A computer-implemented method for processing and/or analyzing nucleic acid sequencing data comprises receiving a first data input and a second data input. The first data input comprises untargeted sequencing data generated from a first nucleic acid sample obtained from a subject. The second data input comprises target-specific sequencing data generated from a second nucleic acid sample obtained from the subject. Next, with the aid of a computer processor, the first data input and the second data input are combined to produce a combined data set. Next, an output derived from the combined data set is generated. The output is indicative of the presence or absence of one or more polymorphisms of the first nucleic acid sample and/or the second nucleic acid sample.

Apparatus and methods for error modeling and correction in one or both of (i) a partially or fully implanted or non-implanted medicant delivery mechanism (such as a pump), and (ii) implanted physiologic parameter sensor. In one exemplary embodiment, the apparatus and methods employ a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the medicant dose calculation process, and (ii) generation of a medicant delivery operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor and pump system. This enhances accuracy of medicant delivery, such as over the lifetime of an implanted pump at a single implantation site, or during multiple relocations of a transcutaneously implanted pump), and enables “personalization” of the pump to each user.

Apparatus and methods for error modeling and correction in one or both of (i) a partially or fully implanted or non-implanted medicant delivery mechanism (such as a pump), and (ii) implanted physiologic parameter sensor. In one exemplary embodiment, the apparatus and methods employ a training mode of operation, whereby the apparatus conducts “machine learning” to model one or more errors (e.g., unmodeled variable system errors) associated with the medicant dose calculation process, and (ii) generation of a medicant delivery operational model (based at least in part on data collected/received in the training mode), which is applied to correct or compensate for the errors during normal operation of the sensor and pump system. This enhances accuracy of medicant delivery, such as over the lifetime of an implanted pump at a single implantation site, or during multiple relocations of a transcutaneously implanted pump), and enables “personalization” of the pump to each user.

The present approach provides a non-invasive methodology for estimation of coronary flow and/or fractional flow reserve. In certain implementations, various approaches for personalizing blood flow models of the coronary vasculature are described. The described personalization approaches involve patient-specific measurements and do not assume or rely on the resting coronary flow being proportional to myocardial mass. Consequently, there are fewer limitations in using these approaches to obtain coronary flow and/or fractional flow reserve estimates non-invasively.

A prescription management system is used by an orthodontic or dental lab and a plurality of prescribing users who send prescriptions for customized orthodontic or dental appliances to the lab. The prescriptions are stored in a database selectively accessible by the lab and plurality of prescribing users. A digital workspace is provided in the system in which the lab or prescribing users may create designs for the customized appliances. The designs of the appliances are stored in the database. A tracking record of fabrication of the appliances is stored in the database. A plurality of billings are simultaneously generated in response to the submissions and storage of the prescriptions, the designs of the customized appliances and the fabrication of the designed customized appliances.

Treatment trajectory guidance systems and methods are provided. In one embodiment, the method for treatment trajectory guidance in a patient's brain includes obtaining a three- dimensional (3D) brain model that includes a model of an anatomy, the model of the anatomy including a plurality of feature points; modifying the 3D brain model based on magnetic resonance (MR) data of the patient's brain from a magnetic resonance imaging (MRI) device to obtain a plurality of modified feature points on a modified model of the patient's anatomy in the patient's brain; displaying on a display a first planned trajectory for treating the patient's anatomy based on the plurality of modified feature points; and displaying, on the display, a first estimated treatment result for the first planned trajectory.

Treatment trajectory guidance systems and methods are provided. In one embodiment, the method for treatment trajectory guidance in a patient's brain includes obtaining a three- dimensional (3D) brain model that includes a model of an anatomy, the model of the anatomy including a plurality of feature points; modifying the 3D brain model based on magnetic resonance (MR) data of the patient's brain from a magnetic resonance imaging (MRI) device to obtain a plurality of modified feature points on a modified model of the patient's anatomy in the patient's brain; displaying on a display a first planned trajectory for treating the patient's anatomy based on the plurality of modified feature points; and displaying, on the display, a first estimated treatment result for the first planned trajectory.

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

A peristaltic pump is disclosed that includes a plunger, a spring, an actuator, a position sensor, and a processor. The plunger actuates toward and away from a tube. The spring biases the plunger toward the tube. The actuator actuates the plunger away from the tube and mechanically engages and disengages from the plunger. The position sensor senses a position of the plunger. The processor receives the sensed position of the plunger and estimates fluid flow within the tube using a first position of the plunger when the actuator is engaged with the plunger and a second position of the plunger when the actuator is disengaged from the plunger.