Caricamento in corso...
Decision aid for personalized patient care
01HZJMV
A.A. 2024/25
Course Language
Inglese
Degree programme(s)
Master of science-level of the Bologna process in Ingegneria Biomedica - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 33 |
| Esercitazioni in laboratorio | 27 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Balestra Gabriella | Professore Associato | IBIO-01/A | 16,5 | 0 | 9 | 0 | 2 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | ING-INF/06 | 6 | B - Caratterizzanti | Ingegneria biomedica |
|---|
2024/25
The course will focus on specific applications of machine learning
introduce the students to the development of intelligent systems for biomedical data classification and interpretation. The applications cover all bioengineering areas. Topics include feature extraction, feature selection, main characteristics of a classifier, optimization, computational intelligence e statistical learning.
The laboratory activities provide competencies on classifier construction and validation.
In the end, the student will be able to solve problems of medium difficulty.
During the last 20 years Clinical guidelines and Clinical pathways improved the quality of care received by the patients. They guarantee that a good standard care is provide to every patient. The next step is to personalize patient treatment and monitoring using clinical status, genetic variables, … This step requires the use of tools able to extract the right information at the right moment from hundreds of data.
The aim of the course is to present innovative applications that can be used in the routine clinical processes to personalize patient care.
The laboratory work will allow the students to apply the methods to realistic problems.
The students will
- learn the main applications in the field of decision aid for personalized patient care and the challenges that needs to be addressed;
- different AI metodi that can be applied in the field;
- how to use these methos.
The students will
- learn the main applications in the field of decision aid for personalized patient care and the challenges that needs to be addressed;
- different AI metodi that can be applied in the field;
- how to use these methos.
None
None
Introduction: Decision Theory, The evolution of AI from Turing test to ChatGPT, The evolution of healthcare from EBM to Precision medicine
Optimization: meta-heuristics and soft computing methods
Advanced machine learning methods
Deep learning
Applications:
- Digital Twins: development steps, examples of applications for patient monitoring and telemedicine
- Data aggregation for monitoring patient status
- Advanced methods to automatically extract information from clinical records and Electronic Health Record (EHR) for precision medicine
Laboratory work: will address aspects related to the applications using R, Matlab, Phyton tools
Introduction: Decision Theory, The evolution of AI from Turing test to ChatGPT, The evolution of healthcare from EBM to Precision medicine
Optimization: meta-heuristics and soft computing methods
Advanced machine learning methods
Deep learning
Applications:
- Digital Twins: development steps, examples of applications for patient monitoring and telemedicine
- Data aggregation for monitoring patient status
- Advanced methods to automatically extract information from clinical records and Electronic Health Record (EHR) for precision medicine
Laboratory work: will address aspects related to the applications using R, Matlab, Phyton tools
Lesson: 33 hours
Laboratory work: 27 hours
Lesson: 33 hours
Laboratory work: 27 hours
Scientific documents
Scientific documents
Slides; Video lezioni dell’anno corrente;
Lecture slides; Video lectures (current year);
Modalità di esame: Elaborato progettuale in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Group project; Computer-based written test in class using POLITO platform;
...
The exam score is obteneid summimg:
- written test [24 points] to evaluate competencies
18 multiple choice questions (each correct answer is 1 point, failed answer -0.5 points)
2 open answer questions (3points each question)
- Laboratory work [9 points]
Valutazione del lavoro svolto durante i laboratori da parte del gruppo [14pt]– Valuta l’Autonomia di giudizio, le Abilità comunicative e la Capacità di lavorare in un team
relazioni dei LAB2, LAB3,LAB4,LAB5 (8 punti totali)
presentazione tramite caricamento di un filmato mp4 della relazione del LAB6: 3 punti ottenuti tramite peer review
3 punti ottenuti dalla valutazione delle docenti
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Group project; Computer-based written test in class using POLITO platform;
The exam score is obtained summing:
- written test [24 points] to evaluate competencies
18 multiple choice questions (each correct answer is 1 point, failed answer -0.5 points)
2 open answer questions (3points each question)
- Laboratory work [9 points] to evaluate (a) the abilities in using the methods and developing a small project and (b) developing soft skill like working on a team and presenting results
Reports of the work will be upload in moodle
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.