MASAKPRA22 Praksisstudier i akuttsykepleierens funksjons- og ansvarsområder, trinn 3 Emneplan

Studenten skal gjennomføre praksisstudier ved akuttmottak, samt ved andre avdelinger som tilbyr vurdering, overvåkning og behandling av akutt og/eller kritisk syke pasienter (for eksempel overvåkningsavdelinger, postoperative avdelinger, prehospitale tjenester, akuttpsykiatrisk avdeling etc). Som minimum bør studenten ha praksis ved ett stort og ett mindre akuttmottak, samt en overvåknings/postoperativ avdeling i løpet av studiet.

Studenten skal med noe veiledning ivareta akuttsykepleierens funksjons- og ansvarsområder. Det forventes at studenten viser progresjon i kunnskap, ferdigheter og generell kompetanse. Det for ventes at studenten viser klar faglig fremgang, tar ansvar og i økende grad viser evne til å gjøre selvstendige vurderinger i utøvelsen av akuttsykepleie.

Praksisstudiene skal tilrettelegges slik at studenten etter gjennomført studium har fått erfaring med og utviklet handlingskompetanse i akuttsykepleie i forhold til alle hyppig forekommende sykdomstilstander, sentrale behandlingsformer og ulike pasientsituasjoner.

The course focuses on the use of artificial intelligence technology as well as image, video and sound analysis to meet the user's needs, as well as handle ambiguous interaction situations.

Admission requirements.

A student who has completed this course should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of this course the student

• has specialized knowledge of intelligent systems and their role in intelligent user interfaces
• has specialized knowledge of ontologies and the semantic gap
• has specialized knowledge of the applications of smart home technology

Skills

On successful completion of this course the student

• can apply classification and categorization to organize data, particularly with k-means, neural networks, fuzzy logic and neuro-fuzzy classification
• can apply optimization techniques to find good solutions to problems using hill climbing, Tabu search and genetic algorithms
• can apply intelligent techniques, such as association rules to implement recommender systems
• can apply techniques from sound analysis, image analysis and video analysis to realize multi-modal input

General competence

On successful completion of this course the student

• can analyse ethical aspects of automatic collection, storage, automatic interpretation and use of person-related measurements
• can analyse opportunities and limitations associated with intelligent systems for given problems

This course is organized as a series of lectures, seminars discussing examples from research literature, and practical work. Lectures cover central theories in artificial intelligence, sound, image and video analysis followed by practical problem solving. Students present research papers in class, using theory to solve new and real problems and developing working prototypes.

• Six obligatory assignments. Students work on each assignment individually.
• Individual oral presentation of research article (45 minutes with questions).

• Project examination in groups. The examination consists of a project report (3000-4000 words). This part of the examination counts 90% of the final grade.
• Group presentation with demo (30 minutes for each group). This part of the examination counts 10% of the final grade.

Each group may consist of up to three candidates. Both exams must be passed in order to pass the course.

The grade for the group presentation cannot be appealed.

All aids.

A grading scale of A (highest) to F (lowest) where A to E is a pass grade and F is a fail grade.

Two internal examiners will assess the report and oral presentation. External examiner is used periodically.