Abstract: A local machine learning based recording and audio transcribing system is proposed that operates in real-time in parallel and time-synchronized with a operating theatre recorder system, modifying encoding of the operating theatre recorder system outputs for generation of the pre-processed recording files to be transmitted to a remote cloud-based processing backend using operating a centralized machine learning model data architecture across a network. By modifying the recording or the generation of the pre-processed recording files, the system can be tuned for providing increased signal resolution at more relevant portions of time or frame portions to improve the accuracy and predictive capability of a backend machine learning model that is configured for operation using the pre-processed recording files while adapting for practical network bandwidth and computing resource limitations in cloud-based implementations.

Abstract: A computer implemented system for automatically recording and generating predictive outputs relating to a medical procedure is described. The system is augmented with biometric sensory data from a biometric sensor coupled to a body of a healthcare practitioner. The biometric sensory data is processed to obtain one or more time-synchronized data objects providing a proxy to an estimated stress level associated with the healthcare practitioner, and the one or more time-synchronized data objects are utilized to identify abnormality-related durations of time encapsulated in the form of time-based metadata tags. The time-based metadata tags are utilized to automatically modify characteristics of the recording or generating of predictive outputs to temporarily consume more computational processing resources during the abnormality-related durations of time.

Abstract: A computer implemented system for automatically recording and generating predictive outputs relating to a medical procedure is described. The system is augmented with biometric sensory data from a biometric sensor coupled to a body of a healthcare practitioner. The biometric sensory data is processed to obtain one or more time-synchronized data objects providing a proxy to an estimated stress level associated with the healthcare practitioner, and the one or more time-synchronized data objects are utilized to identify abnormality-related durations of time encapsulated in the form of time-based metadata tags. The time-based metadata tags are utilized to automatically modify characteristics of the recording or generating of predictive outputs to temporarily consume more computational processing resources during the abnormality-related durations of time.