Abstract: The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.

Abstract: The present disclosure relates to a rope traction type grinding, cleaning, and coating integrated operation robot. The operation robot includes a hanging basket, a first traction mechanism connected to the hanging basket, a grinding mechanism arranged in front of the hanging basket, and a cleaning and spraying mechanism and a spring reaction force regulation mechanism arranged in the hanging basket. The first traction mechanism includes first ropes for connecting the hanging basket and first rope winding mechanisms. The cleaning and spraying mechanism includes a first vertical plate and a second vertical plate that are arranged in parallel in a vertical direction. A cleaning nozzle and a spraying nozzle are mounted on the first vertical plate. From the above technical solution, it can be seen that the operation robot adopts a rope traction manner, and has the advantages of large work space, low mechanism inertia, and accurate and reliable location.

Abstract: A computer in a distributed peer-to-peer system is disclosed. The distributed system includes a plurality of computers configured to run a distributed machine learning (ML) program represented as an expression of a target loss function with a model parameter matrix. The computer includes: a parser module configured to convert a loss function in the distributed program into an expression graph and then one or more multiplication trees; a parameter replica module in communication with the parser module, the parameter replica module configured to maintain the model parameter matrix of the ML program; a compressor module in communication with the parameter replica module, the compressor module configured to extract sufficient factors from the expression graph for updating the model matrix; and a communication module in communication with the compressor module, the communication module configured to send the sufficient factors for updating model matrix to other machines in the distributed system.

Abstract: The present disclosure relates to a rope traction type grinding, cleaning, and coating integrated operation robot. The operation robot includes a hanging basket, a first traction mechanism connected to the hanging basket, a grinding mechanism arranged in front of the hanging basket, and a cleaning and spraying mechanism and a spring reaction force regulation mechanism arranged in the hanging basket. The first traction mechanism includes first ropes for connecting the hanging basket and first rope winding mechanisms. The cleaning and spraying mechanism includes a first vertical plate and a second vertical plate that are arranged in parallel in a vertical direction. A cleaning nozzle and a spraying nozzle are mounted on the first vertical plate. From the above technical solution, it can be seen that the operation robot adopts a rope traction manner, and has the advantages of large work space, low mechanism inertia, and accurate and reliable location.

Abstract: A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

Abstract: The present invention is a system and a method to classify clinical records into International Classification of Diseases (ICD) codes. The system includes a processor, and a memory communicatively coupled to the processor. The memory includes a generator (G), a feature extractor, a discriminator (D), a label encoder, and a keywords reconstructor. The generator (G) generates synthesized features corresponding to ICD code descriptions. The feature extractor extracts real latent features from clinical documents and generates real features by training a GANs. The generator (G) generates synthesized features after the GANs are trained and calibrate a binary code classifier with the real latent features generated by the feature extractor for a low-shot ICD code l. The feature extractor generates code-specific latent features conditioned on a textual description of each ICD code description by using a WGAN-GP.

Abstract: A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.

Abstract: A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

Abstract: A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.

Abstract: The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.

Abstract: The current disclosure is directed towards providing systems and methods for identifying atheromatous plaques in optical coherence tomography (OCT) images. In one example, a method for a trained neural network may include acquiring an OCT image slice of an artery, identifying one or more image features of the OCT image slice with the trained neural network, and responsive to the one or more image features indicating a thin-cap fibroatheroma (TCFA), segmenting the OCT image slice into a plurality of regions with the trained neural network, the plurality of regions including a first region depicting the TCFA, and determining start and end coordinates for the TCFA based on the first region.

Abstract: A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

Abstract: A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

Abstract: A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

Abstract: A system for automatically assigning a set of ICD codes to a patient includes a diagnostic description encoding module and an ICD code assignment module. The diagnostic description encoding module is configured obtain a diagnostic description vector from at least one diagnostic description record of the patient. The diagnostic description record may be in the form of hand-written physician notes. The ICD code assignment module is configured to apply a machine-learned ICD code assignment algorithm to the diagnostic description vector to assign a set of ICD codes to the patient. When multiple codes are assigned, the machine-learned ICD code assignment algorithm establishes an order of importance for the ICD codes.

Abstract: A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.

Abstract: A machine learning system that includes a plurality of machine learning processors, maintains a topic matrix that represents the relevancies or measures of prevalence of a plurality of medical topics among a plurality of clinical documents. Each processor in the system is configured to determine at least one local sufficient factor group for a document included in the plurality of documents, and to send the at least one local sufficient factor group to one or more other processors in the system. Each processor is further configured to receive at least one remote sufficient factor group from another processor in the system, and to process the local sufficient factor group together with the remote sufficient factor group to obtain the topic matrix. The remote sufficient factor group or groups are determined by other processors in the system for another document included in the plurality of documents.

Abstract: Accordingly, patient similarity measurement system is disclosed. In one embodiment, the patient similarity measurement system includes a diversity-promoting distance metric learning (DPDML) model, wherein said PSM system is configured to perform PSM tasks by receiving inputs of the electronic health records (EHRs) of two patients, and generating an output of a score that indicates the similarity of the two patients. One embodiment provides a method for of performing patient similarity measurement via a diversity-promoting distance metric learning model, comprising receiving inputs of the electronic health records (EHRs) of a first patient and a second patient, and generating an output of a score that indicates the similarity of the first and second patient. Other embodiments are disclosed herein.

Abstract: A medication-recommending system is disclosed. The medication-recommendation system includes: a medication-medication correlation (MMC) sub-module configure to generate a correlation score of a first candidate medication and a second candidate medication; a medication-EHR dependency (MED) sub-modules configure to generate a dependency score between each of the first and second medications and an electronic health record (EHR); a relation-constraint (RC) sub-module configured to generate a relationship constraint indicating the interaction relation between the first and second medications; and a medication selection (MS) sub-module configure to select one or more recommended medications from at least the first and second medications based on the correlation score, dependency scores, and relational constraint.

Abstract: A computer in a distributed peer-to-peer system is disclosed. The distributed system includes a plurality of computers configured to run a distributed machine learning (ML) program represented as an expression of a target loss function with a model parameter matrix. The computer includes: a parser module configured to convert a loss function in the distributed program into an expression graph and then one or more multiplication trees; a parameter replica module in communication with the parser module, the parameter replica module configured to maintain the model parameter matrix of the ML program; a compressor module in communication with the parameter replica module, the compressor module configured to extract sufficient factors from the expression graph for updating the model matrix; and a communication module in communication with the compressor module, the communication module configured to send the sufficient factors for updating model matrix to other machines in the distributed system.