Degree | Type | Year | Semester |
---|---|---|---|
4316624 Internet of Things for e-Health | OT | 0 | 2 |
Knowledge of programming languages (prefearably C++, Python or Matlab) and good mathematical background is highly recommended
An important area of application within IoT for digital health are the systems of support to the clinical decision making (diagnosis and intervention). To facilitate its use in the largest number of clinical centers, these systems are beginning to develop as a service in the cloud (Diagnosis as a service). This module provides the student with the necessary techniques through use cases.
A cloud diagnostic service requires a client application that allows the interactive visualization of large volumes of augmented multimodal data with clinically relevant information extracted using AI-specific techniques and processing Image in the cloud. AI techniques and image processing should be able to customize the models for each patient efficiently in order to have all the information in the same intelligent intervention room that allows the doctor to interact with the application without Alter the usual protocol. In addition, the clinical validation of the system requires the use of statistical techniques that allow to contemplate the variability between clinical experts and possible replicas in the experimental design
• Interactive visualization devices and interfaces
• Animation and graphics techniques
• Virtual and augmented reality
• Definition of GroundTruth and variability between observers
• Multiple multifaceted replicas, random effects regression models
• CrowdSouring Techniques for data collection
• Modeling of the anatomy and physiology of the patient
• Advanced Medical Scanner processing techniques: 3d reconstruction methods, multimodal data integration
We will follow a problem based methodology, so learning will we based on the solution of usage cases related to real applications in the field of Iot. Students will be provided with the basic materials and tools required to solve each usage case. Teachers will also give some explanations at some lectures in order that students can understand usage cases and the provided tools. The remainining lectures will focus on helping students to solve the proposed usage cases and extending explanations related to techniques.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Lecture Sessions | 50 | 2 | 7, 3, 4 |
Type: Supervised | |||
Tutorized classroom activities (resolution of usage cases) | 92 | 3.68 | 1, 7, 8, 3, 4, 5, 6, 2 |
Resolution of Usage Cases. Following a PBL methodology, students will solve some usage cases in groups and with the help of the teacher (who will take the role of expert) during the course.
Individual Tests. Students' capability to apply the techniques will be also evaluated individually.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Individual Tests | up to 50% | 2 | 0.08 | 1, 3, 4 |
Resolution of Usage Cases (Project) | up to 50% | 6 | 0.24 | 1, 7, 8, 3, 4, 5, 6, 2 |
Paul Suetens, Fundamentals of medical imaging
Bui, Alex A.T., Taira, Ricky K. (Eds.), Medical Imagine Informatics
Bruce Eckel, Thinking in PYTHON (on line at http://www.bruceeckel.com).
Rao, C.R. (1973), Linear Statistical Inference and Its Applications - second ed, New York: John Wiley & Sons, Inc.
Hosmer, D.W, Jr and Lemeshow, S. (1989), Applied Logistic Regression - John Wiley & Sons, Inc.
A. Watt, , 3rd edition, , 2000. 3D Computer Graphics Addison-Wesley
P. Shirley, Fundamentals of Computer Graphics, 3rd ed., AK Peters, 2002
Python, OpenGL, Visual C++