https://studiegids.vu.nl/en/courses/2024-2025/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 Dublin descriptors:Knowledge and understanding: learning new relevant learning and reasoning techniques.Applying Knowledge and Understanding: the aim is to apply algorithms in practice in a project. Making judgements: deciding which domain knowledge and learning and reasoning techniques are appropriate.Communication skills: how to report on your approach, choices and your resultsMachine 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: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. 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). Topics:inductive logic programming (application eg. discovering possible causes of diseases)probabilistic logic (application in diagnosis) Explainable learning systems: 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. 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