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公开(公告)号：US12027277B1
公开(公告)日：2024-07-02
申请号：US17111765
申请日：2020-12-04
IPC分类号： G16H50/70 , A61B5/00 , G06N5/04 , G06N20/00 , G16H10/60 , G16H40/67 , G16H50/20 , G16H50/30 , G16H50/50 , A61B5/024 , A61B5/0533 , A61B5/08 , A61B5/11
CPC分类号： G16H50/70 , A61B5/7267 , G06N5/04 , G06N20/00 , G16H10/60 , G16H40/67 , G16H50/20 , G16H50/30 , G16H50/50 , A61B5/02405 , A61B5/02438 , A61B5/0533 , A61B5/0816 , A61B5/1118 , A61B5/4806
摘要： 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.
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公开(公告)号：US20210151194A1
公开(公告)日：2021-05-20
申请号：US16953256
申请日：2020-11-19
发明人： Luca Foschini , Tom Quisel , Christine Lemke , Arya Pourzanjani , Haraldur Tomas Hallgrimsson , Jessie Juusola , Alessio Signorini , Ursula Nasch
摘要： 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., the health-behavior phenotype); and outputting information that identifies the particular population segment in association with the individual.
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公开(公告)号：US12033761B2
公开(公告)日：2024-07-09
申请号：US16926510
申请日：2020-07-10
摘要： 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.
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公开(公告)号：US20210241923A1
公开(公告)日：2021-08-05
申请号：US16926510
申请日：2020-07-10
摘要： 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.
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公开(公告)号：US12119115B2
公开(公告)日：2024-10-15
申请号：US18156010
申请日：2023-01-18
发明人： Luca Foschini , Filip Jankovic , Raghunandan Melkote Kainkaryam , Juan Ignacio Oguiza Mendez , Arinbjörn Kolbeinsson
摘要： 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.
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