Causal AI for Continuous Postoperative Deterioration Monitoring in Global Surgery

Continuous wearable sensing can flag postoperative deterioration earlier than intermittent observations, but prediction alone does not quantify the impact of alternative actions. Using the EMUs multi-country “shadowmode” datasets linking Sibel ANNE® One waveforms to time-stamped events and 30-day outcomes (NCT06565559), this PhD will develop AI-first causal models: self-supervised representations of physiology, counterfactual sequence prediction, and off-policy evaluation of escalation strategies. The goal is decision support that surfaces expected benefits, harms, and uncertainty across settings, with robustness and equity tests for global surgery.

Why study with us What is CHAI? 

CHAI is the Causality in Healthcare AI Hub that unites an international consortium of universities, industry partners, government bodies, and regulatory entities to develop cutting-edge causal AI innovations to enhance patient care and outcomes. We are working to develop an explainable causal AI platform specifically addressing unique challenges from healthcare across prevention, diagnosis, and treatment. We believe that to move AI forward, we need to build models incorporating both theory and observation. This will create models that are more transportable, explainable, fair, and of more direct relevance for decision support. The CHAI Hub co-develops research with clinical experts, policy makers, and patients to address complex and heterogenous data structures, nuanced real-world problems, and a rapid pathway to societal and economic impact. 

What is Sibel Health Inc.? 

Sibel Health is a healthcare technology company that develops advanced wireless wearable sensors to continuously monitor vital signs such as heart rate, respiratory rate, temperature, and movement. Its solutions are used in hospitals, clinical research, and remote patient monitoring to provide accurate, real-time health data. The company’s mission is to deliver “Better Health Data for All®” by improving the quality, accessibility, and reliability of patient data so clinicians and researchers can make more informed decisions and ultimately improve patient outcomes worldwide. What can you get from working with us? At CHAI, we’re building a dynamic team of innovators, researchers, and professionals dedicated to transforming healthcare through causal AI. If you’re passionate about making a meaningful impact in healthcare, AI, and data science, CHAI offers a unique opportunity to work at the forefront of causal AI development with a focus on real-world solutions and societal benefit. We offer: 

- An innovative environment. Work on cutting-edge AI solutions for healthcare challenges, from optimising treatment outcomes to advancing diagnostic capabilities. 

- A collaborative culture. Join a multidisciplinary team of experts across AI, healthcare, and data science, with collaboration at the heart of everything we do. 

- Professional growth. We offer opportunities for professional development, including training, mentorship, and access to leading research. 

- Commitment to sustainability. At CHAI, we integrate environmental responsibility into our projects and strive to make sustainable choices in AI innovation. View our website at this link to see what it is like to be an early career researcher with us: https://www.chai.ac.uk/being-a-chai-ecr 

References 

https://clinicaltrials.gov/study/NCT06565559 https://bmjopen.bmj.com/content/bmjopen/15/10/e104463.full.pdf

Project background 

Postoperative deterioration is often detected late because ward monitoring is intermittent and early warning scores compress complex physiology into sparse snapshots. Continuous wearable waveforms create an opportunity to learn patientspecific trajectories and early phenotypes of complications, but real impact requires causal, action-aware modelling rather than “higher AUC”. EMUs (NCT06565559) is an international, prospective, observational cohort collecting up to 10 days of continuous chest/limb sensor data in “shadow mode” alongside standard clinical data and 30-day outcomes. Sibel Health develops wireless, clinical-grade continuous monitoring (ANNE® One) capturing ECG waveforms and vital-sign streams suitable for hospital and resource-variable environments. 

The CHAI Hub focuses on explainable, transportable causal AI platforms that integrate theory and observation to support decisions. This PhD will fuse these strengths to estimate how different postoperative pathways (e.g., earlier escalation vs watchful waiting) change outcomes, and how those effects generalise across health systems. 

Research aims 

1. Learn high-fidelity, clinically grounded representations of postoperative physiology from waveforms and context. 

2. Estimate heterogeneous causal effects of time-varying decisions (escalation, antibiotics, imaging, ICU transfer) using AI-augmented counterfactual modelling and off-policy evaluation. 

3. Develop transportable and fair causal decision policies that remain reliable under domain shift, missingness, and resource constraints, and present trade-offs (benefit/harms/uncertainty) for shared clinical decision making. 

Data & methodology 

Data will extend EMUs by linking ANNE® One waveforms and derived features to time-stamped actions, observations, complications and 30-day outcomes. Methods will emphasise AI-centric causal learning: self-supervised pretraining (contrastive/masked modelling) on waveforms; transformer/state-space models for latent physiologic state; neural causal effect estimators (representation-balancing + doubly robust learners) for static and sequential treatments; and causal reinforcement learning for dynamic treatment regimes with off-policy evaluation and calibrated uncertainty. Robustness will be tested via invariant/causal representation learning and fairness audits across sites. 

Expected outcome and impact 

Outputs of the work as a whole include: 

(i) a pretrained physiologic foundation model for postoperative monitoring; 

(ii) validated counterfactual estimators for action timing and escalation thresholds, with subgroup and site heterogeneity; 

(iii) an interpretable prototype that compares “what if” trajectories under alternative care pathways and highlights trade-offs and uncertainty; 

(iv) evidence on transportability and equity of causal policies across high- and lower-resource settings; and 

(v) trial-ready specifications for an interventional evaluation of AI-guided monitoring strategies. 

Timescale of expected outcomes 

Year 1: governance, waveform pipeline, representation pretraining, and clinical target-trial specification using EMUs extensions. 

Year 2: build and validate counterfactual sequence models and neural effect estimators; define candidate policies and off-policy evaluation plan. 

Year 3: transportability, robustness, and fairness analyses across countries; prototype trade-off interface with clinical co-design. 

Year 4: external validation on newly accrued data; ablation and shift-stress tests; publications, thesis, and a pragmatic trial protocol with deployment metrics. Student training and development The student will gain depth in modern causal ML and sequential decision making (causal representation learning, off-policy evaluation, causal RL), plus signal processing and self-supervised modelling of physiological waveforms. Training will be supported through CHAI Hub and Sibel Health.

How to apply

As part of your application, include the name of Professor Sotirios Tsaftaris in the statement, and discuss your motivation for doing a PhD, what attracts you for this PhD position (including the group, the university, CHAI, and Sibel Health) and what your aspirations are for after the PhD. In the application title, add '“Project funding by Prof. Tsaftaris and Sibel Health for CHAI”

For frequently asked questions regarding applying for a PhD position in Causal AI in understanding medical images, please visit here: https://vios.science/faq/ 

Minimum entry requirements

• a 2:1 undergraduate degree (or equivalent).
• the University’s English language requirements.

What we are looking for?

We encourage applicants to provide evidence of the qualities below in their application, through coursework, projects, work experience, or independent learning. You do not need direct experience within every area, but you should show motivation to learn, awareness of why these skills matter, and a thoughtful attitude toward developing them during the programme. 

We recognise that applicants come from many different starting points. If you have taken a non-traditional route or faced circumstances affecting your performance, we encourage you to describe this in your application so we can consider it appropriately. 

A successful candidate will: 

- Be a student home to the UK who will be based in Edinburgh;

- Have a strong foundation in either computer science, AI, cognitive science, mathematics, physics, engineering, biomedical science, biological science, or clinical & public health sciences;

- Be able to demonstrate skills training in programming, data analysis, or computational thinking, and ideally evidence of successful deployment of these skills in the form of a project;

- Have a genuine interest in biomedical or health applications and awareness of the challenges specific to this sector; 

- Appreciate that AI in medicine.

This project is fully funded for a home student (fees and stipend).

To qualify as a Home student, you must fulfil one of the following criteria:

• You are a UK student OR

• You are an EU student with settled/pre-settled status who also has 3 years residency in the UK/EEA/Gibraltar/Switzerland immediately before the start of your programme. 

  International students are not eligible.

Further information and other funding options.