Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital files in accordance with user requests. For instance, in some cases, the disclosed systems receive, from a client device, a user request for modifying a digital file. The disclosed systems generate, using a language machine learning model, a task plan having formatted code indicating one or more application programming interface calls to execute to modify the digital file. Further, the disclosed systems generate, via one or more code verifications on the formatted code, an error log that identifies one or more errors in the task plan. The disclosed systems generate, from the error log and using the language machine learning model, a corrected task plan that corrects the one or more errors. Additionally, the disclosed systems provide, for display, a modified digital file generated through execution of the corrected task plan.

Abstract: A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.

Abstract: Position-based text-to-speech model and training techniques are described. A digital document, for instance, is received by an audio synthesis service. A text-to-speech model is utilized by the audio synthesis service to generate digital audio from text included in the digital document. The text-to-speech model, for instance, is configured to generate a text encoding and a document positional encoding from an initial text sequence of the digital document. The document positional encoding is based on a location of the text encoding within the digital document. Digital audio is then generated by the text-to-speech model that includes a spectrogram having a reordered text sequence, which is different from the initial text sequence, by decoding the text encoding and the document positional encoding.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a temporal dependency graph. For example, the disclosed systems generate from a text document, a structural vector, a syntactic vector, and a semantic vector. In some embodiments, the disclosed systems generate a multi-dimensional vector by combining the various vectors. In these or other embodiments, the disclosed systems generate an initial dependency graph structure and an adjacency matrix utilizing an iterative deep graph learning model. Further, in some embodiments, the disclosed systems generate an entity-level relation matrix utilizing a convolutional graph neural network. Moreover, in some embodiments, the disclosed systems generate a temporal dependency graph from the entity-level relation matrix and the adjacency matrix.

Abstract: A Clinical Decision Support System (CDSS) provides a comprehensive system to capture a patient's clinical encounter and observation data to inject into a risk calculation algorithm to align with alerts that can support physicians to make clinical decisions for treatment regimes. A software architecture and framework is created with functionality specific modules to develop a CDSS system for a disease area.

Abstract: Systems and methods for document editing are provided. One aspect of the systems and methods includes obtaining a document and a natural language edit request. Another aspect of the systems and methods includes generating a structured edit command using a machine learning model based on the document and the natural language edit request. Yet another aspect of the systems and methods includes generating a modified document based on the document and the structured edit command, where the modified document includes a revision of the document that incorporates the natural language edit request.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a digital document hierarchy comprising layers of parent-child element relationships from the visual elements. For example, for a layer of the layers, the disclosed systems determine, from the visual elements, candidate parent visual elements and child visual elements. In addition, for the layer of the layers, the disclosed systems generate, from the feature embeddings utilizing a neural network, element classifications for the candidate parent visual elements and parent-child element link probabilities for the candidate parent visual elements and the child visual elements. Moreover, for the layer, the disclosed systems select parent visual elements from the candidate parent visual elements based on the parent-child element link probabilities. Further, the disclosed systems utilize the digital document hierarchy to generate an interactive digital document from the digital document image.