both the fundamental and advanced principles of biomedical informatics, the field concerned with the acquisition, storage, and use of information in health and biomedicine. The course begins with a basic introduction to health and biomedicine as well as computing concepts and theories including ethics and legal aspects and then moves on to advanced concepts in these topics.

BMI5111 - Capstone Project

Units: 4
Prerequisite(s): NIL

The big data evolution provides an opportunity for managing huge amount of information and acting on it with analytics for improved outcomes. Understanding data science and data analytics in relation to arificial intelligence allow us to remain competitive and relevant in the rapidly changing healthcare landscape. Students need to understand and approach real world issues, from processing and exploring data to provide insights, developing a healthcare data product for their capstone project.

BMI5207 - Medical Data and Data Processing

Units: 4
Prerequisite(s): NIL

This module seeks to introduce data standards, its sources (traditional and contemporary) and applications in healthcare. Some important standards covered here include SNOMED, ICD9\10, HL7, OMOP and other international standards. Features of healthcare databases and processing of data would be covered. Concepts in databases and data mapping would be demonstrated in practice.

BMI5306 - Advanced Agile Project Management

Units: 4
Prerequisite(s): NIL

The current world is volatile, uncertain, complex and ambiguous. Technologies and digitalization are changing at an alarming pace and disruptors are constantly popping up to challenge the incumbents. To survive and grow in this environment, organizations need to be able to learn and adapt rapidly, execute faster, make better use of data and embrace changes aka agility. Agile practices emphasize flexible approaches and early, frequent releases of product to users. It employs an iterative, incremental approach to optimize predictability and control risk. This course offers essential advanced concepts and practices to use agile for building product and project delivery.

IT5001 - Software Development Fundamentals

Units: 4
Prerequisite(s): NIL
Additional Information: 
Lab based
Skills-future funded

This module aims to introduce non-computing students to the principles and concepts of software development at an accelerated pace. Students will be introduced to the basics of programming (control flow, code and data abstraction, recursion, types, OO), development methodology (ensuring correctness, testing, debugging), simple data structures and algorithms (lists, maps, sorting), and software engineering principles. Through hands on assignments and projects, students will learn good software development practices (documentation, style) and experience a typical software engineering cycle.

BMI5102 - Health Sciences for Non-Clinicians *Not for clinicians

Units: 4
Prerequisite(s): Nil

This course introduces the biomedical informatics student to the health sciences. This encompasses an introduction to clinical practice, and an overview of the underlying biology and manifestations of selected disease states. Besides this an overview of the information gathering and reasoning processes used to detect, understand and treat diseases will be provided. This course aims to provide a functional background to non-clinicians who are new to the healthcare system.

BMI5109 - Advanced Scientific Research Methods

Units: 4
Prerequisite(s): Nil

This course provides an introduction to the basic and advanced principles of scientific research methods from literature review to designing research questions with a focus on clinical research informatics. Topics covered include the scientific theory of research, study methodology design as well as qualitative and quantitative research methods and designing a research questions. Students will be provided with a practical approach to designing clinical research, clinical trial administration as well as types of health registries and records.

BMI5110 - Molecular Informatics

Units: 4
Prerequisite(s): Nil

This course provides students with an understanding of Molecular Bioinformatics from the user's perspective. BMI5110 at its core, will focus on the extraction of clinically relevant information from clinical, molecular and biological data, using a variety of bioinformatics applications. It will cover the introductory aspects of bioinformatics and its applications in genomics, pharmacogenomics, transcriptomics, proteomics, molecular pharmacology, and system biology; enabling technologies (including genome-sequencing and DNA microarrays); big data processing (including experimental design and computational and statistical genetics); and more practical experience with big data analysis.

BMI5206 - Advanced Human Factors Engineering

Units: 4
Prerequisite(s): Nil

This course provides provide students with an in-depth understanding of user engineering, human-computer interactions for the design of biomedical informatics applications. A focus on user-centered design as well as usability assessment will be provided.

DL5102 - Digital Agility & Change Leadership

Units: 4
Prerequisite(s): Nil

Digital Agility and Change Leadership enables participants to acquire skill set to lead their organisation with abilities to sense, response and adapt quickly to market changes and evolving customer needs in a complex and volatile digital business environment today. Some topics covered include practices of agile leadership style, implementation of agile practices, adopting digital first mind-set and developing cohesive change leadership strategies to increase organisation’s agility and digital quotient.

IT5003 - Data Structures and Algorithms

Units: 4
Prerequisite(s): If undertaking a Graduate Degree Coursework THEN must have completed IT5001) OR (if undertaking a CPE (Certificate) THEN must have completed IT5001

This course introduces non-computing students to efficient computational problem solving in an accelerated pace. Students will learn to formulate a computational problem, identify the data required and come up with appropriate data structures to represent them, and apply known strategies to design an algorithm to solve the problem. Students will also learn to quantify the space and time complexity of an algorithm, prove the correctness of an algorithm, and the limits of computation. Topics include common data structures and their algorithms (lists, hash tables, heap, trees, graphs), algorithmic problem solving paradigms (greedy, divide and conquer, dynamic programming), and NP-completeness.

Additional Information:
Lab-Based Module
SkillsFuture Funded

IT5005 - Artificial Intelligence

Units: 4
Prerequisite(s): Nil

The study of artificial intelligence, or AI, aims to make machines achieve human-level intelligence. This course provides a comprehensive introduction to the fundamental components of AI, including how problem-solving, knowledge representation and reasoning, planning and decision making, and learning. The course prepares students without any AI background to pursue advanced courses in AI.

IT5006 - Fundamentals of Data Analytics

Units: 4
Prerequisite(s): If undertaking a Graduate Degree Coursework THEN must have completed IT5001 at a grade of at least D) OR (if undertaking a CPE (Certificate) THEN must have completed IT5001 at a grade of at least D
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed BT5126) OR (if undertaking a Graduate Degree Coursework THEN must not have completed IS5126

This course introduces students to the fundamental concepts in business analytics. They can learn how to apply basic business analytics tools (such as R), and how to effectively use and interpret analytic models and results for making informed business decisions. The course prepares students without any analytics background to pursue advanced courses in business and data analytics.

SPH6004 - Advanced Statistical Learning

Units: 4
Preclusion(s): If undertaking a Graduate Degree Coursework or Graduate Degree Research THEN must not have completed ST5229 at a grade of at least D

This course aims to introduce both the conventional machine learning models and the recent deep neural network based models. In the first half of the course, we will cover the more conventional machine learning models ranging from linear models, tree-based models and kernel-based models, and we will explain the fundamentals on how one can optimise machine learning models. In the second half of the course, we will cover the more modern deep neural network based models, such as Deep Neural Network, Convolution Neural Networks, Recurrent Neural Networks, Transformers, etc. We will also introduce the concepts of reinforcement learning and how deep learning can be applied for reinforcement learning.

*From AY23/24*

SPH5409 - Qualitative Methods in Public Health

Units: 4
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed CO5233 at a grade of at least D

This course will familiarize students with various data collection and analytic methods in qualitative research, allowing them to apply appropriate methods with relevant ethical considerations. Students will be guided through each step of the qualitative research process starting with the underlying principles of qualitative approaches and moving on to study design, sampling, data collection and analysis. Students will have hands-on practical experience applying the various data collection methods; as well as learning practical techniques on how to conduct and write the analysis.

MDG5241 - Advanced Statistical Methods for Bioinformatics

Units: 4
Prerequisite(s): NIL

The course will present modern statistical methods for analyzing large-scale -omics data, with emphasis on application to real-world data. A variety of statistical methods and computing approaches will be covered, including hypothesis testing for high-dimensional data, dimension reduction techniques, and machine learning methods commonly employed in -omics data analysis such as tree-based methods and support vector machine. The course will also include in-class computing exercise for model estimation and inference, such as the expectation- maximization algorithm and sampling-based Bayesian inference. The course will end with advanced statistical approaches for the integration of two or more -omics data.

Additional information: 
Has S/U option for relevant Graduate (Research) students only
Included in Semester 1's Course Planning Exercise

BMI5102 - Health Sciences for Non-Clinicians

Units: 4
Prerequisite(s): Nil

This course introduces the biomedical informatics student to the health sciences. This encompasses an introduction to clinical practice, and an overview of the underlying biology and manifestations of selected disease states. Besides this an overview of the information gathering and reasoning processes used to detect, understand and treat diseases will be provided. This course aims to provide a functional background to non-clinicians who are new to the healthcare system.

BMI5106 - Clinical Decision Support Systems

Units: 4
Prerequisite(s): Nil

This course provides an in-depth review of decision supports systems in the healthcare setting. Fundamental concepts such as decision analysis, decision science, and knowledge management will be covered. Aspects from designing to implementing and maintain clinical decision support tools will also be discussed in depth. Additionally, the course will cover Bayesian theory, decision trees, patient utilities, quality of life and cost related to health outcomes in order to contextualise clinical decision making. Finally, practical issues such as overcoming barriers to the adoption of clinical decision support tools will also be covered.

BMI5107 - Advanced Evidence Based Patient Care

Units: 4
Prerequisite(s): Nil

This course provides an in-depth guide to Evidence Based Medicine (EBM) which strongly influences daily clinical practice. Students will be introduced to the core principles of EBM such as the principles of information gathering, evaluating and summarising available evidence to guide patient care. Additionally, principles of original research, systematic reviews and meta-analysis will be covered in this program. Besides this, students will be instructed on commonly used statistical principles and will gain proficiency with statistical software to perform data analysis. Finally, the limitations of the EBM approach, and assessing for bias and error will also be discussed.

BMI5108 - Advanced Value Based Healthcare

Units: 4
Prerequisite(s): Nil

This course introduces participants to “Value Based Healthcare” concepts and framework, and how value based healthcare can be implemented in Singapore’s context. Participants will learn structured data management framework, identification of key clinical quality measurements for specific medical conditions and tracking of improvements in quality and safety.

BMI5201 - Advanced Clinical Data Systems

Units: 4
Prerequisite(s): Nil

To provide students with an overview of the lifecycle of clinical information systems from user requirements from conception to production and maintenance. Topics cover includes various expects of different system development life cycle and some widely use industry standards such as SDLC, ILM and CRISP-DM. Medical device development and regulation for Health Science Authority Singapore (HSA) will be taught. Data derived from medical devices and its effective use will be discussed.

MDG5248 - Ethics of Health Data and Artificial Intelligence

Units: 4
Prerequisite(s): Nil

This course will explore the ways in which ethical values, analysis, and reflection can inform the use of health data and artificial intelligence/machine learning, enabling students to critically engage with ethical nuances and tradeoffs that pervade the area. We will provide general tools for ethical analysis, then apply these to various facets of data and AI ethics: Privacy, consent, public interest, bias, transparency, governance, and public engagement. Focus will be on application of analysis to cases involving health data and healthcare settings, though many of the principles and issues discussed have application much more widely.

IT5005 - Artificial Intelligence

Units: 4
Prerequisite(s): Nil

Additional Information:
Lab-Based
SkillsFuture Funded

The study of artificial intelligence, or AI, aims to make machines achieve human-level intelligence. This course provides a comprehensive introduction to the fundamental components of AI, including how problem-solving, knowledge representation and reasoning, planning and decision making, and learning. The course prepares students without any AI background to pursue advanced courses in AI.

IT5006 - Fundamentals of Data Analytics

Units: 4
Prerequisite(s): If undertaking a Graduate Degree Coursework THEN must have completed IT5001 at a grade of at least D) OR (if undertaking a CPE (Certificate) THEN must have completed IT5001 at a grade of at least D
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed BT5126) OR (if undertaking a Graduate Degree Coursework THEN must not have completed IS5126

This course introduces students to the fundamental concepts in business analytics. They can learn how to apply basic business analytics tools (such as R), and how to effectively use and interpret analytic models and results for making informed business decisions. The course prepares students without any analytics background to pursue advanced courses in business and data analytics.

SPH5007 - Implementing Public Health Programmes and Policies

Units: 4
Prerequisite(s): Nil
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed SPH5004 at a grade of at least D

This is an introductory course to equip graduate students with a theoretical appreciation of implementing health policies and programmes to improve the health and well-being of populations. Students will learn how to interpret data, integrate evidence and implement evidence-based programmes and policies under pro-active supervision. This course will introduce you to the knowledge and skills needed to develop evidence-based programmes and policies. It will also introduce various implementation strategies to ensure adoption and integration of evidence-based health interventions into routine policies and practices within specific settings.

SPH5412 - Economic Methods in Health Technology Assessment

Units: 4
Prerequisite(s): Nil
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed CO5236 at a grade of at least D

This course aims to provide an applied introduction to Health Technology Assessment (HTA) research in order to enable students to begin conducting their own research and/or to understand research conducted by others. Basic principles of conducting HTA and modelling techniques will be covered. Examples of economic analyses that have been used in all stages of HTA research, starting with conceptualizing HTA studies, to cost-effectiveness of particular health technologies, to budget impact will be included. Students will also gain the expertise to critically evaluate economic evaluations, and use the evaluation results to inform decision-making in healthcare policy and resource allocation.

SPH6004 - Advanced Statistical Learning

Units: 4
Prerequisite(s): Nil
Preclusion(s): If undertaking a Graduate Degree Coursework or Graduate Degree Research THEN must not have completed ST5229 at a grade of at least D

This course aims to introduce both the conventional machine learning models and the recent deep neural network based models. In the first half of the course, we will cover the more conventional machine learning models ranging from linear models, tree-based models and kernel-based models, and we will explain the fundamentals on how one can optimise machine learning models. In the second half of the course, we will cover the more modern deep neural network based models, such as Deep Neural Network, Convolution Neural Networks, Recurrent Neural Networks, Transformers, etc. We will also introduce the concepts of reinforcement learning and how deep learning can be applied for reinforcement learning.

DL5102 - Digital Agility & Change Leadership

Units: 4
Prerequisite(s): Nil

Digital Agility and Change Leadership enables participants to acquire skill set to lead their organisation with abilities to sense, response and adapt quickly to market changes and evolving customer needs in a complex and volatile digital business environment today. Some topics covered include practices of agile leadership style, implementation of agile practices, adopting digital first mind-set and developing cohesive change leadership strategies to increase organisation’s agility and digital quotient.

*From AY23/24*

SPH5003 - Health Behaviour and Communication

Units: 4
Prerequisite(s): Nil
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed 1 of CO5203/SPH6006 at a grade of at least D

This course is designed to equip you with the knowledge and tools needed to change health behaviours and improve community well-being within the field of public health. Throughout the course, you will apply various social and behavioural science theories and concepts to analyse and influence health behaviours. Additionally, you will learn how to craft targeted messages for various audiences to foster positive behavioural change, and learn the fundamental principles of programme evaluation to assess the effectiveness of public health programmes. By the end of the course, you will have developed the skills to create conceptual models for designing effective public health promotion interventions and gained an appreciation of how researchers, policymakers and practitioners communicate public health research evidence and implement interventions to promote health.

SPH5416 - Introduction to Integrated Care

Units: 4
Prerequisite(s): Nil

To meet the challenge of an ageing population, increasing chronic diseases and escalating healthcare costs, there is an opportunity for healthcare delivery to be better integrated. However, integrated care is a complex knowledge domain, with macro, meso and micro levels of theory and practice. This course provides students with the theoretical foundation and methodological enablers of integrated care. It introduces students to systematic methods to evaluate integrated care services, systems, pilots and interventions, including methods to enable large scale integration of care. Students will be able to apply learning to strengthen their own integrated care practice or research.

SPH5401 - Health Economics and Financing

Units: 4
Prerequisite(s): Nil
Preclusion(s): If undertaking a Graduate Degree Coursework THEN must not have completed CO5204 at a grade of at least D

This course addresses the economic and financing aspects of the production, distribution, and organisation of health care services and delivery. This includes the structure of health care delivery and insurance markets, demand for and supply of health services, pricing of services, cost of care, financing mechanisms, and their impact on the relevant markets. A special emphasis will be given to market failures and the role of government in the market for health services. Through textbook readings and discussions of seminal articles and more recent empirical applications in health economics, students will learn the economic way of thinking. They will be given the opportunity to showcase these skills through a series of research papers written throughout the semester that will culminate with a final manuscript that provides an in-depth analysis of a critical health issue.

DL5201 - Strategic Thinking and Digital Foresight

Units: 4
Prerequisite(s): Nil

Strategic Thinking & Foresight enables participants to acquire skill set for strategic thinking and robust foresight that enable leaders to identify and implement digital transformation for organisation to realise the opportunities and manage risk of disruptive technological, economic and social change. Some topics covered include application of strategy framework for digital transformation, sense-making techniques, trends, horizon scanning & market driver analysis for digitalisation, scenario planning & strategy development, building digital capacity etc.

DL5302 - Managing Digitalisation Complexity

Units: 4
Prerequisite(s): Nil

Managing Digitalisation Complexity enables participants to acquire skill set to deal with volatility, uncertainty, complexity, ambiguity (VUCA) and rapid changes to sustain competitive advantage in a complex digital economy. Some topics covered include system thinking, complex project management, scaling enterprise product management, strategies for crisis management, driving process and operational excellence, leading networked organisations, managing complexity in global workforce etc.