Koninklijke Philips Corporation N.v.

Cristhian Mauricio Potes Blandon

Bryan Conroy

Minnan Xu

Asif Rahman

Oladimeji Feyisetan Farri

Sheikh Sadid Al Hasan

Christine Menking Swisher

Rithesh Sreenivasan

Yuan Ling

Jonathan Rubin

Gary Nelson Garcia Molina

Erina Ghosh

Pedro Miguel Ferreira Dos Santos Da Fonseca

A method () for generating a textual description of a medical image, comprising: receiving () a medical image of an anatomical region, the image comprising one or more abnormalities; segmenting () the anatomical region in the received medical image from a remainder of the image; identifying () at least one of the one or more abnormalities in the segmented anatomical region; extracting () one or more features from the identified abnormality; generating (), using the extracted features and a trained text generation model, a textual description of the identified abnormality; and reporting (), via a user interface of the system, the generated textual description of the identified abnormality.

Convolutional localization networks for intelligent captioning of medical images

Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions () may be provided (). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function () may be provided () as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided () as context training data. An approximation function may be applied () to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained () based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

Learning and applying contextual similiarities between entities

Techniques disclosed herein relate to learning and applying contextual patient similarities. In various embodiments, a first value for a query entity may be displayed () on an interface. The first value may be related to a first context. A first trained similarity function may be selected () from a plurality of trained similarity functions. The first trained similarity function may be associated with the first context. The first selected trained similarity function may be applied () to a set of features associated with the query entity and respective sets of features associated with a plurality of candidate entities. A set of one or more similar candidate entities may be selected () from the plurality of candidate entities based on application of the first trained similarity function. Information associated with the first set of one or more similar candidate entities may be displayed () on the interface.

Learning and applying contextual similarities between entities

An electronic clinical decision support (CDS) device () employs a trained CDS algorithm () that operates on values of a set of covariates to output a prediction of a medical condition. The CDS algorithm was trained on a training data set (). The CDS device includes a computer () that is programmed to provide a user interface () for completing clinical survey questions using the display and the one or more user input devices. Marginal probability distributions () for the covariates of the set of covariates are generated from the completed clinical survey questions. The trained CDS algorithm is adjusted for covariate shift using the marginal probability distributions. A prediction of the medical condition is generated for a medical subject using the trained CDS algorithm adjusted for covariate shift () operating on values for the medical subject of the covariates of the set of covariates.

Electronic clinical decision support device based on hospital demographics

When evaluating the quality of photoplethysmography (PPG) signal () measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as 'clean.' If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as 'noisy.'

Method to quantify photoplethysmogram (PPG) signal quality

A system () for monitoring an individual's sleep includes: (i) a patient monitor () configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor () in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface () configured to report the generated sleep quality measurement.

Method and system for monitoring sleep quality

Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

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Salem, NH, United States of America

Cristhian Mauricio Potes Blandon