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Advancing mental health science

Ieso’s research team combines expertise in clinical science, artificial intelligence, and software engineering. We work to understand the causes of mental illness and to find the most effective way to help our patients get better as quickly as possible.

To do that, we mine the most comprehensive mental health therapy dataset in the world, covering more than 250,000 therapy hours. We analyse that data using advanced statistical techniques, including deep learning, to find out what moves patients to recovery, what works for whom, and what keeps patients engaged in therapy.

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CBT formulation on an ipad

Creating tools for therapists

Understanding the active ingredients in CBT is enabling us to build tools to empower clinicians to be the best they can be.

Ieso’s clinical decision support tool uses AI to help therapists assess patients more effectively, by providing therapists with insights into each patient’s diagnoses, severity of symptoms and likelihood of engaging with therapy. This provides clinicians with the greatest chance of making good clinical decisions during the assessment session, creating a solid foundation for effective treatment.

How science is changing our understanding of psychological therapy

Our publications

Our data is enabling us to understand more about what aspects of therapy work for different people, based on variables such as behavioural, cognitive, and biochemical biomarkers. We will be using our data to bring personalised care to mental health treatment.

We are also conducting research to learn how we can prevent relapse, and potentially stop mental illness from occurring.

Using tech to associate therapist variables with outcomes

Bateup, S., Palmer, C., & Catarino, A. (2020). Using technology to understand how therapist variables are associated with clinical outcomes in IAPT. The Cognitive Behaviour Therapist, 13, E26. doi:10.1017/S1754470X20000252

The relationship between patient language and outcomes

Ewbank, M. P., Cummins, R., Tablan, V., Catarino, A., Buchholz, S., & Blackwell, A. D. (2020). Understanding the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy: A deep learning approach to automatic coding of session transcripts. Psychotherapy Research, 1-13.

Depressive states and state transitions in cognitive behavioural therapy

Catarino, A., Fawcett, J. M., Ewbank, M. P., Bateup, S., Cummins, R., Tablan, V., & Blackwell, A. D. (2020). Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling. Psychological Medicine, 1-10.

Associating psychotherapy content and clinical outcomes

Ewbank, M. P., Cummins, R., Tablan, V., Bateup, S., Catarino, A., Martin, A. J., & Blackwell, A. D. (2020). Quantifying the association between psychotherapy content and clinical outcomes using deep learning. JAMA psychiatry, 77(1), 35-43.

TIM: Psychotherapy insights tool

Cummins, R., Ewbank, M. P., Martin, A., Tablan, V., Catarino, A., & Blackwell, A. D. (2019, May). TIM: A Tool for Gaining Insights into Psychotherapy. In The World Wide Web Conference (pp. 3503-3506).

Perceptions of chatbots in therapy

Bell, S., Wood, C., & Sarkar, A. (2019, May). Perceptions of chatbots in therapy. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-6).

Demographic and clinical predictors of outcome

Catarino, A., Bateup, S., Tablan, V., Innes, K., Freer, S., Richards, A., ... & Blackwell, A. D. (2018). Demographic and clinical predictors of response to internet-enabled cognitive–behavioural therapy for depression and anxiety. BJPsych open, 4(5), 411-418.

RCT: Psychotherapy for depression

Kessler, D., Lewis, G., Kaur, S., Wiles, N., King, M., Weich, S., ... & Peters, T. J. (2009). Therapist-delivered internet psychotherapy for depression in primary care: a randomised controlled trial. The Lancet, 374(9690), 628-634.

TEDxNatick Talk: Artificial Intelligence Meets Mental Health Therapy

At Ieso we are committed to transforming mental healthcare. By analysing large volumes of de-identified data we can gain new insights into how therapy works, and for whom, at a scale that has not been possible before. We can share this knowledge with clinicians, and translate these insights into clinical practice – by analysing data from thousands of patients, we are learning to deliver the best care to each individual patient.

Ana Catarino

Principal Scientist

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