Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: Enclosed are embodiments for estimating gait time events and GCT using a wrist-worn device. In some embodiments, a method comprises: obtaining, with at least one processor of a wrist-worn device, sensor data indicative of acceleration and rotation rate; and predicting, with the at least one processor, at least one gait event time based on a machine learning (ML) model with the acceleration and rotation rate as input to the ML model.

Abstract: Embodiments are disclosed for a biomechanical trigger for improved responsiveness in grade estimation. In some embodiments, a method comprises: A method comprises: obtaining, from a wearable device worn by a user, cadence data, speed data and elevation data; determining a grade of a surface on which the user is traveling based on a ratio of a change in elevation based on the elevation data and a change in speed data; determining that the grade satisfies a first condition indicative of a horizontal speed compensation by the user at a grade onset; determining that the grade satisfies a second condition indicative of a rapid elevation increase or decrease at a grade onset; and confirming that the grade is a valid estimate based on either the first condition or the second condition being satisfied.

Abstract: Methods, systems, and devices are contemplated for assembling a genome from duplicate segments of the genome. Sequences with a first level of common neighboring base pairs are identified and organized into first level groups. Groups are then identified from the first level groups that have a second level of common neighboring base pairs and organized into a number of second level groups. A third level of groups can further be organized in some embodiments. Typically the second level groups are assembled into spans having contiguous base pair sequences, which are then assembled into the broader genome sequence. The inventive subject matter is preferably used for whole genome sequencing.

Abstract: Embodiments are disclosed for identifying poor cardio metabolic health using sensors of wearable devices. In an embodiment, a method comprises: obtaining estimates of maximal oxygen consumption of a user during exercise; determining at least one confidence weight based on context data; adjusting the maximal oxygen consumption estimates using the at least one confidence weight; aggregating the adjusted maximal oxygen consumption estimates to generate a summary maximal oxygen consumption estimate and corresponding confidence interval for the user; and classifying cardiorespiratory fitness of the user based on at least one of the summary maximum consumption estimate, the corresponding confidence interval, a population error model or a low cardiorespiratory fitness threshold.

Abstract: Embodiments are disclosed for assessing walking steadiness of a mobile device user. In some embodiments, a method comprises: obtaining, with at least one processor of a mobile device, one or more mobility metrics indicative of a user's mobility, the mobility metrics obtained at least in part from a time series of sensor data output by at least one sensor of the mobile device; evaluating, with the at least one processor, the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features indicative of variability of the user's gait; and generating, with the at least one processor, at least one walking steadiness indicator for the user based on one or more walking steadiness component models and the one or more longitudinal features. Also disclosed are embodiments for training the component models.

Abstract: Embodiments are disclosed for assessing fall risk of a mobile device user. In some embodiments, a method comprises: obtaining one or more mobility metrics indicative of a user’s mobility, the mobility metrics obtained at least in part from sensor data output by at least one sensor of the mobile device; evaluating the one or more mobility metrics over one or more specified time periods to derive one or more longitudinal features; estimating a plurality of walking steadiness indicators based on a plurality of component models and the one or more longitudinal features; inferring the user’s risk of falling based at least in part on the plurality of walking steadiness indicators; and initiating an action or application on the mobile device based at least in part on the user’s risk of falling.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: One or more electronic device may use motion and/or activity sensors to estimate a user's maximum volumetric flow of oxygen, or VO2 max. In particular, although a correlation between heart rate and VO2 max may be linear at high heart rate levels, there is not a linear correlation at lower heart rate levels. Therefore, for users without extensive workout data, the motion sensors and activity sensors may be used to determine maximum calories burned by the user, workout data, including heart rate data, and body metric data. Based on these parameters, a personalized relationship between the user's heart rate and oxygen pulse (which is a function of VO2) may be determined, even with a lack of high intensity workout data. In this way, a maximum heart rate and therefore a VO2 max value may be approximated for the user.

Abstract: Methods, systems, and devices are contemplated for assembling a genome from duplicate segments of the genome. Sequences with a first level of common neighboring base pairs are identified and organized into first level groups. Groups are then identified from the first level groups that have a second level of common neighboring base pairs and organized into a number of second level groups. A third level of groups can further be organized in some embodiments. Typically the second level groups are assembled into spans having contiguous base pair sequences, which are then assembled into the broader genome sequence. The inventive subject matter is preferably used for whole genome sequencing.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: Techniques for identifying data for use in a healthcare application are described. In an example, a server can receive, via a graphical user interface (GUI), first instructions defining rule(s) associated with an outcome that is relevant to a healthcare application and second instructions defining a schema for translating the rule(s) into logical expression(s). The server can access a plurality of data entries and can apply the logical expression(s) to the plurality of data entries to identify a first data entry that satisfies the rule(s). At a substantially same time, the server can apply a model to the plurality of data entries to identify a second data entry that is predicted to be associated with the outcome. The server can generate an output identifying the first data entry and the second data entry and can utilize the output for the healthcare application.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: A plan authoring tool can receive user input describing parameters of benefits of a benefit plan. The plan authoring tool can convert the user input into a machine structured plan description (MSPD) that defines the benefit plan. The MSPD can be used by a plan documentation generator to generate one or more plan documents that explain the benefits and/or indicate how to manually adjudicate claims submitted in association with the benefit plan. The MSPD can also be used to automatically adjudicate claims. Plan analysis tools can be used to evaluate an MSPD to validate a new or edited benefit plan and/or provide user feedback on the benefit plan so that users, if desired, can revise the MSPD via the plan authoring tool.

Abstract: A computer-implemented method for estimating biophysiological rates using the Hilbert transform includes receiving a quasiperiodic data stream from a biophysiological sensor, and removing at least a portion of an offset from the quasiperiodic data stream to provide a smoothed data stream by filtering the quasiperiodic data stream through a bandpass filter and phase compensating the filtered quasiperiodic data stream. The method also includes transforming the smoothed data stream into an analytic data stream using a Hilbert transform approximation and calculating the time derivative of the phase angle of the analytic data stream, where the time derivative is a frequency of the quasiperiodic data stream. The method further includes providing an output data stream derived from the frequency.