PMEDPRA10 Clinical Studies A, Placement in the Ambulance Service Course description

The class covers foundations and recent advances of Machine Learning from the point of view of Statistical Learning Theory. The goal of this class is to provide students with the practical skillset to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

No formal requirements over and above the admission requirements.

After completing the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• can explain how medical equipment is used for assessment, diagnostics and treatment of acutely ill and injured patients
• can describe responsible drugs administration in relation to laws, regulations and local guidelines
• can explain how fundamental evidence-based guidelines are used in examinations, treatment and diagnostics of acutely ill and injured patients
• can explain the relevance of the Patients Rights’ Act in patient-centred care
• can explain the relevance of the Health Personnel Act in patient-centred care
• can describe the Patient Records Act
• can describe the significance of the Act relating to Control of Communicable Diseases in patient-centred care
• can describe the practice placements locations general routines and guidelines

Skills

The student

• can carry out routine daily and weekly tasks at the practice placement location in accordance with local guidelines
• masters relevant diagnostic medical equipment, including the transfer of electrocardiography (ECG) to the relevant receiving authority
• can apply risk assessment principles and implement measures to maintain their own, patients’ and other people’s health and safety
• masters basic use of infection control equipment
• can use communication tools in accordance with applicable national guidelines and procedures and explain the use of them and other relevant IT tools in operational work
• can carry out patient conversations based on basic communication principles
• can assess and implement necessary interventions based on the primary survey of the acutely ill or injured patients in a professional and caring manner
• can facilitate the patient’s consent to health assistance independent of age, physical condition, maturity and experience
• can collect, critically assess and process patient data in accordance with relevant regulations of confidentiality
• can interact with patients, next of kin, colleagues, partners and others, independent of gender, age and ethnic background
• masters basic lifting and handling techniques
• can, in cooperation with the supervisor, hand out and administer pharmaceuticals to patients
• can monitor drug intake and observe possible immediate reactions to the administered drug
• masters the dilution of drugs
• can, in cooperation with the supervisor, assess and treat acutely ill or injured patients on the basis of signs and symptoms identified in the secondary survey in a professionally responsible and caring manner
• can cooperate with the supervisor in choosing the level of care and destination
• can document relevant and necessary information about the patient in the patient care report form
• can cooperate with the supervisor on patient handover

General competence

The student

• can handle hygiene and infection principles to provide responsible health care
• can show respect and care in a safe and efficient manner that fosters good relations with patients, next of kin, colleagues and partners
• can identify and reflect on laws that are relevant for professional practice
• can identify and reflect on ethical issues in dealings with patients, next of kin, colleagues and partners
• can reflect on their own learning strategies
• can handle feedback and guidance
• can reflect on interdisciplinary cooperation

This course is divided into two parts. The first part with focus on covering the principles of Statistical Learning. Different seminars will be given on the different methodological aspects of Statistical learning, mainly, supervised learning and unsupervised learning.

The second part will focus on the students completing a programming project. This is a real data analysis problem, where the student is asked to carry out the analysis using the tools and techniques from the course and hand in a report documenting the steps he has taken in the analysis. The ultimate goal is to build a predictive model.

The project report will consist of at least 25 pages and max 60 pages.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The following required coursework must be approved before the student can take the exam:

At least 80% of the lab assignments need to be approved.

50% of the grade based on the individual project (25 - 60 pages) 50% of the grade based on individual oral examination.

Both exams must be passed in order to pass the course. The oral exam cannot be appealed.

None.

For the final assessment a grading scale from A to E is used, where A denotes the highest and E the lowest pass grade, and F denotes a fail.

Two internal examiners. External examiner is used periodically.

The participants are expected to know linear algebra, basic functional analysis, and basic concepts in probability theory.