https://studiegids.vu.nl/en/courses/2025-2026/XM_0102The main aim of this course is to make the student familiar with the combination of machine learning and reasoning techniques applied in healthcare. We will focus on machine learning with prior knowledge (“informed machine learning”), machine learning with symbolic output, and explainable learning systems. We apply each approach to some of the important tasks in healthcare, namely diagnosis, prediction, treatment and prevention using patient data (electronic health record data or trial data). The course is focused on the following Bloom descriptors:Understand and apply a generic framework for machine learning (ML) and reasoning to solve real-world problems in healthcare.Demonstrate proficiency in designing and implementing machine learning models that incorporate prior domain-specific knowledge.Develop and evaluate machine learning models that produce symbolic outputs for healthcare applicationsAnalyze and implement techniques to improve the interpretability and generalisability of machine learning systemsApply machine learning and reasoning techniques to healthcare applications, such as disease prediction, diagnosis, treatment planning and monitoringThe lectures are organized into four modules corresponding to the stages of ML and reasoning solution development in healthcare:Module 1: Initiating a Healthcare ML ProjectModule 2: Design of Learning and Reasoning SystemsModule 3: Evaluating the AI SystemModule 4: Adding to Existing KnowledgeModule 1: Initiating a Healthcare ML Project Module 2: Design of Learning and Reasoning Systems (Machine learning with prior knowledge) The input of informed machine learning consists of usual training data and additional prior knowledge. The prior knowledge is independently of the learning task and can be in the form of logic rules, simulation results, knowledge graphs, etc. Topics:Design patterns.Prediction with machine learning techniques (i.e., CART, LR, RF) enriched with domain knowledge (ontologies). For example predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records.Reinforcement learning with initial policies based on domain knowledge and/or constraints. Reinforcement learning can be used to select sequences of interventions. This should however be done safely, and one should not ignore existing knowledge on suitable intervention strategies. For example, limiting the actions space per situation based on rules and regulations and starting from the current treatments regimes, and refining them based on data.Module 3: Evaluating the AI system Machine learning algorithms are usually black boxes, however physicians would like an explanation for the decisions of machine learning algorithms. Topics: SHAPE and LIME algorithms for providing an explanation. Those algorithms can be used for instance for explaining predictions of breast cancer survival or using background knowledge for deductively reconstructing an explanation for the results of machine learning algorithms. Module 4: Adding to existing knowledge (Machine learning with symbolic output) Machine learning techniques can be applied to symbolic structures (eg. Electronic Health Record) that results in symbolic output (e.g. discovery of possible causes of rare diseases, or simple guidelines). Project: exploiting a combination of reasoning and learning in health care along the lines of the above themes (machine learning with prior knowledge, machine learning with symbolic output, explainable learning. systems). For instance using several machine learning techniques and knowledge sources to predict the occurrence of a particular disease based on medical records.Lecture series, plus workgroup sessions for the assignment. In the first part of the period the emphasis is on the theory (lectures), the second part of the period is a practical assignment in the form of a project. The course ends with an exam. Furthermore we aim to have one or two guest lectures by medical experts (physicians).Written exam (E) (50%) and practical assignment (A) (50%). For both parts the grade needs to be sufficient to obtain a final grade. No resist is possible for the practical assignment.Collection of research papers.Master students Artificial Intelligence (track AI for Health) Master students Medical Informatics (UvA) (track AI for Health)Registration for medical informatics (UvA) students: Please check this document and then register for secondary courses via https://vu.nl/en/student/information-about-registration-and-enrolment