Mining Causal Graphs from Patient Records

Electronic patient records are a rich resource of current practice in the health domain; practitioners meticulously record how they diagnosed and treated their patients. Often these records provide a more up­to­date overview of treatment patterns than medical  guidelines, as medical practice often differs for good reasons from the idealised guidelines.

In this project, we will create a structured graph representation of medical practitioners’ actions in response to observing particular symptoms, following [Goodwin & Harabagiu, 2014]. This graph will allow us to analyse the types of treatments practitioners choose, and to compare these treatments to those proposed in guidelines. Such a comparison yields either a signal of undesired deviation from the guideline, or a prompt  to update the guideline.

For our experiments we have access to anonymised routine healthcare data from the Julius General Practitioners’ Network Database, on close to 500,000 patients from more than 60 primary healthcare centers in the Utrecht region . For guidelines, we will use a selection of the publically available Dutch General  Practitioner guidelines, with which we are familiar from earlier work.

In the first part of the project, we will apply the graph induction method from to the Dutch patient records. This will yield the first scientific result of the project by reproducing this novel result from the literature on a new corpus in a different language. The results will serve as a baseline for the next step.

In the second part of the project we will use medical background knowledge (available as linked open data) such as drug descriptions, adverse event reports, rare diseases reports etc. for improving the graph from the first step. The graph constructed with the help of medical background knowledge will be compared with the baseline from the first step.

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