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ICT for health

01QWYBH

A.A. 2022/23

Course Language

Inglese

Course degree

Master of science-level of the Bologna process in Ict For Smart Societies (Ict Per La Societa' Del Futuro) - Torino

Course structure
Teaching Hours
Lezioni 39
Esercitazioni in laboratorio 21
Teachers
Teacher Status SSD h.Les h.Ex h.Lab h.Tut Years teaching
Visintin Monica Professore Associato ING-INF/03 19,5 0 21 0 7
Teaching assistant
Espandi

Context
SSD CFU Activities Area context
ING-INF/03 6 B - Caratterizzanti Ingegneria delle telecomunicazioni
2022/23
The medium of instruction is English. The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health". The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues. Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.
The medium of instruction is English. The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health". The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues. Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.
Knowledge of: - basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc) - e-health and m-health applications - telemedicine applications - regression techniques - clustering techniques - classification techniques Ability to: - understand the issues of a telemedicine application - apply regression techniques in health problems - apply clustering techniques in health problems - apply classification techniques in health problems - use open-source machine learning software
Knowledge of: - basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc) - e-health and m-health applications - telemedicine applications - regression techniques - clustering techniques - classification techniques Ability to: - understand the issues of a telemedicine application - apply regression techniques in health problems - apply clustering techniques in health problems - apply classification techniques in health problems - use open-source machine learning software
Knowledge of probability theory, linear algebra, optimization techniques
Knowledge of probability theory, linear algebra, optimization techniques.
- Description of some e-health, m-health, and telemedicine applications (2 CFU) on the following topics: smart aging, fitness, Parkinson's disease, EEG, ECG, dermatology, lean in health care (2.1 CFU). - Review of linear algebra and basics on optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - regression techniques: linear regression, PCR, Gaussian processes for regression (0.9 CFU) - clustering techniques: k-means, hierarchical trees, DBSCAN (0.9 CFU) - classification techniques: decision trees and information theory, Hidden Markov Models (0.9 CFU) - Independent component analysis (ICA) applied to EEG (0.3 CFU)
- Description of some e-health, m-health, and telemedicine applications (2.1 CFU) on the following topics: - smart aging, - fitness, - Parkinson's disease, - EEG, - ECG, - dermatology, - lean in health care, - management of emergencies. - Review of linear algebra and basics on optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - regression techniques: linear regression, Gaussian processes for regression applied to score of Parkinson's disease (0.9 CFU) - clustering techniques: k-means, hierarchical trees, and DBSCAN, applied to body images like CTScan or pictures of skin moles (0.9 CFU) - classification techniques: sensitivity, specificity, prevalence, incidence applied to tests based on blood markers; decision trees and information theory applied to detection of an illness like Chronic Kidney Disease (0.9 CFU) - Independent component analysis (ICA) applied to EEG (0.3 CFU)
Lectures will describe the health context and the problem to be solved, then the relevant ICT/learning machine methods to be used to solve the problem are discussed and implemented in Python in the laboratory classes.
Lectures will describe the health context and the problem to be solved, then the relevant ICT methods to be used to solve the problem are discussed and implemented in Python in the laboratory classes.
- Class slides will be available on the portal - K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012 - Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006 - David J.C. MacKay, "Information Theory, Inference and Learning Algorithms" Cambridge University Press 2003 - C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006
- Class slides will be available on the portal - K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012 - Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006 - David J.C. MacKay, "Information Theory, Inference and Learning Algorithms" Cambridge University Press 2003 - C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006
ModalitÓ di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato progettuale individuale;
Exam: Compulsory oral exam; Individual essay; Individual project;
The student must write two reports on the lab activity; together with the report the student must provide the zipped folder with the Python scripts. Each of these reports gets a grade between 0 and 5 (0 if the report is missing), for a total maximum grade equal to 10; the grade depends on the correctness of the results and on the completeness and clearness of the document. The mandatory oral exam consists of 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms and methods described in the lectures and the lab activity. The student gets a grade from 0 to 7 on each question; the grade depends on the ability to describe and critically discuss the learned methods. The grades of the reports and the oral exam are added together to obtain the final grade. The "lode" is given to students with an overall grade 31. The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.
Gli studenti e le studentesse con disabilitÓ 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'UnitÓ Special Needs, al fine di permettere al/la docente la declinazione pi¨ idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Individual essay; Individual project;
The student must write two reports on the lab activity; together with the report the student must provide the zipped folder with the Python scripts. Each of these reports gets a grade between 0 and 5 (0 if the report is missing), for a total maximum grade equal to 10; the grade depends on the correctness of the results and on the completeness and clearness of the document. The mandatory oral exam consists of 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms and methods described in the lectures and the lab activity. The student gets a grade from 0 to 7 on each question; the grade depends on the ability to describe and critically discuss the learned methods. The grades of the reports and the oral exam are added together to obtain the final grade. The "lode" is given to students with an overall grade 31. The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.
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.
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