Alizée Pace, MSc MPhil
- alizee.pace@ ai.ethz.ch
ETH AI Center
Department of Computer Science
- CAB E 77.1
My research interests are centred on applications of machine learning and causal inference in medicine. I develop imitation and reinforcement learning methods for treatment prediction and clinical decision support.
I am a Doctoral Fellow at the ETH AI Center, jointly supervised by Prof. Gunnar Rätsch of the BMI group and Prof. Bernhard Schölkopf, leader the Empirical Inference group at the Max-Planck Institute for Intelligent Systems (Tübingen, Germany). Prior to my PhD, I led a project on imitation learning for clinical decision-making with Prof. Mihaela van der Schaar at the University of Cambridge. My professional experience also includes medical device development for stroke treatment, sensor-assisted surgery and 3D-printed heart stents, as well as software engineering at CERN. In parallel, I studied Physics, Materials Science and Machine Learning at Cambridge.
Please see my personal website for further information.
Abstract Recent advances in deep learning architectures for sequence modeling have not fully transferred to tasks handling time-series from electronic health records. In particular, in problems related to the Intensive Care Unit (ICU), the state-of-the-art remains to tackle sequence classification in a tabular manner with tree-based methods. Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features. Given the similar level of feature heterogeneity exhibited by ICU time-series and motivated by these findings, we explore these novel methods' impact on clinical sequence modeling tasks. By jointly using such advances in deep learning for tabular data, our primary objective is to underscore the importance of step-wise embeddings in time-series modeling, which remain unexplored in machine learning methods for clinical data. On a variety of clinically relevant tasks from two large-scale ICU datasets, MIMIC-III and HiRID, our work provides an exhaustive analysis of state-of-the-art methods for tabular time-series as time-step embedding models, showing overall performance improvement. In particular, we evidence the importance of feature grouping in clinical time-series, with significant performance gains when considering features within predefined semantic groups in the step-wise embedding module.
Authors Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch
Submitted ML4H 2023 (PMLR)
Abstract Models that can predict the occurrence of events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging task is typically treated as a simple binary classification, ignoring temporal dependencies between samples, whereas we propose to exploit this structure. We first introduce a common theoretical framework unifying dynamic survival analysis and early event prediction. Following an analysis of objectives from both fields, we propose Temporal Label Smoothing (TLS), a simpler, yet best-performing method that preserves prediction monotonicity over time. By focusing the objective on areas with a stronger predictive signal, TLS improves performance over all baselines on two large-scale benchmark tasks. Gains are particularly notable along clinically relevant measures, such as event recall at low false-alarm rates. TLS reduces the number of missed events by up to a factor of two over previously used approaches in early event prediction.
Authors Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
Submitted ICML 2023
Abstract Balancing forces within weight-bearing joints such as the hip during joint replacement is essential for implant longevity. Minimising implant failure and the corresponding need for expensive and difficult revision surgery is vital to both improve the quality of life of the patient and lighten the burden on overstretched healthcare systems. However, balancing forces during total hip replacements is currently subjective and entirely dependent on surgical skill, as there are no sensors currently on the market that are capable of providing quantitative force feedback within the small and complex geometry of the hip joint. Here, we solve this unmet clinical need by presenting a thin and conformable microfluidic force sensor, which is compatible with the standard surgical procedure. The sensors are fabricated via additive manufacturing, using a combination of 3D and aerosol-jet printing. We optimised the design using finite element modelling, then incorporated and calibrated our sensors in a 3D printed model hip implant. Using a bespoke testing rig, we demonstrated high sensitivity at typical forces experienced following implantation of hip replacements. We anticipate that these sensors will aid soft tissue balancing and implant positioning, thereby increasing the longevity of hip replacements. These sensors thus represent a powerful new surgical tool for a range of orthopaedic procedures where balancing forces is crucial.
Authors Liam Ives, Alizée Pace, Fabian Bor, Qingshen Jing, Tom Wade, Jehangir Cama, Vikas Khanduja, Sohini Kar-Narayan
Submitted Materials & Design
Abstract Building models of human decision-making from observed behaviour is critical to better understand, diagnose and support real-world policies such as clinical care. As established policy learning approaches remain focused on imitation performance, they fall short of explaining the demonstrated decision-making process. Policy Extraction through decision Trees (POETREE) is a novel framework for interpretable policy learning, compatible with fully-offline and partially-observable clinical decision environments -- and builds probabilistic tree policies determining physician actions based on patients' observations and medical history. Fully-differentiable tree architectures are grown incrementally during optimization to adapt their complexity to the modelling task, and learn a representation of patient history through recurrence, resulting in decision tree policies that adapt over time with patient information. This policy learning method outperforms the state-of-the-art on real and synthetic medical datasets, both in terms of understanding, quantifying and evaluating observed behaviour as well as in accurately replicating it -- with potential to improve future decision support systems.
Authors Alizée Pace, Alex Chan, Mihaela van der Schaar
Submitted ICLR 2022 (Spotlight)