Artificial Intelligence in Health Care

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Credits
3
Types
Elective
Requirements
This subject has not requirements

Department
Heath care (HC) is one of the main application domains of artificial intelligence (AI) since its appearance in 1956. Most of the AI technologies find a natural application area in HC problems though the benefit of this application has been sometimes called into question. In the last times, however, we've witnessed a revival of the interest of AI applied to medicine.

By means of the analysis of remarkable published articles, during this course the student will be introduced in several AI solutions to HC needs and problems and will correlate the AI concepts and technologies studied in other subjects of the master in the resolution (or support to the resolution) of HC problems.

Classes are every other week.

Teachers

Person in charge

  • David Riaño ( )

Weekly hours

Theory
1.5
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
3.5

Competences

Generic Technical Competences

Generic

  • CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

Academic

  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP6 - Capability to assimilate and integrate the changing economic, social and technological environment to the objectives and procedures of informatic work in intelligent systems.

Transversal Competences

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Basic

  • CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
  • CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
  • CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Objectives

  1. Capacity to read, understand, and relate the information contained in scientific & technological documents
    Related competences: CT3, CT6, CEA8, CB7, CB9,
  2. Train the synthesis, preparation, exposition, and defense of scientific topics in public
    Related competences: CT3, CT6, CEA8, CG1, CB7, CB8, CB9,
  3. Ability to connect and complement own ideas with other's and also with AI technologies explained in other courses
    Related competences: CT3, CT6, CEP3, CEP6,

Contents

  1. Artificial intelligence in health care
    A review of the state of AI in health care will be analyzed
  2. Grand challenges in clinical decision support
    A review of the pending reseach and development CDS open problems will be analyzed
  3. Data mining in health care
    A review of important AI data mining technologies and their application to medicine will be analyzed
  4. Big data analytics in health care
    A description of BDA and its application to health care will be analyzed
  5. IBM Watson
    The use of IBM Watson and technology underneath when applied to health care will be analyzed
  6. Conclusions to AI in health care
    Summary of important issues of AI in health care

Activities

Introduction of the course

The professor will expose the relevant issues related to the subject: Content; Material; Calendar; Evaluation; Bibliography
Theory
1.5
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0

Preparation of 5 topics by the students

The five topics of the subject are prepared by the students in groups, every other week.
Theory
0
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
21
Objectives: 1
Contents:

Conclusions I by the professor

The conclusions of the course are exposed.
Theory
3
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
4.5
Objectives: 3
Contents:

Presentation of Conclusions II by the professor

The conclusions of the course are exposed.
Theory
3
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
4.5
Objectives: 3
Contents:

Teaching methodology

The entire course will work in groups. A topic of AI applied to health care will be presented to all the groups, an article and a list of questions will be released related to the topic presented. Each group will have two weeks to prepare an oral presentation of 15 minutes which will outline the important issues of the article and his response to the questions. After the presentation of all groups, there will be an open discussion among all groups about topic. This methodology will be repeated five times throughout the year, each with a different topic of IA applied to medicine differently.

Evaluation methodology

Presentations (60%): adjustment to time; clarity of presentation (oral); clarity of presentation (slides); addressed all the relevant issues in the questions; amenity

Participation in discussions of other's presentations (40%)

Bibliografy

Basic:

  • The coming of age of artificial intelligence in medicine. - Patel VL, Shortliffe EH, et al. , J AIIM 46, 5-17. , 2009. ISBN:
  • Grand Challenges in clinical decision support. - Sitting DF, Wrigth A, et al. , JBI 41, 387-392 , 2008. ISBN:
  • A survey on data mining approaches for healthcare, - Tomar D, Agarwal S., IJ Bio-sci & Bio-tech. 5(5), 241-266. , 2013. ISBN:
  • Big data analytics in healthcare: promise and potential. - Raghupathi W, Raghupathi V., Health info science and syst. 2:3 , 2014. ISBN:
  • IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. - Chen Y, Argentinis E, et al., Clinical therapeutics 38(4), 688-701 , 2016. ISBN:

Previous capacities

Basic concepts of AI.