Abstract: An emerging service in the medical industry is to provide high quality remote care to patients by remotely monitoring patient vital information. In some instances, patient vitals information is collected at a much higher frequency in comparison to a traditional clinical environment. This frequent influx of patient health information can result in a considerable amount of health-related noise that health computing systems and clinicians must evaluate. The instant systems and methods leverage autoregressive recurrent neural networks and patient embeddings to predict the likelihood of needing to address certain patient information, thereby reducing the amount of health-related noise and to enable health computing systems and clinicians to use their resources more effectively.

Abstract: An emerging service in the medical industry is to provide high quality remote care to patients by remotely monitoring patient vital information. In some instances, patient vitals information is collected at a much higher frequency in comparison to a traditional clinical environment. This frequent influx of patient health information can result in a considerable amount of health-related noise that health computing systems and clinicians must evaluate. The instant systems and methods leverage autoregressive recurrent neural networks and patient embeddings to predict the likelihood of needing to address certain patient information, thereby reducing the amount of health-related noise and to enable health computing systems and clinicians to use their resources more effectively.

Abstract: There are several data attributes available for a patient in electronic medical records, including vitals, medications, symptoms, notes from clinical encounters, and other biographical and demographic information. Various machine learning techniques can be leveraged to extract valuable insights from this data. However, it can be challenging to train machine learning algorithms on a large volume of labeled data in a supervised setting. As a solution to such deficiencies in supervised approaches, neural networks can be trained in an unsupervised environment, wherein autoencoders learn lower dimensional representations of the attributes. Once trained, the neural networks can effectively predict input features, detect anomalies, and forecast time series information.

Abstract: A composite clinical language modeling system that can leverage textual attributes of a patient's medical record for analytics, visualizations and accessibility. The composite clinical language model leverages a trainer module that fine-tunes a pre-trained language model using this text corpus, producing a model that can be customized for specific use cases. This model is then used to produce embeddings from input text which can then be used for several task-specific natural language processing models, wherein each task-specific natural language processing model has its own individual transfer learning loop that is responsible for continuously improving and fine-tuning task-specific natural language processing models for these specific tasks.

Abstract: A multi-modal end to end learning system configured to answer questions about clinical documents like patient notes, medical reports, and lab results. Documents are polled from an electronic medical record system, converted to text, and scrubbed for protected health information before processing. Sanitized text data is then fed as context to a language model that has been fine-tuned for question-answering (QA). The other input to the model is a prompt or a question that is either provided on-the-fly by a clinician as part of a search or pre-determined for specific needs. In return, the model outputs an answer highlighting part of the text/image where it found the answer and a confidence score quantifying the likelihood of the answer being correct. A clinician can optionally correct the answer if needed. This feedback by the clinician is fed back to a fine-tuner module and used to improve the model over time.

Abstract: An emerging service in the medical industry is to provide high quality remote care to patients by remotely monitoring patient vital information. In some instances, patient vitals information is collected at a much higher frequency in comparison to a traditional clinical environment. This frequent influx of patient health information can result in a considerable amount of health-related noise that health computing systems and clinicians must evaluate. The instant systems and methods leverage autoregressive recurrent neural networks and patient embeddings to predict the likelihood of needing to address certain patient information, thereby reducing the amount of health-related noise and to enable health computing systems and clinicians to use their resources more effectively.