Work towards MSc degree under the supervision of Dr. Shlomi Laufer
When: 29.9.2021 at 14:00
Where: zoom
Abstract: Medical simulators provide a controlled environment for training and assessing clinical skills. However, as an assessment platform, it requires the presence of an experienced examiner to provide performance feedback, commonly preformed using a task specific checklist. This makes the assessment process inefficient and expensive. Furthermore, this evaluation method does not provide medical practitioners the opportunity for independent training. Ideally, the process of managing the simulation should be done by a fully aware objective system, capable of recognizing and monitoring the clinical performance and to act accordingly. In our study we applied techniques from graph networks and language models to construct a fully autonomic simulation framework, based on clinical data collected from 28 medical simulations. A key finding of our work is that by analyzing physicians’ speech alone, we can successfully perform state estimation and to make predictions regarding their medical treatment planning. We propose that the fully autonomic speech-based framework for managing medical simulations constructed in this study is applicable to clinical practice. In a field where seconds can make the difference between life and death, integrating an autonomic assisting tool is of great importance.