Abstract: An active learning system can analyze a dataset of users with self-reported symptoms and associated data from wearable devices to train a baseline machine learning model to predict symptoms of a chronic health condition based on wearable device data. For example, symptoms can be predicted in terms of lost physical activity, increased sleep requirements, and changes in resting heart rate. Using the baseline model, the active learning system can train and refine individual user-specific models to predict the onset of chronic health condition symptoms over time. These models can be used to predict symptoms for inclusion in a log of symptoms for the target user (which may be used by a healthcare provider to personalize treatment for the target user) or to provide interventions to the user (for example, warning of a predicted severe symptom day). In some implementations individual chronic health condition models are maintained and updated using active learning techniques.

Abstract: Disclosed is a method comprising accessing, by a machine learning system, a set of data records for a plurality of users, the data records representative of physical statistics measured for each of the plurality of users over a time period. At least a subset of the data records comprises patterns of missing data for at least a portion of the time period. The method also comprises generating a set of masked data records by masking a subset of the data records in accordance with a pattern of natural missingness from a data record. The method also comprises generating, by the machine learning system, a set of learned representations from at least the set of masked data records. Finally, the method comprises fine tuning, by the machine learning system, a machine learning model using the set of learned representations, the machine learning model configured to perform a downstream machine learning task.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: Disclosed is a method comprising accessing, by a machine learning system, a set of data records for a plurality of users, the data records representative of physical statistics measured for each of the plurality of users over a time period. At least a subset of the data records comprises patterns of missing data for at least a portion of the time period. The method also comprises generating a set of masked data records by masking a subset of the data records in accordance with a pattern of natural missingness from a data record. The method also comprises generating, by the machine learning system, a set of learned representations from at least the set of masked data records. Finally, the method comprises fine tuning, by the machine learning system, a machine learning model using the set of learned representations, the machine learning model configured to perform a downstream machine learning task.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: An active learning system can analyze a dataset of users with self-reported symptoms and associated data from wearable devices to train a baseline machine learning model to predict symptoms of a chronic health condition based on wearable device data. For example, symptoms can be predicted in terms of lost physical activity, increased sleep requirements, and changes in resting heart rate. Using the baseline model, the active learning system can train and refine individual user-specific models to predict the onset of chronic health condition symptoms over time. These models can be used to predict symptoms for inclusion in a log of symptoms for the target user (which may be used by a healthcare provider to personalize treatment for the target user) or to provide interventions to the user (for example, warning of a predicted severe symptom day). In some implementations individual chronic health condition models are maintained and updated using active learning techniques.

Abstract: Disclosed is a method comprising accessing, by a machine learning system, a set of data records for a plurality of users, the data records representative of physical statistics measured for each of the plurality of users over a time period. At least a subset of the data records comprises patterns of missing data for at least a portion of the time period. The method also comprises generating a set of masked data records by masking a subset of the data records in accordance with a pattern of natural missingness from a data record. The method also comprises generating, by the machine learning system, a set of learned representations from at least the set of masked data records. Finally, the method comprises fine tuning, by the machine learning system, a machine learning model using the set of learned representations, the machine learning model configured to perform a downstream machine learning task.

Abstract: In an aspect, a computer-implemented method for assembling a pool of high-risk subjects for developing an acute illness is disclosed. The method comprises obtaining, from a plurality of subjects, (i) one or more responses to one or more health queries, and (ii) geographic incidence data for the acute illness. The method next comprises predicting, using a machine learning model, a risk of developing the acute illness for the plurality of subjects based on the one or more responses and the geographic incidence data. The method next comprises identifying the pool of high-risk subjects from the plurality of subjects, wherein the risk of developing the acute illness for each subject of the pool of high-risk subjects satisfies a threshold. Finally, the method comprises outputting the pool of high-risk subjects.

Abstract: In an aspect, a method for predicting, for a subject, a recovery time from an acute or debilitating event is disclosed. The method may comprise (i) retrieving wearable sensor data from a first time period and a second time period. The first time period may be prior to the acute or debilitating event. The second time period may be after the acute or debilitating event. The method also may comprise (ii) determining the recovery time for the acute or debilitating event at least in part by processing said wearable sensor data from the first time period and the second time period with a trained machine learning algorithm.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: Embodiments of the present disclosure relate systems and methods for detecting cognitive decline of a subject using passively obtained data from at least one mobile device. In an exemplary embodiment, a computer-implemented method comprises receiving passively obtained data from at least one mobile device. The method further comprises generating digital biomarker data from the passively obtained data. The method further comprises analyzing the digital biomarker data to determine whether the subject is exhibiting signs of cognitive decline.

Abstract: A machine learning prediction system can analyze a dataset of users with self-reported symptoms and associated data from a wearable device to impact measure the impact of an acute health condition (such as the flu) at the population level. The machine learning prediction system can train a machine learning model to recognize individual acute health condition patterns based on differences in user activity with respect to the characteristics of determined baseline periods. For example, per-individual normalized change with respect to baseline aggregated at the population level can be used to determine individual acute health condition patterns and predict the onset of certain acute health conditions using a trained machine learning model. In response to predictions, the machine learning prediction system can take interventions to manage the impact of a predicted acute health condition on an individual.

Abstract: In one implementation, a computer-implemented method includes accessing, upon authorization, behavior data that includes one or more time series of events indicating health-related behaviors of an individual; determining a behavior score for the individual based on the behavior data, the behavior score indicating the individual's latent behavior state; augmenting the behavioral score with medical data for the individual; identifying, by the computer system, a health-behavior phenotype for the individual based on a current position or trajectory of the augmented behavioral score within a latent health-behavior space that correlates the individual's augmented behavioral score with the health-behavior phenotype for the individual; assigning the individual to a particular population segment based, at least in part, on the current position or trajectory of the individual within the latent health-behavior space (i.e.

Abstract: In one implementation, a computer-implemented method includes receiving a request to perform an experiment across a plurality of client computing devices associated with a plurality of users, the request includes criteria for users to be included in the experiment and parameters for the experiment; obtaining information for the plurality of users; selecting a subset of the plurality of users for the experiment based, at least in part, on the information; determining a minimum population size to provide at least a threshold (i) level of anonymity for participants in the experiment and (ii) power calculation for results of the experiment, wherein the minimum population size is determined based, at least in part, on the subset of the plurality of users and the parameters for the experiment; providing information that identifies the minimum population size for the experiment.

Abstract: Systems and methods for clustering news information are disclosed. The news information is clustered to form clusters to include one or more of articles, blogs, images, videos and the like. The news information is organized according to topic and/or temporal information. The clustered news information can be presented to a user who can browse or search the clustered news information.

Abstract: Systems and methods for organizing related news information is disclosed herein. The systems and methods include clustering a stream of news information according to the topic of each news information and according to temporal information of the news information. Systems and methods for presenting information to users are also disclosed herein. The systems and methods include receiving a search for news information from a user and presenting the news information according to the topic of the news information and the temporal information.