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AI4Cardiology
AI4Cardiology is a collaborative data science effort 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.

KIK Staff: Tseko Yordanov, Anita Ravelli, Ameen Abu-Hanna
AI4Hf
The AI4Hf project focusses on co-design, development, evaluation and exploitation of integrative and thrustworthy artificial intelligence solutions for personalized heart failure risk-assessment.

KIK Staff: Machteld Boonstra
CaRe-NLP
Unstructured data is guesstimated to account for 80% of all patient data but currently severely underused because it is noisy, hard to interpret, and privacy-sensitive. In the CaRe-NLP project, we develop human-centric responsible Natural Language Processing and Machine Learning methods that will allow to safely tap this unstructured data’s potential.

KIK Staff: Nishant Mishra, Heloisa Oss Boll, Xinlan Yan, Iacer Coimbra Alves Cavalcanti Calixto, Ameen Abu-Hanna
DailyMeds
Medication Recommender Systems are novel systems that can assist physicians with the selection of appropriate medications and with flagging inappropriate medications by uncovering patterns and exploiting similarity among patients’ and medications’ data. We investigate its potential for the ICU setting.

KIK Staff: Chao Zhao, Iacopo Vagliano, Ameen Abu Hanna, Joanna Klopotowska
Datatools4heart
The datatools4heart project works on a comprehensive, federated, privacy-preserved cardiology data toolbox. This toolbox includes standardized data ingestion and harmonization tools, multilingual natural language processing and federated machine learning and data synthesis methods.

KIK Staff: Noman Dormosh, Iacer Coimbra Alves Cavalcanti Calixto, Ameen Abu-Hanna, Machteld Boonstra
ExplaiNLP
Model explainability and transparency are requirements in healthcare applications. We would like to build models that are inherently explainable and transparent. In this project, we propose not only post-hoc explainability methods for Natural Language Processing (NLP), but also NLP methods that are explainable by design.

KIK Staff: Nishant Mishra, Iacer Coimbra Alves Cavalcanti Calixto, Ameen Abu-Hanna
MediSpeech
MediSpeech will present an integrated, digital IT ecosystem for automated medical reporting with proven added value in the clinical practice. The innovative technologies include AI-powered speech recognition, data interoperability and harmonisation, a clinical decision support system and medical reporting providing past data-driven knowledge.

KIK Staff: Iacer Coimbra Alves Cavalcanti Calixto, Ameen Abu-Hanna
Out-Of-Distribution detection for Medical AI
For machine learning models, 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. In this project, our primary objective is to investigate and develop a robust method for identifying out-of-distribution data points in real time.

KIK Staff: Giovanni Cinà, Mohammad Azizmalayeri, Ameen Abu-Hanna
Pacmed Intensive Care Assistant
This project aims at supporting the decisions of intensive care clinicians dealing with treatments of variable length. The work package entrusted to KIK concerns the study of causal inference techniques to assess the effect of treatments. The solutions will then be tested on real-world data, enabling a quick iteration and a speedy trajectory towards the improvement of current hospital processes.

Prediction2Action
This project will analyze the potential risks associated with the use of prediction models for decision support, develop metrics that actually track what we want, and use these theoretical tools to review the prediction models currently used at Amsterdam UMC.

KIK Staff: Giovanni Cinà, Otto Nyberg, Ameen Abu-Hanna