Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling.

Background: It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).

Methods: Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session.

Quantifying the Association Between Psychotherapy Content and Clinical Outcomes Using Deep Learning.

Importance: Compared with the treatment of physical conditions, the quality of care of mental health disorders remains poor and the rate of improvement in treatment is slow, a primary reason being the lack of objective and systematic methods for measuring the delivery of psychotherapy.

Objective: To use a deep learning model applied to a large-scale clinical data set of cognitive behavioral therapy (CBT) session transcripts to generate a quantifiable measure of treatment delivered and to determine the association between the quantity of each aspect of therapy delivered and clinical outcomes.

Design, Setting, And Participants: All data were obtained from patients receiving internet-enabled CBT for the treatment of a mental health disorder between June 2012 and March 2018 in England.

Demographic and clinical predictors of response to internet-enabled cognitive-behavioural therapy for depression and anxiety.

Background: Common mental health problems affect a quarter of the population. Online cognitive-behavioural therapy (CBT) is increasingly used, but the factors modulating response to this treatment modality remain unclear.

Aims: This study aims to explore the demographic and clinical predictors of response to one-to-one CBT delivered via the internet.

Getting more out of biomedical documents with GATE's full lifecycle open source text analytics.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine.

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud.