Abstract: Systems and methods are disclosed for predicting a next text. A method may include receiving one or more documents, such as a document associated with a healthcare provider. The document is then processed to generate one or more tokens which are representative of the document. The document is then processed with a machine-learning model, such as a topic model, and a topic vector is output for the document. Based at least in a part on this topic vector, the document is then processed by one or more expert machine-learning models, which each output a probability vector. The various probability vectors are then further processed to calculate a total probability vector for the document. Based at least in part on the total probability vector for the document, a text output is selected.

Abstract: Embodiments of the present disclosure provide for improved data processing using interconnected variational autoencoder models, which may be used for any of a myriad of purposes. Some embodiments specially train the interconnected variational autoencoder models by utilizing different training scenarios corresponding to presence and/or absence of particular data in a training data set. Particular encoder(s) and/or decoder(s) from the specially trained interconnected variational autoencoder models may then be utilized to improve accuracy of the desired data processing tasks, for example, to generate particular output data.

Abstract: Embodiments provide for application of personalized or individualized sensor-based risk profiles for impacts of external events. An example method includes receiving sensor data from one or more sensors couplable with a subject body of a subject population comprising a plurality of subject bodies; receiving external factor data associated with the subject population; generating a population-level external event impact metric, where the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population; generating a subject-level external impact metric, where the subject-level external event impact metric represents a predicted impact of the one or more external events on the physiological or other metric associated with the subject body; and initiating the performance of one or more prediction-based actions based on the subject-level external event impact 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: 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: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may generate a plurality of multidimensional patient-drug tensors based at least in part on a plurality of patient record data objects and a plurality of combined drug input vectors, generate an interaction-attentive prediction data object based at least in part on the plurality of multidimensional patient-drug tensors and at least one interaction-attentive machine learning model, generate an interaction-inattentive prediction data object based at least in part on the plurality of patient record data objects and an interaction-inattentive machine learning model, determine a drug combination indicator based at least in part on comparing the interaction-attentive prediction data object and the interaction-inattentive prediction data object, generate a predicted multi-drug contraindication data object based at least in part on the drug combination indicator, and perform one or more prediction-based actions.

Abstract: Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.

Abstract: Various embodiments of the present disclosure disclose a machine learning training approach for intelligently training a plurality of machine learning models associated with a multitask environment. The techniques include jointly training the plurality of machine learning models based on task similarities by generating a similarity matrix corresponding to a plurality machine learning models, generating a sharing loss value for the at least two machine learning models, generating, using a loss function and a training dataset, a prediction loss value for a particular machine learning model of the at least two machine learning models, generating an aggregated loss value for the particular machine learning model based on the similarity matrix, the sharing loss value, and the prediction loss value, and updating the particular machine learning model based on the aggregated loss value for the particular machine learning model.

Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.

Abstract: Various embodiments of the present disclosure disclose machine-learning based data augmentation and prediction techniques for generating predictive classifications based on temporal data. A machine-learning based model is provided that can receive an input data object associated with a plurality of predictive temporal parameters; determine augmented temporal data objects based on the predictive temporal parameters; generate predictive data representations for the input data object based on the predictive temporal parameters and the augmented temporal data objects; generate a multi-channel predictive data representation based on the predictive data representations for the input data object; and generate a predictive classification for the input data object based on the multi-channel predictive data representation.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.

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.

Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.

Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Abstract: The present disclosure provides methods and systems for incorporating multiple environmental and genetic risk factors into an individual's genomic profile. Methods include assessing the association between an individual's genotype and at least one disease or condition by incorporating multiple genetic risk factors, environmental risk factors, or a combination of both.

Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.

Abstract: A method of comparing nucleic acid sequences being ESTs included in a first database of sequences and nucleic acid sequences included in a second database of sequences to form groups of sequences from the two databases that all relate to the same gene. For each one or more n-groups of sequences of one of the two databases, associating therewith lists of nucleic acid sequences, each from one of said two databases, each sequence on the list containing the n-groups, and matching sequences on the lists to generate said group.