Abstract: Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.

Abstract: A method and apparatus include receiving, by a device, medical information associated with a user. Inquiry information is determined based on the medical information associated with the user and a reinforcement learning model. The inquiry information is provided to permit response information to be received. The response information is received based on providing the inquiry information. Diagnosis information is determined based on the medical information and the response information using a machine learning model. The diagnosis information is provided to a set of devices via a network.

Abstract: A method and apparatus are provided that includes, in a same framework, storing an artificial intelligence (AI) model, loading the AI model into a serving platform, loading and testing a test unit against the AI model loaded into the serving platform, and collecting reports from results of storing the AI model, loading the AI model into the serving platform and testing the test unit.

Abstract: A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

Abstract: A method and user device for providing a unified Parkinson's disease rating scale (UPDRS) value by a user device includes generating, by a sensor of the user device, sensor data based on a user of the user device manipulating the user device. The UPDRS value is determined based on the sensor data and a deep neural network (DNN) model. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

Abstract: Embodiments of the present disclosure include a method, device and computer readable medium involving receiving image data of one or more coronary arteries, generating a binary segmentation indicating presence of calcium in the one or more coronary arteries from the image data, generating a branch density of the one or more coronary arteries, and assigning a coronary artery label from the branch density to the binary segmentation such that at least one indication of presence of calcium of the binary segmentation is labeled as present in a specific one of the one or more coronary arteries.

Abstract: Provided are a method and an apparatus for receiving a query including a sequence of words. The sequence of words is converted into a sequence of feature vectors. A semantic meaning of the sequence of words is generated using a bi-directional long-short term memory (LSTM). The LSTM is regularized using a Gaussian mixed model (GMM) based on the generated semantic meaning of the sequence of feature vectors.

Abstract: There is included an apparatus and system including image segmentation code, configured to cause at least one hardware processor to segment an image of a person's hand from an input image, and classification code configured to cause the at least one processor to classify the segmented image of the person's hand according to at least one predefined pose.

Abstract: A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

Abstract: A method and apparatus include receiving a three dimensional (3D) non-contrast computed tomography (NCCT) image of a head including a hematoma. A plurality of two dimensional (2D) images of the head including the hematoma are generated using the 3D NCCT image of the head including the hematoma. A plurality of 2D hematoma images are generated using a first 2D convolutional neural network (CNN) based on the plurality of 2D images. A 3D region of interest (ROI) that encompasses the hematoma is identified based on the plurality of 2D hematoma images. A plurality of 2D images that correspond to the ROI are generated. A hematoma expansion (HE) prediction score is determined using a second CNN based on the plurality of 2D images that correspond to the ROI. The HE prediction score is provided.

Abstract: A method and user device for determining a unified Parkinson's disease rating scale (UPDRS) value associated with a user of the user device include obtaining video data associated with a movement of a body part of the user. The UPDRS value is determined using a model and the video data associated with the movement of the body part of the user. The UPDRS value is provided to permit an evaluation of the user based on the UPDRS value.

Abstract: A method and apparatus are provided that includes iteratively sampling candidates from medical records and evaluating whether ones of the candidates better explain a member from the medical records. The iterations replace the member with the candidates and depending on whether the candidates better explain the member from the medical records may be weighted in a next iteration.

Abstract: There is included an apparatus and system including an intra-heartbeat (HB) extraction code configured to extract intra-HB features from electrocardiography (ECG) signals, and an inter-HB extraction code configured to extract inter-HB features from the ECG signals, and at least one attention mechanism code configured to control at least one of the intra-HB extraction code and inter-HB extraction code based on at least one attention mechanism.

Abstract: A method and apparatus include receiving a first sentence including a first set of words, and a second sentence including a second set of words. A first set of vectors corresponding to the first set of words of the first sentence, and a second set of vectors corresponding to the second set of words of the second sentence are generated using a word embedding model. A similarity matrix based on the first set of vectors and the second set of vectors is generated. An alignment score associated with the first set of vectors and the second set of vectors is determined using the similarity matrix. The alignment score is transmitted to permit information retrieval based on a similarity between the first sentence and the second sentence.

Abstract: Provided herein are turn-on fluorescent chemical probes useful for monitoring and/or detecting lysine delipoylation activity in a sample including or suspected of including a delipoylation enzyme.

Abstract: A method, computer program, and computer system is provided for estimating three-dimensional hand poses in images. Data corresponding to two hand images is receive, and an optical flow value corresponding to a change in a hand gesture in the received hand image data is calculate. A heat map is generated based on the calculated optical flow, and a hand mesh map is estimated based on the generated heat map. A hand pose present within the hand images is determined based on the estimated hand mesh map.

Abstract: A computer-implemented method, computer readable storage medium, and computer system is provided for estimating three-dimensional (3D) hand poses in images by receiving data corresponding to a hand image, generating a depth map corresponding to the received hand image data, and estimating a hand pose from the received hand image data and the generated depth map.

Abstract: The present invention relates to a compound for use in detecting “reader” and “eraser” proteins of lysine lipoylation. The present invention provides an affinity-based probe, referred to herein as “KPlip”, capable of interrogating the lipoylated peptide/protein interactions under native cellular environments. The chemical probe allows for the identification of potential regulators of lysine lipoylation, thus uncovering new biology related to lipoylation. There is also provided a method of using the compound to identifying proteins that interact with lipoylated proteins.

Abstract: Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.

Abstract: A method and apparatus for generating augmented training data for hand pose estimation include receiving source data that is associated with a first lighting condition. Target data that is associated with a second lighting condition is received. A lighting condition translation between the first lighting condition and the second lighting condition is determined. Lighting translated data is generated based on the lighting condition translation and the source data. Augmented training data for hand pose estimation is generated based on the target data and the lighting translated data.