Methods in Medical Informatics

Study and apply data methods. We study and explore new AI and other methods of analyzing data, which may also include free text, within the healthcare sector. We generate predictions with these data and identify causes of diseases or other outcomes. Within this research line, we also develop computer programs that can assist healthcare providers in making decisions. Our main clinical partners are from the departments of general practice, geriatrics, perinatology, cardiology, and intensive care.

Scientific projects

PACMAN Assistant #2

This project is a consortium with the medical device company Pacmed and it aims at supporting the decisions of intensive care clinicians dealing with treatments of variable length. Such treatments are difficult to administer because it is hard to understand when is the optimal time to stop treatment; for instance with mechanical ventilation, it can be hard to judge when the patient can be safely extubated. The work package entrusted to KIK concerns the study of causal inference techniques to assess the effect of such dynamic treatments. These solutions will then be tested on real-world data leveraging the ongoing collaboration between Pacmed and the Santeon hospitals, enabling a quick iteration and a speedy trajectory towards the improvement of current hospital processes.

Timespan: 2022-2026

Research line: Methods in Medical Informatics

Out-Of-Distribution detection for Medical AI 

Machine learning (ML) models have made remarkable advancements in analyzing medical data, yielding impressive results. However, a significant limitation lies in their performance, which is primarily optimized for data from the training distribution and can degrade when the model is used on a different distribution. As we aim to deploy these ML models in real-world healthcare scenarios, ensuring their reliability becomes of utmost importance. Consequently, it is crucial to devise a method that can effectively detect samples that lie outside the training distribution before making potentially erroneous predictions on them. By doing so, we can proactively avoid erroneous predictions on out-of-distribution (OOD) data. 

In this project, we address this specific challenge within the context of medical tabular data. Our primary objective is to investigate and develop a robust method for identifying OOD data points in real time. By developing effective solutions to detect OOD data in medical tabular datasets, we can elevate the reliability and applicability of ML models in real-world healthcare settings. This could potentially lead to improved patient outcomes and enhanced decision-making processes, as healthcare professionals can trust the model's predictions and be alerted when encountering unfamiliar or OOD cases. Ultimately, our research aims to bridge the gap between ML advancements and practical healthcare implementation, fostering a more reliable and secure healthcare AI ecosystem. 

Research line: Methods in Medical Informatics


Model explainability and transparency are requirements in healthcare applications. This is especially important when proposing models for healthcare that consume or generate natural language inputs such as free-text clinical notes. The major trend in explainability research in artificial intelligence resorts to the post-hoc explainability of black-box models, which are methods that try to approximate how a black-box model produces their predictions. However, we would like to build models that are inherently explainable and transparent. In this project, we propose not only post-hoc explainability methods for NLP, but also NLP methods that are explainable by design. 

Research line: Methods in Medical Informatics


Within the SNOWDROP project, we are developing a clinical decision support system for decreasing fall risk-increasing medication for general practitioners. The system predicts the individual risk of falls for the older patient based on prediction models. If a patient has a high risk of falling according to the system, the GP can call the patient for a consultation in which a medication assessment will be carried out and the fall risk will be further identified. The patient is offered information about the fall risk and medication-related treatment options via a patient portal. The patient can use this information to prepare for the consultation. During the consultation, the clinical decision support system provides support to the GP and the patient for joint decision-making about adjusting fall risk-increasing medication. The patient can view the decisions and advice discussed during the consultation in the patient portal

Timespan: 2019-2024

Do you want to know more about this project? Please see the website.


AI4Cardiology is a collaborative data science effort performed by a multidisciplinary team to explore the potential of machine learning in the field of cardiology. By delving deep into complex, real-world cardiac datasets, we aim to uncover valuable insights and positively impact clinical decision-making. 

Time span: 2021-2024

Research line: Methods in Medical Informatics

Staff involved

Prof. dr. Ameen Abu-Hanna, dr. Giovanni Cina, dr. Iacer Coimbra Alves Cavalcanti Calixto, dr. Iacopo Vagliano, dr. Anita Ravelli, Tinka Bakker MSc, Noman Dormosh MSc, Tseko Yordanov MSc, Nishant Mishra MSc, Sagar Simha MSc, Mohammad Azizmalayeri MSc