Teaching plan for the course unit

 

 

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General information

 

Course unit name: Data Science and Health

Course unit code: 574186

Academic year: 2021-2022

Coordinator: Laura Igual Muņoz

Department: Department of Mathematics and Computer Science

Credits: 3

Single program: S

 

 

Other contents

 

Courses:

Deep Learning with Electronic Health Record (EHR) Systems:

https://goku.me/blog/deep-learning-with-ehr-systems

The Data Science of Health Informatics:

https://www.coursera.org/learn/the-data-science-of-health-informatics

Introduction to Clinical Data Science:

https://www.coursera.org/learn/introduction-clinical-data-science

Program Health Information Literacy for Data Analytics:

https://www.coursera.org/specializations/healthcare-information-literacy-data-analytics#courses

Data Analytics and Visualization in Health Care:

https://www.edx.org/course/data-analytics-and-visualization-in-health-care

 

 

Estimated learning time

Total number of hours 75

 

Face-to-face and/or online activities

30

 

-  Lecture with practical component

Face-to-face

 

30

 

(face-to-face and on-line)

Supervised project

15

Independent learning

30

 

 

Competences to be gained during study

 

Ability to apply the knowledge acquired to develop and defend arguments, and to solve problems related to data science for health, often in a research context.

Ability to gather and interpret relevant data to make judgments that include reflection on important issues related to data science for health.

Ability to work independently and make decisions.

Ability to find relevant information by accessing bibliographic databases.

Ability to find relevant information by accessing bibliographic databases.

Ability to synthesize and present a research work.

 

 

 

 

Learning objectives

 

Referring to knowledge

This course will give a general overview of the different topics related with Data Science for Health: Electronic Health Records, eHealth, clinical modeling and predictions, medical imaging, and clinical applications. In addition, practical assignments will focus on specific problems with real health data, which will involve working on the data collection and preparation, data analysis methods and tools, as well as how to generate and communicate meaningful insights extracted from the analysis.

 

 

Teaching blocks

 

1. Introduction to DSxHealth and Electronic Health Records

2. Digital transformation in healthcare: eHealth and mHealth

3. Clinical Modeling and Predictions

4. Medical Imaging

5. DSxHealth applications

 

 

Teaching methods and general organization

 

If the health situation allows it, during the 2020-2021 academic year, the subject will follow a face-to-face model. Weekly teaching will be two hours where we will cover theoretical and practical contents.

The activities are proposed and followed through the Virtual Campus.

 

 

Official assessment of learning outcomes

 

Problem Sets: 100%.

 

Examination-based assessment

Problem Sets: 100%.

 

 

Reading and study resources

Consulteu la disponibilitat a CERCABIB

Book

“Data Science for Healthcare: Methodologies and Applications”, Consoli, Sergio, Reforgiato Recupero, Diego, Petković, Milan. Springer 2019.

 https://www.springer.com/gp/book/9783030052485#