Objective: Medication review (MR) is the systematic assessment of a patient's medicines - a critical step in preventing of drug adverse events. MR aims to identify drug-related problems (DRP) that trigger documented pharmacist interventions (PI). The information-rich data documenting PI, produced daily, provides a unique opportunity to develop a deep learning algorithm to automatically categorize PI. Materials and Methods: The study was conducted at the University Hospital of Strasbourg. Text data documenting PI were collected over the year 2017. Data from the first six-months of 2017 were reviewed by pharmacists who manually assigned to each PI the main class of the 29 possible classes of the French Society of Clinical Pharmacy classification. A deep neural network classifier was then trained to learn to automatically predict the main PI class from processed text data. Accuracy, specificity and sensitivity metrics were used to evaluate performance. Results: 27,699 PI (first six-months of 2017) were extracted, processed and used to train and evaluate a classifier. Class prediction accuracy calculated on the validation dataset was 78.0%. Class specific sensitivities and specificities ranged from 0.31 to 0.96 and from 0.94 to 1.00, respectively. To demonstrate the classification ability of the algorithm, we predicted the PI class for documents collected during the second semester of 2017. Of the 4,460 predictions checked, only 67 required corrections. The latter data was concatenated with the original dataset to create an extended dataset to re-train the neural network. The updated global accuracy reached 81.0% showing that the prediction process can still improve with the increase in the amount of data. Conclusion: PI classification is beneficial for assessing and improving pharmaceutical care practice. Here we report a high performance automatic PI classification based on deep learning. This application could find an essential place to highlight the clinical relevance of the review of drug prescriptions performed daily by hospital pharmacists.

The authors have declared no competing interest.

This study did not receive any funding

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