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 Models that can predict adverse events ahead of time with low false-alarm rates are critical to the acceptance of decision support systems in the medical community. This challenging machine learning task remains typically treated as simple binary classification, with few bespoke methods proposed to leverage temporal dependency across samples. We propose Temporal Label Smoothing (TLS), a novel learning strategy that modulates smoothing strength as a function of proximity to the event of interest. This regularization technique reduces model confidence at the class boundary, where the signal is often noisy or uninformative, thus allowing training to focus on clinically informative data points away from this boundary region. From a theoretical perspective, we also show that our method can be framed as an extension of multi-horizon prediction, a learning heuristic proposed in other early prediction work. TLS empirically matches or outperforms considered competing methods on various early prediction benchmark tasks. In particular, our approach significantly improves performance on clinically-relevant metrics such as event recall at low false-alarm rates.
Authors Hugo Yèche, Alizée Pace, Gunnar Rätsch, Rita Kuznetsova
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)