Abstract: A computer implemented method synthesizes electroencephalograph signals. A number of processor units creates a training dataset comprising real electroencephalograph signals, speech signals correlating to the real electroencephalograph signals, and a set of human characteristics for the real electroencephalograph signals. The number of processor units trains a generative adversarial network using the training dataset to create a trained generative adversarial network. The trained generative adversarial network generates synthetic electroencephalograph signals in response to receiving new speech signals.

Abstract: A method, computer program product, and computer system for generating synthetic time-series data for a specific disease. One or more processors of a computer system provide a generative adversarial network (GAN), train the GAN to generate time series data using episodic measurement results as metadata for a patient cohort with a specific disease; receive input metadata associated with an episodic measurement for a patient in the patient cohort with the specific disease by the trained GAN, and generate synthetic time series data that simulates the patient in the patient cohort with the specific disease.

Abstract: A method, computer system, and a computer program product are provided for testing functional mobility and memory. Information is about a participant to an artificial intelligence (AI) engine and then instructions are provided to the participant to memorize a random set of steps. The participant is then asked to recall the random set of instruction steps and then to perform the steps memorized. The participant is asked to recall the steps again after the physical performance. The result of the test is analyzed and a final participant assessment is rendered based on the overall performance analysis. The participant is provided instructions and further monitored and recorded for the performance of the test using smart devices, video or similar means.

Abstract: Automated labeling of user sensor data is provided. It is determined that a user is at a medical facility using a location of a user device. User sensor data is collected from one or more user devices while the user is at the medical facility. A result is retrieved of a medical evaluation of the user performed at the medical facility. User sensor data collected during the medical evaluation is tagged with the retrieved result from the medical evaluation.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include identifying one or more joints of a user based on collected data and generating one or more 3D representations of the one or more joints of the user. Embodiments may further include anonymizing the one or more 3D representations, classifying one or more actions of the user based on the one or more 3D representations, wherein the classifying outputs an action score, and exporting at least one of the one or more actions and the action score.

Abstract: A method, a structure, and a computer system for traumatic event detection. The exemplary embodiments may include collecting data using sensors worn by a user and identifying a traumatic event based on applying a model to the data, wherein the model correlates values of the data with traumatic events and traumatic brain injuries. The exemplary embodiments may further include identifying the traumatic brain injury resulting from the traumatic event.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: A method, a structure, and a computer system for enabling telemedicine using printed devices. Exemplary embodiments may include receiving a design for a device and printing the device based on the design using a printer. The exemplary embodiments may further include combining the device with a smart device and utilizing the device to collect data during a telemedicine session administered on the smart device.

Abstract: A method, a structure, and a computer system for assessing a cognitive state of a driver of a vehicle. The exemplary embodiments may include collecting data from one or more sensors positioned around the vehicle and calculating a distraction value, an engagement value, and a workload value corresponding to the driver of the vehicle based on the data. The exemplary embodiments may further include determining whether the driver exhibits a low cognitive state based on the distraction value and the engagement value, and, based on determining that the driver exhibits the low cognitive state, assuming control of the vehicle.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing one or more Parkinson's disease symptoms. The exemplary embodiments may include collecting data of a user's motion, extracting one or more features from the collected data, and assessing one or more Parkinson's disease symptoms of the user based on applying one or more models to the data.

Abstract: A method, a structure, and a computer system for privacy-preserving motion analysis. Embodiments may include collecting data corresponding to a user with one or more sensors and identifying one or more joints of the user based on the data. Embodiments may additionally include generating one or more 3D representations of the one or more joints of the user and anonymizing the one or more 3D representations by applying thereto a joint-centering and a random shuffling. Embodiments may further include classifying one or more actions of the user based on analysing the one or more 3D representations, and exporting at least one of the data and the one or more actions.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system for assessing a user's gate. The exemplary embodiments may include collecting data corresponding to a walking user and assessing a gait of the user based on applying one or more models to the data.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined. Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined. Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: This application provides a method to identify genetic markers associated with increased sensitivity or resistance to hormonal therapies using an outlier analysis. More specifically, this application discloses that amplifications on chromosomes 8 and 17 are associated with increased proliferation and poor outcome in ER-positive breast cancer, and amplicons 17q21.33-q25.1, 8p11.2 and 8q24.3 may be responsible for higher proliferation and poor outcome in the setting of antiestrogen, in particular Tamoxifen, treatment clinically observed in a subset of ER-positive, HER2-negative breast cancers. The invention also provides use of the identified genetic markers in the development of targeted treatments for antiestrogen-resistant ER-positive breast cancers as well as in improving current methods of drug response prediction.

Abstract: Disclosed is a novel system, and method to evaluate a prediction of a possibly unknown outcome out of a plurality of predictions of that outcome. The method begins with accessing a particular prediction of an outcome out of a plurality of predictions of that outcome in which the outcome may be unknown. Next, a subsample of the plurality of predictions of the outcome is accessed. The subsample can possibly include the particular prediction. A consensus prediction of the outcome based on the subsample of the plurality of predictions is determined. A proximity of the particular prediction to the consensus prediction is determined. Each prediction is ranked out of the plurality of predictions in an order of a closest in proximity to the consensus prediction to a farthest in proximity to the consensus prediction.

Abstract: This application provides a method to identify genetic markers associated with increased sensitivity or resistance to hormonal therapies using an outlier analysis. More specifically, this application discloses that amplifications on chromosomes 8 and 17 are associated with increased proliferation and poor outcome in ER-positive breast cancer, and amplicons 17q21.33-q25.1, 8p11.2 and 8q24.3 may be responsible for higher proliferation and poor outcome in the setting of antiestrogen, in particular Tamoxifen, treatment clinically observed in a subset of ER-positive, HER2-negative breast cancers. The invention also provides use of the identified genetic markers in the development of targeted treatments for antiestrogen-resistant ER-positive breast cancers as well as in improving current methods of drug response prediction.