Abstract: Various embodiments of the present disclosure provide machine learning training techniques for training a model to improve upon traditional prediction models for various prediction domains. The techniques may include receiving training tuples for a training entity. A machine learning model may be used to generate a prediction output for the training entity based on the training tuples. A composite loss function may be used to generate a composite loss metric for the machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of historical reward measures and (ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output. One or more model parameters of the first machine earning model may be modified based on the composite loss metric.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a prediction output comprising one or more actions by receiving data associated with encounters in a tuple form, tokenizing the encounters, training a causal transformer machine learning model configured to predict outcomes of actions by translating action tokens from the tokenized encounters into one or more embedding spaces, and training a causal transformer machine learning model to select the one or more actions based on embeddings from the one or more embedding spaces.

Abstract: Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Abstract: Systems and methods for training a signal generation model and generating imputed physiological waveform signals in accordance with embodiments of the invention are illustrated. One embodiment includes a method for measuring physiological waveform signals. The method includes steps for receiving a set of one or more input physiological waveform signals, processing the set of input physiological waveform signals, generating an output physiological waveform signal using a signal generation model, and providing outputs based on the generated output signal.