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.
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.
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.
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.
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
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.
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
DJ Hand, H Mannila, P Smyth
Principles of data mining
MIT Press Cambridge, MA, USA ©2001
Roland Bouman, Jos van Dongen
Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL
Wiley Publishing, Inc.
Fulton, Hal and Olsen, Russ
The ruby way: solutions and techniques in ruby programming, third edition
Addison-Wesley Professional ©2014
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course.
|DJ Hand, H Mannila, P Smyth||Principles of data mining||MIT Press Cambridge||2001||9780262082907|
|Fulton, Hal and Olsen, Russ||The ruby way: solutions and techniques in ruby programming (Edizione 3)||Addison-Wesley Professional||2014||0-321-71463-6|
The exam modality aims to verify the autonomy and the skills of the student in applying the concepts provided during the course for realizing Decision Support Systems.
The exam consists of an interview on the implementation
of two projects assigned during classes, one for each macro-topic of the course:
1) Data Mining;
2) OLAP Analysis.
The two projects must be realized as a team or as an individual. Moreover, a necessary but not sufficient condition for passing the exam is that both the implementations of the projects must be complete. In particular, each project will be evaluated on a scale going from 1 to 15 included, the final grade is given by the sum of the two individual project grades.
There is no difference in the exam modality among students that attended the course and students that did not.