PMOHR · Probabilistic modelling of electronic health records

Probabilistic modelling of electronic health records

Machine learning is a field of computer science with the aim of discovering statistical patterns in large datasets and making predictions using these patterns. While there is a growing interest on methods that can provide accurate predictions on unseen data, comparatively less effort is invested on designing interpretable machine learning models. Interpretable models have the benefit to provide human-understandable patterns that can be interpreted by domain experts. This enables knowledge discovery in the scientific disciplines, and it also allows reasoning about causality and making counterfactual predictions. In probabilistic machine learning, we first encode our assumptions about the data structure in the form of a model that has latent variables, which represent the hidden patterns. We then learn the latent variables using an inference algorithm. The results of the inference procedure can be used to make predictions and explore the data collection. Crucially, we can impose an interpretable structure in the model design phase. Finally, in probabilistic machine learning we can also apply model testing approaches to verify the properties of the posited model and whether if fails capture certain properties of the data. PMOHR is an interdisciplinary project focused on the design of interpretable models through probabilistic machine learning, with the ultimate goal of modelling Electronic Health Records (EHRs). Applying machine learning tools to EHR data can help not only design clinical support systems, but it may also lead to uncover unknown patterns from the data and even form causal theories. However, medical data, and EHRs in particular, present several challenges that prevent us from applying probabilistic modelling tools, because the datasets are large and heterogeneous. The objectives of PMOHR are to develop both probabilistic models and inference algorithms that are suitable for modelling EHR data. This new set of tools can then be applied to make predictions and to analyse medical datasets. In particular, PMOHR uses both publicly available EHR data and EHR data from the New York Presbyterian Hospital.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз