[Operating room nurses and the surgical robot, turning a competitor into an ally]. / Les infirmières de bloc opératoire et le robot chirurgical, faire d'un concurrent un allié.

The ambivalence of the professional identity of the State Certified Operating Room Nurse (SCNO) is due to the difficulty of building a non-surgical technicality. Therefore, the participation of ORNs in robotic developments seems to be a way of emancipation for the profession.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

[Dictating one's medical report to control the interaction. About the use of speech recognition technologies in medical consultations]. / Dicter son compte-rendu pour maîtriser l'interaction - De l'usage de la reconnaissance vocale en consultation médicale.

Title: Dicter son compte-rendu pour maîtriser l'interaction - De l'usage de la reconnaissance vocale en consultation médicale. Abstract: « Les humanités en santé : approches de terrain ¼ sont coordonnés par Claire Crignon, professeure d'histoire et de philosophie des sciences à l'université de Lorraine, qui a créé le master « humanités biomédicales ¼ à Sorbonne université.

[Socio-anthropological and ethical issues of the augmented operating theatre]. / Enjeux socio-anthropologiques et éthiques du bloc opératoire augmenté.

The arrival of new technologies in the operating theatre raises questions about surgical practice, in a context of societal changes and hospital reorganisation. These innovations will bring changes to the ethos of the profession and ethical issues will be raised by the increase of the surgeon.

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and whose complexity depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search. We show how to extract rules that are not necessarily short, but characteristic of the input, and easy to visualize. Our experiments show that the proposed approach is able to model the functionality of the neural network while at the same time producing interpretable logical rules. Thus, we demonstrate the potential of rule-based approaches for images which allows to combine advantages of neural networks and rule learning.

Detection of changes in the circadian rhythm of cattle in relation to disease, stress, and reproductive events.

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24 h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of ~120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e.g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events.