A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan

Abstract

This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.

Keywords: Depression; LSTM; Machine learning; Mental health; Treatment optimisation.

Fig. 1

OptiMA1 Data vs STAR*D Trial Remission Percentages over a 48-week period [29]. The OptiMA1 study results are shown in colour, while the STAR*D trial is shown in grey (Colour figure online)

Fig. 2

Frequency histogram illustrating the number of reviews participants completed

Fig. 3

Block diagram of created algorithm

Fig. 4

Frequency histogram illustrating patient age distribution (in years)

Fig. 5

Frequency histogram illustrating the number of patients prescribed a particular medication class

Fig. 6

Accuracy of the developed model at predicting a patient’s R8 Depression score, with and without reviews

Fig. 7

Accuracy of model predicting by medication class with and without reviews

Fig. 8

The developed model’s train/test loss per epoch

Fig. 9

Optimisation of the developed model’s β coefficient by accuracy with and without reviews

Fig. 10

Optimisation change measures in relation to number of patient reviews

Fig. 11

The developed model’s mean absolute error per number of reviews