Optimising Psychology Treatment Within IAPT

IAPT (Improving access to psychological therapies) was developed to improve the access of and delivery of treatment to anxious and depressed patients within the UK. Patients can get referred to or refer themselves to an IAPT service to receive treatment for their mental health conditions. However, IAPT is known to have very high dropout rates for patients, with some treatments being known to have dropout rates as high as 50%. These dropouts or patients who Leave Without Being Seen (LWBS) take appointment slots of other potential patients which could have accessed the treatment. Thus, our research is interested in predicting which patients are susceptible to dropping out or LWBS and ultimately, terminating their treatment prematurely. A prediction of which patients are likely to leave treatment prematurely could allow mental health practitioners to put countermeasures in place to reduce the likelihood this even occurs; this may include phone and text reminders.
Unlike previous literature this dropout prediction problem is interested in dealing with patients who are yet to access any form of treatment, with the patient having just been referred to the service or post initial assessment (where the condition is discussed but no treatment is administered). Previous literature focuses on predicting dropout of patients once they have begun treatment, but we are interested in the section of patients which are yet to begin any form of treatment, and this is the novelty in our research, to understand this group of patients.

This prediction model will be built using a blended approach from both statistical and machine learning domains. Our methodology will not be vastly different than that of the previous literature, however, the application to our group of patients is more difficult, since the patient is yet to enter treatment, we will on average have less information per patient. These methods will include XGBoost, logistic regression and decision tree classifiers to build the model and we will be using various algorithms to fill in missing data.

Combining specialised modelling techniques with complex data analysis in order to deliver prediction with quantified uncertainties lies at the heart of many of the major challenges facing UK industry and society over the next decades. Indeed, the recent Government Office for Science report "Computational Modelling, Technological Futures, 2018" specifies putting the UK at the forefront of the data revolution as one of their Grand Challenges.

The beneficiaries of our research portfolio will include a wide range of UK industrial sectors such as the pharmaceutical industry, risk consultancy, telecommunications and advanced materials, as well as government bodies, including the NHS, the Met Office and the Environment Agency.

Examples of current impactful projects pursued by students and in collaboration with stake-holders include:

- Using machine learning techniques to develop automated assessment of psoriatic arthritis from hand X-Rays, freeing up consultants' time (with the NHS).

- Uncertainty quantification for the Neutron Transport Equation improving nuclear reactor safety (co-funded by Wood).

- Optimising the resilience and self-configuration of communication networks with the help of random graph colouring problems (co-funded by BT).

- Risk quantification of failure cascades on oil platforms by using Bayesian networks to improve safety assessment for certification (co-funded by DNV-GL).

- Krylov regularisation in a Bayesian framework for low-resolution Nuclear Magnetic Resonance to assess properties of porous media for real-time exploration (co-funded by Schlumberger).

- Machine learning methods to untangle oceanographic sound data for a variety of goals in including the protection of wildlife in shipping lanes (with the Department of Physics).

Future committed partners for SAMBa 2.0 are: BT, Syngenta, Schlumberger, DNV GL, Wood, ONS, AstraZeneca, Roche, Diamond Light Source, GKN, NHS, NPL, Environment Agency, Novartis, Cytel, Mango, Moogsoft, Willis Towers Watson.

SAMBa's core mission is to train the next generation of academic and industrial researchers with the breadth and depth of skills necessary to address these challenges. SAMBa's most sustained impact will be through the contributions these researchers make over the longer term of their careers. To set the students up with the skills needed to maximise this impact, SAMBa has developed a bespoke training experience in collaboration with industry, at the heart of its activities. Integrative Think Tanks (ITTs) are week-long workshops in which industrial partners present high-level research challenges to students and academics. All participants work collaboratively to formulate mathematical
models and questions that address the challenges. These outputs are meaningful both to the non-academic partner, and as a mechanism for identifying mathematical topics which are suitable for PhD research. Through the co-ownership of collaboratively developed projects, SAMBa has the capacity to lead industry in capitalising on recent advances in mathematics. ITTs occur twice a year and excel in the process of problem distillation and formulation, resulting in an exemplary environment for developing impactful projects.

SAMBa's impact on the student experience will be profound, with training in a broad range of mathematical areas, in team working, in academic-industrial collaborations, and in developing skills in communicating with specialist and generalist audiences about their research. Experience with current SAMBa students has proven that these skills are highly prized: "The SAMBa approach was a great template for setting up a productive, creative and collaborative atmosphere. The commitment of the students in getting involved with unfamiliar areas of research and applying their experience towards producing solutions was very impressive." - Dr Mike Marsh, Space weather researcher, Met Office.