Abstract: Disclosed is a method for supporting disease analysis, the method including classifying, on the basis of images obtained from a plurality of analysis target cells contained in a specimen collected from a subject, a morphology of each analysis target cell, and obtaining cell morphology classification information corresponding to the specimen, on the basis of a result of the classification; and analyzing a disease of the subject by means of a computer algorithm, on the basis of the cell morphology classification information.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: The invention relates generally to a system and methods by which a microbiome-based action component may be developed that is useful in order to establish, restore, promote, or maintain subject health. More specifically, the system and methods of the present invention may be used to develop a microbiome-based action component that efficiently summarizes the state of a subject's microbiome along with measures of gut maturity in case of an infant and nutrition readiness in case of an adult subject. Such an efficient summary may be used to render more immediate support or health care to the subject.

Abstract: Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

Abstract: The invention generally relates to computer-based systems to evaluate and market clinical trial research centers. In certain aspects, the invention provides computer-based systems to collect information about clinical research centers. Systems include a tangible, non-transitory memory coupled to a processor operable to retrieve, based on a user's input, an identity of a clinical research center and prompt the user for information relating generally to the center. The system can collect disease-specific information by prompting the user for a selection of a disease and then collecting from the user information identifying an ability of the center to perform one or more tests relating to the disease.

Abstract: A heart-rate detection system can receive heartbeat data generated by a wearable heart-rate sensor worn by a wearer. The system can then execute a noise-reduction process for reducing noise in the heartbeat data. The noise-reduction process can involve applying a lowpass filter to the heartbeat data, generating wavelet coefficients by applying a wavelet transform to the filtered heartbeat data, and generating a reduced set of wavelet coefficients by thresholding the wavelet coefficients. An inverse wavelet signal can then be generated by applying an inverse wavelet transform to the reduced set of wavelet coefficients. R-peaks can be identified by performing peak detection on the instantaneous amplitudes of the data points in the inverse wavelet signal. A heart rate curve can then be generated based on the R-peaks and modified by applying a Hampel filter. Heartbeat data can then be generated based on the modified heart rate curve for output.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes generating, by a processor, utilizing a risk determination machine learning model and based at least in part on one or more hidden features of the first predictive entity, the predicted risk measure, and performing one or more prediction-based actions based at least in part on the predicted risk measure.

Abstract: Pulmonary function estimation can include detecting one or more cough events from a time series of audio signals generated by an electronic device of a user. Based on the one or more cough events, one or more lung function metrics of the user can be determined.

Abstract: The present invention relates to a method for generating a report (50) comprising receiving a user input to thereby generate at least one final finding (24); automatically generating the report (50) based on the at least one final finding (24); and utilizing the at least one final finding (24) to train at least one algorithm (12), wherein the at least one algorithm (12) is configured to generate at least one machine finding (22). The present invention also relates to a corresponding system and use.

Abstract: An application system capable of providing a continuation of care for a user. This is achieved through the assessing of medication compliance, assessing medical testing compliance and providing appropriate care suggestions. The system incorporates medication reminder, medication compliance analytics and the capacity to provide responses to user specific medication inquiries. The system also incorporates medical test assessment capabilities and the capacity to provide basic medical testing protocols. Additionally, as medication and medical testing data are collected, processed and analyzed, they are stored in a zero-click fashion with results being rapidly presented back to the user via an interactive avatar. These data can also be provided in standard of care summaries to users, HIPAA compliant third parties and/or medical care providers.

Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: A personalized health maintenance system, includes reading of an input-output data pair of each medical diagnosis test of a plurality of different medical diagnosis tests conducted for a first user; and cross-correlate a plurality of results of each medical diagnosis test of the plurality of different medical diagnosis tests to find a relationship among the plurality of different medical diagnosis tests for the first user. A coherent health state of the first user is determined based on the cross-correlation of the plurality of different medical diagnosis tests. The personalized health maintenance system includes a robotic system configured to output a first digital therapeutic environment around the first user. The first digital therapeutic environment is a human-sense stimulating mixed reality environment that induces a specific stimulus to the first user for remediation of at least one condition of the determined coherent health state of the first user.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: An integrated system and method for personalized stratification and prediction of neurodegenerative disease state and progression rate. Homogenous patient clusters are identified among heterogeneous patient data. Each patient cluster is characterized by specific disease factors that are used to predict disease progression state and rate for patients assigned to a particular patient cluster in accordance with his disease profile.

Abstract: In an aspect an apparatus for determining toxic load quantifiers is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user input comprising user data. At least a processor is configured to generate a query as a function of user data. At least a processor is configured to obtain query results as a function of a query. At least a processor is configured to determine a toxic load quantifier of a query results of query results as a function of a toxicity criterion and user data. At least a processor is configured to display a toxic load quantifier of query results to a user.

Abstract: A system for timing impact of nourishment consumption, the system including a computing device configured to receive a nutrient profile of a subject, wherein the nutrient profile maps physiological data of the subject to current nutrient levels of the subject, determine, using the nutrient profile, a nourishment consumption program, wherein the nourishment consumption program includes at least an alimentary element, and a time of day for consuming the alimentary element wherein the time of day is determined as a function of the nutrient profile and the current nutrient level of the subject, and provide, to the subject, the nourishment consumption program.

Abstract: Described herein are systems, media, and methods for assessing an individual by generating a classification or regression based on input data comprising metabolite information, protein information, nucleic acid information, non-molecular information, or any combination thereof.

Abstract: A method of operating a health tracking system is disclosed. The method comprises: storing a food knowledge graph having a plurality of labels describing consumable items and a plurality of relationships between pairs of labels, some of the labels being generic names for consumable items; receiving a data record having a descriptive string regarding a consumable item from a first health tracking device; matching the descriptive string to at least one label in the plurality of labels; and updating one or more information fields of the data record to associate the data record with the at least one label to which the descriptive string was matched. In some embodiments, the method further includes receiving a request for data records from a health tracking device and matching the request to the plurality of labels to provide an improved response to the request for data records.

Abstract: A patient monitoring system includes a physiological sensor to sense light after it has passed through tissue of a patient and generate a signal indicative a physiological parameters in response to the sensed light, and a patient monitoring device in communication with the physiological sensor to receive the signal and determine measurements of the physiological parameters from the received signal. The patient monitoring device includes a processor and memory having multiple system images. The patient monitoring device downloads an image upgrade to one system image that the not latest used or tested system image. The patient monitoring device boots the processor from the system image that includes the image upgrade. If the upgraded system image fails, the patient monitoring device boots the processor from another system image of the multiple system images. The patient monitoring device repairs the failed system image.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.