Teaching code

4S004553

Teacher

Pietro Sala

Coordinator

Pietro Sala

Credits

6

Also offered in courses:

• Biomedical decision support systems of the course Master's degree in Artificial intelligence

Language

English

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

Semester 2 dal Mar 3, 2025 al Jun 13, 2025.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

Knowledge and understanding The course aims to introduce principles that form the foundations of the Decision Support System together with case-studies of their real-world applications, with particular focus on their use in Biomedical domain. In particular, the purpose of the course consists of providing advanced knowledge on the techniques and principles involved in managing and manipulating very large databases (with specific examples borrowed from the biomedical domain). Moreover, the course will provide the theretical and practical foundations of the main data mining techniques used in clinical domains. Applying knowledge and understanding During the course students will aquire the following competences: - they will be able to choose and use the appropriate components in order to provide solution for supporting decision to the medical staff; - they will be able torealize complex operations of Extraction, Transformation, and Loading (ETL) on several clinical data types coming from different sources (Relational Databases, API, Websites, and so on) and encoded in both structure (relational tables) and semi-structured (XML) fashion; -they will be able to model and realize OLAP (On-Line Analytical Processing) solutions for supportuing decisions in a Biomedical context; -they will be able to use or adapt advanced data-mining techniques (Approximate Functional Dependencies, Association Rules, Entropy-based Classifiers, and so on) for extracting knowledge from large amounts of data. Making judgements Students will develop the required skills in order to be autonomous in the following tasks: - choose and apply data mining techniques for extracting medical knowledge from large amount of data; - choose the appropriate graphical/interactive representations for represent specific clinical information. Communication skills The student will learn how to address the correct priorities to the informations that must be reported to the end-user according to his needs and the language of his domain. Learning skills The students will be introduced to the main algorithms and techniques used in the clinical data mining field, together with the description of the factors that affect their efficiency and effectiveness. This knowledge will be the basis for comprehend more specific techniques adopted nowadays for data mining for clinical domain. Moreover, the student will be able to choose autonomously the data mining techinque for answering a given quesry of the end-user. Finally, he will be able to evaluate the performance and the accuracy of the proposed solution.

Prerequisites and basic notions

Good programming skills,
good database skills especially querying and
manipulating data.

Program

Functional Dependencies (FD):
concepts and applications of FDs, forcing and verifying FDs in PostgreSQL
Approximate Functional Dependencies (AFD):
introducing approximation in FDs as confidence measure. Clinical knowledge extraction using AFD: examples. AFD analysis in the biomedical context.
Algorithms for extracting AFDs:
minimal AFDs: definition, semantics and analysis. Theoretical Lower Bounds on the number of minimal AFD: the curse of cardinality. Basic algorthm for extracting minimal AFD. Compact representations of
sets of extracte AFDs. Randomized algorithms for extracting minimal AFDs:
theory and implementation.
Approximation in presence of measures:
Delta Functional Dependencies (DFDs) : definition, application, and verification. Analysis of DFDs extracted from the biomedical domain. Approximated DFDs
(ADFD):
definition, applications and analysis in the biomedical domain (examples). Algorithm for verifying single ADFD restricted to the case of 2 measures (2ADFD):
complexity, implementation. Extraction of minimal 2ADFD from clinical data.
Association Rules (ARs):
definition, examples in the biomedical domain. Extraction of di AR: support and confidence. Theoretical analysis: the curse of cardinality. Frequent Itemsets (FIs): definition, role in the extraction
of ARs, and algorithm for vandidates generation. ARs extraction from sets of FIs. Sets of FIs: minimal sets, closed sets.
Strategies for exploring FIs lattices. Alternatives to standard extraction algorithm using specific data structures (hash trees, FP-trees). Evaluation of association patterns: drawbacks of the support/confidence framework. Examples of paradoxes. alternative measures for association pattern analysis:
definition and examples.
Extraction Transformation and Loading (ETL):
definition, functions, role inside a data warehouse, data flows. Basic entities of ETL procedures and how they work: Job, Transformations, Job, Step, Transformation Step. Conceptual modelling of ETL procedures in Business Process Model and Notation (BPMN). Modelling examples: case studies. Embedding external procedures into ETL procedures: comunication, staging and managing of errors. API (Application Programming Interface) usage inside ETL procedures. Short description of XPATH constructs and how to use them. Screen scraping of websites in ETL procedures by using XPATH. Using Business Intellingence tools to realize ETL procedures.
Entropy-based classifiers:
introduction to the concept of Entropy. Decision Trees in the biomedical context. The Iterative Dichotomiser 3 (ID3) classifier: algorithm, examples and implementation. Measures discretization. Using ID3 for discretizing measures:
problems, modification and implementation. Temporal analysis application to adverse drug reactions.
Reporting and OLAP (Online Analytical Processing):
Interactive reporting systems: querying the clinical databases, parametrization of the reports. Dynamic retrieval of report information by using ETAL transformation. Modelling analysis using OLAP cubes and theri implementation: case studies. Using Business Intellingence tools to realize dynamic/interactive reports and OLAP cubes
SUGGESTED TEXTS:
DJ Hand, H Mannila, P Smyth
Principles of data mining
MIT Press Cambridge, MA, USA ©2001
ISBN:0-262-08290-X 9780262082907
Roland Bouman, Jos van Dongen
Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL
Wiley Publishing, Inc.
ISBN: 978-0-470-48432-6
648 pages
September 2009
Fulton, Hal and Olsen, Russ
The ruby way: solutions and techniques in ruby programming, third edition
Addison-Wesley Professional ©2014
ISBN:0-321-71463-6
COURSE MATERIAL:
class slides;
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course.

Bibliography

Didactic methods

During the lecture, which will not be recorded,
a more in-depth explanation of the aforementioned topics
will be given by means of examples and
exercises that the lecturer will explain and comment.
Moreover, after the explanation, the lecturer
is available for helping the students with the exercises.
The lecture will be given in a classroom, and students are encouraged, if possible, to attend in person
and bring a laptop it is also suggested to share a laptop betwen multiple students so the exercises may be discussed in groups.

Learning assessment procedures

The examination method is aimed at verifying the student's autonomy and ability to apply the concepts learned in class to develop decision support systems in their main variations.
The exam consists of an oral interview about the completion of 4/5 exercises assigned during the lessons, one for each of the main topics covered in the course.
The projects can be carried out individually or in groups. The oral interview focuses exclusively on the implementation of the two projects. A necessary but not sufficient condition for passing the exam consists of completing both projects in their entirety. The final grade will be the sum of the evaluations of the individual exercises.
The exam format remains the same for both attending and non-attending students.

Evaluation criteria

The projects will be evaluated according to the following criteria:
requirements fulfillment;
soundness, completeness, and clarity of the code and the documentation;
consistent application of the methodologies explained during the lectures.

Criteria for the composition of the final grade

7.5 points per exercise if 4 exercises are assigned and
6 points per exercise if 5 exercises are assigned
IMPORTANT NOTE: All exercises must be presented to pass the exam.

Exam language

Inglese