Servizi per la didattica
PORTALE DELLA DIDATTICA

Clinical informatics

01RXMMV

A.A. 2022/23

Lingua dell'insegnamento

Inglese

Corsi di studio

Corso di Laurea Magistrale in Ingegneria Biomedica - Torino

Organizzazione dell'insegnamento
Didattica Ore
Docenti
Docente Qualifica Settore h.Lez h.Es h.Lab h.Tut Anni incarico
Collaboratori
Espandi

Didattica
SSD CFU Attivita' formative Ambiti disciplinari
ING-INF/06 6 B - Caratterizzanti Ingegneria biomedica
2018/19
Clinical informatics, also known as healthcare informatics, is the study and use of data and information technology to deliver health care services and to improve patients’ ability to monitor and maintain their own health. Clinicians, patients, and caregivers are the beneficiaries of the data analysis and clinical decision support involved in this field. The course will introduce the student to the methods used: • To collect, store, and analyze health care data, • To support clinical decisions, including summarization, provision of evidence, and active decision support • To optimize the flow of information and coordinating it with care providers’ and patients’ workflows to maximize patient safety and care quality
Clinical informatics, also known as healthcare informatics, is the study and use of data and information technology to deliver health care services and to improve patients’ ability to monitor and maintain their own health. Clinicians, patients, and caregivers are the beneficiaries of the data analysis and clinical decision support involved in this field. The course will introduce the student to the methods used: • To collect, store, and analyze health care data, • To support clinical decisions, including summarization, provision of evidence, and active decision support • To optimize the flow of information and coordinating it with care providers’ and patients’ workflows to maximize patient safety and care quality
At the end of the course, the student will be able to analyze and compare different clinical process, to organize datawarehouses and apply data mining methods, and to develop clinical decision support systems.
At the end of the course, the student will be able to analyze and compare different clinical process, to organize datawarehouses and apply data mining methods, and to develop clinical decision support systems.
Computational intelligence and machine learning methods.
Computational intelligence and machine learning methods.
01. Introduction 02. Clinical pathways, patient journey, process mining 03. Clinical decision support systems 04. Data mining and Knowledge extraction 05. Patient empowerment Process analysis, Decision support systems, Data mining and Knowledge extraction are the topics of the laboratory work.
01. Introduction 02. Clinical pathways, patient journey, process mining 03. Clinical decision support systems 04. Data mining and Knowledge extraction 05. Patient empowerment Process analysis, Decision support systems, Data mining and Knowledge extraction are the topics of the laboratory work.
The course consists of 30 hours in class and 30 hours of work in laboratory
The course consists of 30 hours in class and 30 hours of work in laboratory
Slides
Slides
Modalità di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Written test; Compulsory oral exam; Group essay;
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: Written test; Compulsory oral exam; Group essay;
The exam consists of a written test and an oral presentation of the laboratory work. The first is performed singularly; the second involves the whole group. The grade is obtained by summing: a) Written test lasting 1 hour and consisting in 3 open answer questions, max 24 points (the minimum score that must be obtained for each question is equal to 3 points, otherwise the exam is failed). It evaluates the knowledge acquired on the methods. During the text the student is not allowed to use his/her notes or any other material. b) Oral presentation of the laboratory work: max 3 points. It evaluates the ability of the students to present the results of their work. c) Written reports of the laboratory work: max 6 points. It evaluates the ability in using the methods.
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.
Esporta Word


© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti