Explainable hierarchical clustering for patient subtyping and risk prediction.

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

RUBIC: An Untethered Soft Robot With Discrete Path Following.

Soft robots have the potential to diminish the need for humans to venture into unsuitable environments or work in extreme conditions. While their soft nature gives them the advantage of being adaptable to changing environments, their control can be challenging because of the compliance that makes them effective. In this paper we present RUBIC: the Rolling, Untethered, Ballooning, Intelligent Cube, that overcomes some of the difficulties of 2D control by constraining motion to a discretised Cartesian space. RUBIC's method of locomotion is by rolling from one face of the cube to another, in any one of four directions. This motion causes it to move within a 2D grid structure, the dimensions of which are defined by the cube's characteristic length. When in its resting position RUBIC is inherently stable and forms a safe platform for tasks including taking measurements and soil samples, for localization and ad hoc network infrastructure, and as the foundation for larger robots and structures. We present the design of RUBIC's body, the four pneumatic ballooning actuators per face that generate its unique gait, and the control systems for locomotion and obstacle climbing. We consider constraints imposed by the design and fabrication methods including physical dimension and weight, material properties and control fidelity. An alternative locomotion scheme is proposed to improve the speed and linearity which also increases the distance traveled per roll. RUBIC travels with a mean locomotion accuracy of 4.58° deviation and successfully traverses steps up to 35% of its own height. The discretisation of a soft robotics workspace, as demonstrated by RUBIC, has advantages for safe and predictable locomotion and has applications in both structured and hazardous environments.