Deep Generative Models for Treatment Effect Inference in Healthcare

Healthcare treatment recommendations are typically formulated by studying the 'average patient' and conducting randomised control trials (RCTs). However, individual responses to treatments vary. Current healthcare decisions often lack personalised, data-driven approaches, resulting in suboptimal outcomes. Tailoring treatments to a patient's evolving characteristics and past responses (dynamic treatment regimens) is complex due to sequential decision-making, data demands, and computational challenges. Furthermore, identifying key factors influencing treatment effects in clinical practice is difficult. Estimating the causal effects of treatments using electronic health records (EHRs) requires innovative methods that account for patients' health status and outcomes over time.

The primary objective of this project is to model clinical treatment effects for various medical scenarios. Among other techniques, this will be achieved by using deep generative models. These models learn the fundamental representation of EHRs and allow to create synthetic twins that closely resemble a patient's characteristics and temporal dynamics.
1. The first goal of this proposed project is to develop a personalised treatment effect model, based on the synthesis of a patient's EHR. The proposed model will facilitate the generation of representative digital twins that can serve as synthetic counterparts for individual patients, enabling in-depth analysis and exploration of personalised healthcare outcomes.
2. The second research goal will focus on leveraging the proposed model to identify and optimise treatment regimes. This involves utilising the digital twins' representation to estimate the causal treatment effects for individual patients and exploring their responses to different treatments. The objective is to develop a decision-making framework that enables the identification of optimal treatment strategies for a variety of possible scenarios and healthcare settings including (i) primary care, (ii) secondary care in the hospital wards and (iii) secondary care in the intensive care unit (ICU). The primary care setting is a promising environment for testing use cases related to health conditions that progress gradually, such as chronic diseases like hypercholesterolemia. In the hospital setting, we could explore dynamic treatment regimens like administering antibiotics for infections, which have been under-studied. Potential applications in an ICU setting include treating sepsis through administering vasopressors.

This project falls within the EPSRC Health Technologies research area and specifically addresses Challenge 3, discovering and accelerating the development of new interventions. The research showcases its novelty by departing from the conventional approach of deriving treatment effects solely from average effects. Instead, it strives to empower doctors with personalised treatment recommendations that consider the unique characteristics of each patient. More specifically, the research will be novel in terms of the medical applications that are being explored and have been under-studied to date, but also in terms of methods deployed. For instance, we aim to create new personalised treatment effect models applicable to dynamic treatment regimens and will devise methods to ensure the clarity and interpretability of predictions. Furthermore, we will investigate new ways to validate the applicability of the framework to real-world medical use-cases, e.g., by performing an observational study to reproduce findings from a large-scale RCT study. The outcomes will potentially enhance decision-making in healthcare, leading to improved patient outcomes and tailored treatments. Additionally, this approach offers a promising avenue to overcome challenges in clinical trials by providing a simulated environment to assess treatment efficacy and identify personalised treatment options.

In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.

Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.

Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.

Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.

Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.