Data Mining in Healthcare

Code:

OPT42

Acronym:

ECD

Keywords
Classification	Keyword
OFICIAL	Medicine

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Community Medicine, Information and Health Decision Sciences
Course/CS Responsible:	Integrated Master in Medicine

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIMED	1	Plano Oficial 2021	3	-	2	19	54

Teaching language

Suitable for English-speaking students

Objectives

In this curricular unit, machine learning methods will be addressed for the knowledge discovery in data (data mining) in the health area.

Learning outcomes and competences

This unit aims to empower students with the necessary knowledge and skills to: identify problems where data mining techniques could be applied; to apply data modeling methods, and specifically to apply machine learning techniques; to be able to interpret results in the context of practical medicine and clinical research in health services.

Working method

Presencial

Program

Machine learning and data mining (introduction, practical scenarios, the knowledge discovery process, specific characteristics in the area of health); data modeling, medical data preprocessing, data quality in health; supervised learning (decision trees, Bayesian classification, neural networks); unsupervised learning (cluster analysis, outlier analysis, association and mining frequent pattern analysis); evaluation of machine learning techniques (classification models, clustering).

Software: RapidMiner, Orange, R

Mandatory literature

Jiawei Han, Micheline Kamber and Jian Pei; Data Mining: Concepts and Techniques, Morgan Kaufmann, 2011
Ana Carolina Lorena, Katti Faceli, Márcia Oliveira, André Ponce de Leon Carvalho, João Gama; Extração de Conhecimento de Dados - Data Mining, Edições Silabo, 2012
Cruz-Correia RJ et al; Data Quality and Integration Issues in Electronic Health Records In: Hristidis V (ed.). Information Discovery on Electronic Health Records, Chapman and Hall, 2009
Vijay Kotu, Bala Deshpande; Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner, Morgan Kaufmann, 2014

Teaching methods and learning activities

Theoretical lectures (4h) and practical lessons (8h), with topic discussion, individual and group exercises, and hands-on training on medical scenarios, with proper software. Tutorial guidance (7 hours) for monitoring and discussion of group assignments.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	80,00
Participação presencial	20,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	2,00
Estudo autónomo	15,00
Frequência das aulas	12,00
Trabalho de investigação	25,00
Total:	54,00

Eligibility for exams

Presence in 75% of the classes

Calculation formula of final grade

Evaluation will be based on individual and group assignments (80%), with oral presentations (20%).