Data-driven Health
Datadriven hälsa

BMEN35F, 7.5 högskolepoäng

Gäller från och med: Autumn 2022
Beslutad av: Professor Thomas Johansson
Datum för fastställande: 2022-03-24

Allmänna uppgifter

Avdelning: Biomedical Engineering
Kurstyp: Gemensam kurs, avancerad nivå och forskarnivå
Kursen ges även på avancerad nivå med kurskod: BMEN35
Undervisningsspråk: English

Syfte

The course provides basic knowledge in the field of artificial intelligence and machine learning for applications in medicine and health. The course covers the chain from medical databases via algorithms to regulations and requirements for diagnostic software.

Mål

Kunskap och förståelse

För godkänd kurs skall doktoranden

• be able to and understand and have an overall picture of how machine learning and artificial intelligence can be designed for different medical issues.
• be able to apply the most common methods to real problems and real medical signals (Python)
• know and understand how special requirements and regulations for diagnostic systems and other medical devices affect the design and validation process.

Färdighet och förmåga

För godkänd kurs skall doktoranden

• be able to understand the characteristics of the most common machine learning methods.
• be able to understand what settings need to be made for different methods.
• be able to understand how properties of medical training and validation data affect the performance of the methods.
• be able to choose the appropriate method for a described situation.
• be able to overview the consequences of different method choices and evaluation strategies that can be chosen for different types of problems
• be able to use available toolboxes for machine learning and in this way solve practical problems in Python.

Värderingsförmåga och förhållningssätt

För godkänd kurs skall doktoranden

• have the ability to analyze and evaluate different machine learning algorithms, as well as interpret and describe their inherent principles.
• have insight into how the transparency and generalizability of different methods make them more or less suitable in different medical contexts.

Kursinnehåll

Areas covered are: - Introduction of artificial intelligence in healthcare applications - Overview of machine learning algorithms and methods - How to choose ML methods for different applications - How to select settings and optimize performance - How to evaluate performance - Regulatory, social, ethical and legal issues regarding artificial intelligence in medicine -State-of-the-art AI that is applied to important medical fields such as ECG, neurology, biomedical imaging, heart sound, oncology, diabetes, etc. Practical work: - Introduction to Python / Jupyter / Colab (basics, linear algebra, plotting) - Linear models - Measurement values and visualization - Trees and knn - Ensemble methods - Neural networks (shallow, MLP, introduction to Keras / Tensorflow) - Deep Neural Networks (CNN) - Deep Learning (LSTM / RNN)

Kurslitteratur

• Österberg, M. & Lindsköld, L.: AI for Better Health. 2020.
• Lindholm, A., Wahlström, N., Lindsten, F. & Schön, Thomas B.: Machine Learning - A First Course for Engineers and Scientists. Cambridge University Press, 2021.
• Bohr, A. & Memarzadeh, K.: Artificial Intelligence in Healthcare. Academic Press, 2020. ISBN 9780128184387.
• Xing, L., Giger, Maryellen L. & Min, J.: Artificial Intelligence in Medicine: Technical Basis and Clinical Applications. Academic Press, 2020. ISBN 9780128212592.

Kursens undervisningsformer

Undervisningsformer: Föreläsningar, seminarier, övningar

Kursens examination

Examinationsform: Skriftlig tentamen. Computer assignments
Betygsskala: Underkänd, godkänd
Examinator:

Antagningsuppgifter

Kurstillfällesinformation

Startdatum: 2022-07-01
Kursfart: Full time

Anmälningsinformation

Apply by email to Course Coordinator

Kontaktinformation och övrigt

Kursansvariga:

• Christian Antfolk <christian.antfolk@bme.lth.se>
• Martin Stridh <martin.stridh@bme.lth.se>