PMOHR comprises the three main steps of the probabilistic modelling pipeline: model design, scalable inference algorithms, and model testing. The project has achieved a number of results towards its objectives. These results are described below.
In terms of modelling, PMOHR has achieved two main results. The first one is a new class of models, called exponential family embeddings, which can capture co-occurrence patterns of objects in a dataset. When applied on EHRs, exponential family embeddings can capture how medical diagnoses relate to each other and can also find meaningful features of the diagnoses, which are useful for downstream analyses. Exponential family embeddings can be applied on high-dimensional discrete data, such as medical text or medical diagnoses, as well as on real-valued data, such as neural activity.
See the attached figure for an example of the results of exponential family embeddings when applied to the publicly available MIMIC-III dataset. The figure shows a projection of the features found by an exponential family embedding model applied on the diagnoses in the publicly available MIMIC-III dataset. On the first figure, each dot corresponds to a disease, and colours indicate the categorisation of the diseases. Exponential family embeddings identify clusters of similar diseases based on their co-occurrence patterns, even though the ontology information was not provided to the algorithm. On the second figure, we can see a zoom on a particular location of the first figure. Exponential family embeddings find a cluster of related respiratory conditions, even when they belong to several groups. The embedding representation found by the model can be used both for making predictions but also as features for further analyses.
The second modelling result is a new model for the analysis of longitudinal datasets, based on Bayesian non-parametric techniques. This model allows us to model the dynamics of time series (such as the latent health of a patient over time). Importantly, the complexity of the model adapts automatically to the available data, following the structured posited in the model design phase.
In terms of inference, PMOHR has extended state-of-the-art inference algorithms to obtain faster and scalable algorithm that are suitable for virtually any probabilistic model. This is crucial for algorithms to scale for complex models and large EHR datasets. The developed inference methods are black-box, in the sense that they are general-purpose algorithms that can be applied off-the-shelf.
In terms of model testing, PMOHR has developed a novel method for testing probabilistic causal models. The analysis of EHRs requires to posit causal theories, many of which are fundamentally not testable. However, there are some other modelling assumptions for which the model testing phase can still provide insights about possible model mismatch. PMOHR extends the tools of Bayesian model testing to study the validity of probabilistic causal models.
