Syllabus

Title	MSc in Biomedical Data Science
Website	Master oficial website-URV
Edition	2023/24 (2nd edition)
Seats	50
ECTS	120 Total (18 Master Thesis) / 2 Academic Years
Universities	URV (Coordinator), UPC, UB, UAB, UdL, UdG, UVic-UCC ~ 20 departments
Language	English
Modality	Virtual training (up to 30% syncronicity) / Optionally face-to-face (Summer school, Master Thesis)
Time slots for synchronicity	Monday to Friday 14:00-17:00/17:30
Candidates Profile	Graduates in Engineering, Computer Science, Experimental Sciences, Bioinformatics
Program driver	Bioinformatics Barcelona (BIB)
Programme sponsor	AMGEN
Academic Commitee	Sergio Gómez, Maria Vinaixa (URV); Petia Ivanova (UB); Alex Perera (UPC); Lola Rexachs (UAB); Rui Alves (UdL); Beatriz López (UdG); Mireia Olivella (UVic-UCC); Ana Ripoll (BIB)
Clinical Advisory Committee	Hospital Arnau de Vilanova, Hospital Josep Trueta, Hospital Clínic, Hospital San Joan de Deu, Hospital del Mar, Hospital de l a Vall d’Hebron, Hospital Sant Pau, Hospital Germans Trias i Pujol, Hospital Sant Joan, Hospital Joan XXIII, Hospital de Vic i l ’Hospital Parc Taulí
Corporate Advisory Committee	AMGEN, Bitac, Costaisa, Made of Genes, Sequentia, Aizon, Janssen and, Barcelona Tech City.

Educational guide

• Visit the educational guide containing all the detailed information of each subject

Master’s Vision

• The MSc in Biomedical Data Science program is aimed at providing the interdisciplinary training necessary to handle, manage and analyse and model large-scale biomedical data. Our vision is to build a intensely intersiciplinary and domain-dependent technological program leveraging informatics/data science and basic biomedical knowledge.

• Core comptences are assembled over the big data value chain ― a roadmap to big data value creation ― offering a unique technological programme with particular emphasis on the domain.

Programme structure

A single block of compulsory courses organized in seven different subjects

Subjects and courses

Biomedical Framework—10.5 ECTS

• BDC—Biomedical data challenges—3 ECTS

  • The big data value chain

  • Precision medicine

  • Biomedical challenges and data science, practical cases

• BE—Biomedicine for engineers—4.5 ECTS

  • Molecular biology and Cellular biology. Macromolecules. Cellular components and their function.

  • Physiology. The human body. Nervous and motor systems. Respiratory, digestive, inmune and endocrine systems. Homeostasis.

• SS—Summer School—3 ECTS

  • Advances in biomedical data analysis

  • Biomedical data stewardship and governance

  • Valorization in biomedical data analysis, practical cases

Health Data—16.5 ECTS

• EHR—Electronic health records—4.5 ECTS

  • Clinical databases

  • ETL(Extract/Transform and Load) processes

  • Free text mining

  • Cohorts design and generation

• MI—Medical imaging—4.5 ECTS

  • Basics on medical imaging

  • RX imaging

  • Ultrasound imaging

  • Magnetic resonance imaging (MRI)

  • Nuclear Medicine Imaging

  • Digital pathology imaging

  • Other imaging technologies in healthcare

• BSSP—Biomedical sensors and signal processing—3 ECTS

  • Biomedical sensors and actuators

  • Data acquisition in sensor based systems

  • Biomedical digital signal processing

  • Statistical signal processing and feature extraction

• ERP—Ethics, regulation and privacy—4.5 ECTS

  • Legislation of biomedical data

  • Good practices and regulation in the management of biomedical data

  • Values and ethical challenges in data science applied to biomedicine

  • Bioethics committees

  • Privacy technologies

  • Data anonymization

Health Data Technologies—10.5 ECTS

• SP—Scientific programming—4.5 ECTS

  • Environment for scientific computing: Unix shell and operating systems. Code management, collaborative coding, revision control and documentation. Dockerization

  • Introduction to statistical programming (R).

  • Introduction to functional programming for scientific computing (Python)

  • Scalable analysis. Processing of large volumes of data.

  • Application challenges. Biomedical data dashboards.

  • Multivariate data and application to EDA. Dimensionality reduction techniques.

• HPDC—High-performance and distributed computing for big data—6 ECTS

  • Concepts and architecture of high performance computing (HPC)

  • Algorithms and parallel programming

  • Cloud computing architecture

  • Cloud computing models and applications

  • Management and processing of large volumes of data

Health Data Science—21 ECTS

• BS—Biomedical statistics—6 ECTS

  • Study design in biomedicine

  • Statistical models

  • Parameter estimation and inference

  • Risk assessment in biomedical studies

  • Logistic regression

• MLPM—Machine learning for precision medicine—6 ECTS

  • Fundamentals of machine learning

  • Feature Extraction

  • Supervised learning

  • Unsupervised learning

• CN—Complex networks—4.5 ECTS

  • Structural properties of complex networks

  • Complex network models

  • Mesoscopic description of complex networks

  • Dynamics in complex networks

• HDVC—Health data visualization and communication—4.5 ECTS

  • Fundamentals of information visualization

  • Tools for creating graphic representations

  • Interpretability of graphic representations

Advanced Health Data Science—13.5 ECTS

• AHDA—Advanced health data analysis—6 ECTS

  • Advanced regression analysis

  • Survival analysis

  • Bayesian Analysis

  • Causal inference

• DL—Deep learning—4.5 ECTS

  • Basics of artificial neural networks

  • Back-propagation training

  • Deep learning

  • Auto-encoders

  • Convolutional networks

  • Recurring networks

  • Explainability and interpretability

• TMH—Text mining for healthcare—3 ECTS

  • Basic concepts of language processing. Text particularities in the medical domain.

  • Word level: Morphological disambiguation. Lexical semantics. Distributional semantics.

  • Sequence level: Recognition and classification of entities.

  • Text mining: Text classification. Relationship Extraction.

Health Data Analytics—21 ECTS

• CAD—Computer-aided diagnosis and decision making—4.5 ECTS

  • Diagnostic techniques and computer-assisted decision-making in medical applications

  • Approaches and techniques based on machine learning and artificial intelligence for decision making

  • Case studies at different levels: primary care, hospitals, geriatric care, and health resource management

• CE—Computational epidemiology—4.5 ECTS

  • Introduction to computational epidemiology

  • Basic epidemiological models

  • Advanced epidemiological models

  • Stochastic simulation of epidemiological models

• COTM—Clinical -omics and translational medicine—4.5 ECTS

  • Experimental design / QC/QA in -omics data

  • Computational Metabolomics

  • Computational Proteomics

  • Differential gene expression

  • Enrichment and functional analysis

• AMI—Advanced medical image analysis—4.5 ECTS

  • Image pre-processing

  • Image registration

  • Image segmentation

  • Applications for the analysis of medical images

• HDI—Health data integration—3 ECTS

  • Omic and phenotypic information integration systems

  • Analysis environments in genetic disorders, cancer and complex diseases

  • Disease trajectory analysis

Biomedical Data Projects—27 ECTS

• PRM—Project and research methodologies—4.5 ECTS

  • Fundamentals of scientific research

  • Criteria for quality research

  • Methodology and research stages

  • Dissemination of research

  • Regulatory, ethical and social management of research

  • Specific management of research in biomedicine and data science

  • Project management

  • Agile IT project management methodologies

  • Economic management and exploitation of projects

• EI—Entrepreneurship and innovation—4.5 ECTS

  • The process of generating ideas, co-creation and innovation

  • The value proposition and business model (Canvas)

  • Communication skills for entrepreneur

  • Marketing and sales of the entrepreneurial project

  • Financial, tax and legal aspects of the entrepreneurial project

• MT—Master’s Thesis—18 ECTS

  • Research areas: Mathematics and statistics; Biomedicine; Bioinformatics; Computer Science; Engineering

  • Main topics for the master’s thesis: Medical informatics; Digital medical records; Cloud computing; Parallel and distributed computing; Security, privacy, anonymization and encryption of medical data; Biosignal processing; Internet of things; Artificial intelligence applied to health; Machine learning; Deep learning; Case-based reasoning; Intelligent diagnosis support systems; Natural language processing; Medical image;Computer vision; Analysis of complex systems; Computational epidemiology; Biomedical data science in public health; Clinical trials; Biostatistics; Computational statistics; Cluster analysis and classification; Bayesian Statistics; Probability models for discrete data; Sciences -omics; Metabolomics and spatial metabolomics; Synthetic biology; Systems Biology;Bioinformatics; Structural biology

Subjects and courses timing

Academic calendar

• The programme spans 4 semesters - 15 school weeks each (2 academic years)

• Official calendar for academic course 2023/24

Timetables

• Timetable of synchronous activities 2023/24, in Central European Time (CET)

2023/24 – 1st course

	Q1					Q2
	MON	TUE	WED	THU	FRI	MON	TUE	WED	THU	FRI
14:00-14:30	MI	BDC	MI	BE	SP	AHDA	HPDC	HPDC	AHDA	MLPM
14:30-15:00	MI	BDC	MI	BE	SP	AHDA	HPDC	HPDC	AHDA	MLPM
15:00-15:30	MI	BDC	BS	BE	BS	AHDA	HPDC	HPDC	AHDA	MLPM
15:30-16:00	BS	EHR	BS	BSSP	BS	PRM	ERP	MLPM	PRM	ERP
16:00-16:30	BS	EHR	SP	BSSP	EHR	PRM	ERP	MLPM	PRM	ERP
16:30-17:00	BE	EHR	SP	BSSP	EHR	PRM	ERP	MLPM
17:00-17:30	BE		SP

• Summer School (Monday to Friday, 6 hours/day) / 3–12 June 2023

2023/24 – 2nd course

	Q3					Q4
	MON	TUE	WED	THU	FRI	MON	TUE	WED	THU	FRI
14:00-14:30	CE	TMH	CAD*	DL	CN	AMI	AMI
14:30-15:00	CE	TMH	CAD*	DL	CN	AMI	AMI
15:00-15:30	COTM	TMH	HDVC	COTM	CN	AMI	EI
15:30-16:00	COTM	CAD	HDVC	COTM	HDVC	HDI	EI
16:00-16:30	COTM	CAD	CE	DL	HDVC	HDI	EI
16:30-17:00	CN	CAD	CE	DL	HDVC	HDI	EI
17:00-17:30	CN	CAD	CE	DL			EI

• (*every two weeks)

Framework

Why should you become a Biomedical Sata Scientist?

• Big data explosion in the clinical setting (i.e., EHR; medical imaging; systems-medicine; public health data; mobile health data, etc) offering an unprecedented potential to advance towards precision medicine.

• Increasing workforce demand and short supply of those specialist able to manage, process and get value and knowledge out of these large data volumes

• Biomedical Data Science is an emerging, fast-moving and multidisciplinary discipline encompassing the use of critical thinking and analytics to handle, manage and derive knowledge from large sources of biomedical data.

• A Biomedical Data Scientist is a specialist with an interdisciplinary mindset that:

  • Works collaboratively with teams of faculty and health care providers to draw insight and intelligence from large datasets.

  • Develops and creates models, algorithms and/or tools to tackle data-driven biomedical problems.

  • Uses data mining, statistics, statistical modeling and machine learning to understand relationships and patterns in biomedical data.

  • Translates analytics into information that can be used by clinical and operational leadership.

Biomedical data science curriculum

Seminal and inspirational work to built upon to establish this Biomedical Data Science programme: Trans-NIH Big Data to Knowledge (BD2K, USA) and Health Data Research, UK initiatives

• The Training of Next Generation Data Scientists in Biomedicine

• Building the biomedical data science workforce

• What is Biomedical Data Science and Do We Need an Annual Review of It?

• Core skills for biomedical data scientist (2018)-NIH Office of Strategic Initiatives (OSI) Reports

• BD2K Predoctoral Training in Biomedical Big Data Science (T32)

Quality

• Verification Memory

• AQU evaluation report for accreditation

• CU ressolution

Contact

• e-mail the coordinators for information on the programme

• e-mail the secretariat for information on pre-registration, admission and registration