Course info

Couse name: Development and implementation of machine learning models for dynamic risk prediction models in health care applications (2023)

PhD Program: Medicine, Biomedical Science and Technology

Description (preliminary):

Traditional risk prediction generates a risk estimate at a defined timepoint in a patient’s disease trajectory, for example the risk of death within 30 days following a surgical procedure. In contrast, dynamic risk prediction enables prediction of risk at any time point. This allows to continuously monitor a patient’s risk profile and forms the basis for intervention if the predicted risk increases. In this course, we will explore methodological and technical solutions, as well as corresponding challenges, for developing and implementing such solutions in health care. The course includes the following topics:

1) Data management: This part of the course considers the challenges of preparing heterogenous longitudinal health data for prediction. We will cover the various steps involved in this process, including data formatting, feature engineering, and splitting strategies for model validation. This will include discussion about how to handle irregularly sampled health data, data leakage, class imbalance, temporal robustness, normalization, and other potential biases.

2) Modelling: In this part of the course, participants will be led through the process of building such models. We will introduce both basic and more advanced dynamic machine learning prediction algorithms, such as gradient tree boosting, random forest, and LSTM and discuss issues related to performance metrics and hyperparameter optimization, for example Bayesian optimization.

3) Implementation: In the last part of the course, we will consider the challenges associated with the implementation of predictive tools in the clinic. This includes technical aspects about hosting, user interface, and access to live data, including an introduction to the FHIR standard. Regulatory and organizational issues will also be discussed. During the project the participants will get hands on experience covering realistic scenarios related to the subjects discussed. This will include data management of representative data sets, training models and hands on introduction to the FHIR set-up.

Organizers: 

Heidi Søgaard Christensen, Postdoc, Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, hschr@dcm.aau.dk

Charles Vesteghem, Assistant Professor, Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU, cvesteghem@dcm.aau.dk 

Martin Bøgsted, Professor, Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine and Center for Clinical Data Science, Department of Clinical Medicine, m_boegsted@dcm.aau.dk

Lecturers (preliminary): 

Heidi Søgaard Christensen, Postdoc, Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU

Anne Krogh Nøhr, Postdoc, Charles Vesteghem, Assistant Professor, Rasmus Froberg Brøndum, Associate Professor, Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU

Ida Burchardt Egendal, PhD Student, Simon Christian Dahl, Senior Software Developer, Center for Clinical Data Science, Department of Clinical Medicine, AAU

More TBA.

ECTS: 3

Dates: June 13 – June 16, 2023 (4 days)

Time: 8:30 – 16:00

Place: 13 June, room 12.02.066

Zip code:  9220

City:  Aalborg Ø

Number of seats: 30

Deadline: 2023-05-29

Important information concerning PhD courses:
Literature/Requirements:

The participants are expected to have basic knowledge on regression analysis as well as programming experience in a statistical software tool, such as R or Python. The workshop will be held using Python and limited support will be provided for other programming languages.

Tomašev, N. et al. Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records. Nature Protocols (2021) doi:10.1038/s41596-021-00513-5.

Vesteghem, C. et al. Dynamic risk prediction of 30-day mortality in patients with advanced lung cancer: Comparing five machine learning approaches. JCO CCI (2022) doi:10.1016/j.clon.2022.03.015

Important information concerning PhD courses:

We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.