Analytics for Better Health

This module is part of the Graduate Certificate in Health Informatics.

Course Information

Dates: 19 January – 19 April 2024, every Friday

Time: 6pm – 9pm (SGT)

Venue: MD1 Tahir Foundation Building, National University of Singapore (map)

SSG Course Code: TGS-2020507482

Overview

This module will cover major topics in healthcare analytics, including clinical related analytics (e.g., diseases, medication, laboratory test, etc) and healthcare operations related analytics (e.g., resource planning, care process analytics and improvement, etc). Participants will learn about the different healthcare analytics areas and select and apply the appropriate analytical techniques to address healthcare-related questions.

Learning Outcomes

  • Relate analytics results with business value in healthcare sector
  • Understand the key elements to build a successful healthcare analytics solution
  • Understand data source, data collection, data cleansing in healthcare analytics
  • Understand the key problems and challenges in healthcare clinical analytics area
  • Master the common analytics techniques used in healthcare analytics, and be able to select the right techniques for different healthcare analytics problems
  • Understand the state-of-the-arts in healthcare analytics

Who Should Attend?

Individuals interested in understanding the roles of IT and data analytics in healthcare, and in applying domain knowledge and skills to promote public health and improve patient care in their workplace.

Assessment & Certification

This is a SkillsFuture Singapore (SSG) approved course. As part of SSG funding requirements, participants have to achieve at least 75% attendance and pass all assessment(s)/examination(s).

An electronic certificate will be issued to all participants who complete the course:

  • Certificate of Competence – Attain a ‘C’ grade or above and meet a minimum of 75% attendance
  • Certificate of Participation – Attain a grade lower than ‘C’ and meet a minimum of 75% attendance

Course Instructors

Feng Mengling
Assistant Professor Feng Mengling

Dr Feng’s research is to develop effective data analytics and Artificial Intelligence (AI) solutions to extract actionable knowledge to improve the quality of care. His research brings together concepts and tools across machine learning, optimization, signal processing, statistical causal inference and big data management. In particular, he has been publishing on physiological signal forecasting, modeling of disease progress trajectory, dynamic patient phenotyping, statistical understanding of treatment effects and management of heterogeneous medical big data. Dr Feng works closely with clinicians around the world, and he also collaborates with major healthcare and IT companies, such as MSD, Philips and SAP. Dr Feng’s work was recognised by both well-established journals, such as Science Translational Medicine, JAMA, Intensive Care Medicine and top international conferences, such as KDD, AAAI and AMIA.

View his full profile here.

Course Fee

Subsidy available Fee payable after subsidy*
International Participant – S$8257.68
Singapore Citizen aged 39 years & below / Permanent Resident SSG Funding S$2477.30
Singapore Citizen aged 40 years & above SSG Funding & Mid-Career Enhanced Subsidy S$948.10
Singapore Citizen / Permanent Resident Sponsored by SMEs SSG Funding & Enhanced Training Support for SMEs S$948.10

Remarks:

  • All enrolled candidates will also be required to pay a Student Services Fee of S$25.46*.
  • Self-Sponsored participants can use their SkillsFuture Credits to pay for or offset course fees. SkillsFuture Credit claim has to be submitted to SkillsFuture Singapore (SSG) within 60 days before course commencement. SSG will not allow claims to be made after the first day of the course.
  • The University reserves the right to review and adjust the course fees and make changes to the programme structure and requirements as necessary and accordingly without prior notice.

*Prices stated above are inclusive of 8% GST. 

Application

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