Medical and Health Informatics
6 Credits (ECTS)
Medical and Health Informatics
Get 70% back via Tax Credit
€683 refund on this module
Module Description
This module has two parts. The first part of the module will equip learners with the basic understanding of Healthcare Data Analytics, Biomedical Image Analysis and Natural Language, Processing and its applications in various sectors including Electronic Health Record, Mining of Sensor Data in Healthcare, Biomedical Signal Analysis and Data Mining for Clinical Text etc.
Learners will also learn various components of Electronic Health Records, the Biomedical- Social Media Analytics for Healthcare etc.
The second part of the module will equip learners with the Advanced Data Analytics for Healthcare and Applications and some practical aspects of Healthcare systems.
It will include Clinical Prediction Models and Data Analytics for Pervasive Health- Fraud Detection in Healthcare etc.
Entry Requirements
Candidates who apply for this course must possess one of the following:
- a Level 6 degree related to AI/Computer-Science/Mathematics/Electronics;
OR
- a Level 6 degree not related to AI/Computer Science/Mathematics/Electronics and a minimum of two years’ relevant experience;
OR
- a Level 5 diploma or higher diploma and five years’ of relevant work experience.
Target Audience
This course is targeted at:
- Industry professionals working in different domains, including Technology, Engineering, Science, IT, Finance, Accountancy, Management, Marketing, Insurance, Banking, Gaming, Healthcare, Medicine, Pharmaceutical, Human Resources, Psychology, Blockchain, Legal, Administration, Policy Making, Digital Art, Archaeology, Architecture, Education and other related areas.
- Recent graduates with degrees in Computer Science, Technology, Marketing, Finance, Economics, Accountancy, Management, HR, Law, Engineering, Science, Medicine, Psychology, Digital Art, Game Development, Archaeology, Architecture or Business.
- Mid-career-break professionals looking for opportunities to return to or change their career.
The target group may also be extended to positions such as that of wedding manager, transport manager, maintenance manager, operations manager, marketing manager, conference manager and even that of general manager.
Career Paths
The programme prepares for positions such as and not limited to:
- Compliance Supervisor/Manager/Officers
- Anti-Money Laundering Managers
- Money Laundering Reporting Officer and their deputies
- Law enforcement agencies and supervisory and regulatory authorities
- Managers with responsibilities for internal AML controls
- Risk Managers
- Director of Compliance
How you’ll be assessed
The course comprises:
- Evening classes for part-time courses.
- Classes held throughout the day for full-time courses.
- Guided learning, presentations, comprising synchronous online discussions, tutorials and/or videos.
- Self-study hours comprising research, reading and assignment work.
Assessment
Assessment is carried out via two mandatory components:
- Modular Assessment
- Summative Assessment
The programme includes different forms of assessment which allow for and promote students’ critical engagement. The formative and summative assessment tasks may include an in-class assignment and/or a home-based written assignment using diverse assessment tools which may take the form of online and in-class discussions, examinations, case studies, reports, proposals, essays, and presentations, etc., as applicable to the diverse modules.
Additional Info
Upon successful completion of this course, students will be eligible for a 70% refund of the cost through the ‘Get Qualified’ scheme.**
Due to the modular structure of the course, you may also opt to take individual modules as stand-alone. The entry requirements still apply.***
*Prices are applicable to students who reside in Malta at the time of applying.
**Terms and conditions apply.
Learning Outcomes
Competences:
At the end of the module/unit the learner will have acquired the responsibility and autonomy to:
- Exhaustively evaluate the requirements for Healthcare Data Analytics.
- Rigorously assess potential challenges and barriers for adoption of Electronic Health Records (EHR).
- Understand the mining of Sensor data in healthcare.
- Thoroughly understand and apply Natural Language Processing and Data Mining for Clinical Text.
- Critically analyse the Practical Systems for Healthcare and Data Analytics for Pervasive Health, including Fraud Detection in Healthcare.
Knowledge:
At the end of the module/unit the learner will have been exposed to the following:
- Discuss the concepts related to Medical and Healthcare Data Analytics.
- Explain Clinical Prediction Models and application of Visual Analytics for Healthcare.
- Identify Analytics of Natural Language Processing including data mining for clinical text.
- Identify and discuss applications and Practical Systems for Healthcare.
Key indicative topic areas cover:
- Medical and Healthcare Data Analytics
- Biomedical Image Analysis
- Analytics of Natural Language Processing
- Advanced-Data Analytics
- Medical and Healthcare Data Applications
Skills:
At the end of the module/unit the learner will have acquired the following skills:
- Judiciously apply Advanced Data Analytics Techniques for Healthcare data.
- Carry out Review of Clinical Prediction Models.
- Exhaustively evaluate the requirements for Healthcare Data Analytics.
- Thoroughly understand the Mining of Sensor Data in Healthcare.
- Critically evaluate the Practical Systems for Healthcare and Data Analytics for Pervasive Health, including Fraud Detection in Healthcare.
Judgment Skills and Critical Abilities:
The learner will be able to:
- Collect, analyse and interpret data/information to support arguments, and to develop and apply ideas.
- Communicates ideas, problems, and solutions to both specialist and non-specialist audiences using a range of techniques involving qualitative and quantitative information to sustain arguments.
Module-Specific Learner Skills
The learner will be able to:
- Undertake independent and self-directed study through primary and secondary research.
Module-Specific Digital Skills and Competences
The learner will be able to:
- Navigate through the online learning platform to find assignments, discussion boards, literature, tutorials etc.