A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.

A method for operating a clinic viewer on a computing device of a medical personnel is disclosed. The method includes receiving a reason that a patient scheduled an appointment with the medical personnel. The method further includes receiving a condition diagnosed for the patient and a care plan generated for the condition, wherein the care plan is generated by an artificial intelligence engine of a cognitive intelligence platform. The method further includes presenting, on the clinic viewer, the care plan and a watch-list comprising the reason, the condition, or some combination thereof.

A method for operating a patient viewer on a computing device of a patient is described herein. The method may include receiving an action instruction for the patient to perform, wherein the action instruction pertains to a condition of the patient, and the action instruction is generated based on the condition of the patient by an artificial intelligence engine of a cognitive intelligence platform. The method may further include presenting the action instruction in a first screen of the patient viewer, receiving medical records comprising information about the condition of the patient, and presenting at least a portion of the medical records in a second screen of the patient viewer. The method may further include receiving recommended curated content pertaining to the condition of the patient to educate the patient about the condition and presenting the recommended curated content in a third screen of the patient viewer.

A method for operating a patient viewer on a computing device of a patient is described herein. The method may include receiving an action instruction for the patient to perform, wherein the action instruction pertains to a condition of the patient, and the action instruction is generated based on the condition of the patient by an artificial intelligence engine of a cognitive intelligence platform. The method may further include presenting the action instruction in a first screen of the patient viewer, receiving medical records comprising information about the condition of the patient, and presenting at least a portion of the medical records in a second screen of the patient viewer. The method may further include receiving recommended curated content pertaining to the condition of the patient to educate the patient about the condition and presenting the recommended curated content in a third screen of the patient viewer.

Described are methods and systems for evaluating glycemic control and glucose homeostasis in a subject. Also described is a model of glucose homeostasis based on proportional and integral terms in a control system. A representative curve is generated based on glucose time series data and fit to the model in order to determine coefficients for each subject. The coefficients provide a digital biomarker of glycemic control for the subject and may be used to identify subjects with glycemic dysfunction.

A method for generating treatment regimen for one or more health conditions includes retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs. Each of the treatment programs includes a respective treatment user interface to modify a respective behavior associated with one or more neurohumoral factors that are associated with the respective health condition. In response to receiving input that specifies a first health condition of the one or more health conditions, the method uses the healthcare treatment model to select one or more treatment programs corresponding to the first health condition and provides the treatment user interfaces for the one or more treatment programs.

A method for generating treatment regimen for one or more health conditions includes retrieving a stored healthcare treatment model that has been trained to identify, for each of a plurality of health conditions, one or more respective treatment programs. Each of the treatment programs includes a respective treatment user interface to modify a respective behavior associated with one or more neurohumoral factors that are associated with the respective health condition. In response to receiving input that specifies a first health condition of the one or more health conditions, the method uses the healthcare treatment model to select one or more treatment programs corresponding to the first health condition and provides the treatment user interfaces for the one or more treatment programs.

The present disclosure relates to a portable uroflowmetry apparatus, and uroflowmetry, voiding diary writing system and uroflowmetry method using the same.

Embodiments of the invention relate to a computer-implemented method for quantifying a biomarker in a tissue sample of an organism. An image analysis system receives images of a stained tissue sample. Each received digital image depicts the tissue sample region at the end of an exposure interval. The system analyzes the intensity values and exposure intervals of the received digital images for determining the time when the intensity values corresponding to the plurality of exposure intervals ordered according to ascending exposure interval lengths reach a plateau (saturation residence time—SRT). The system determines the amount of a biomarker in the tissue sample and/or predicts a tumor stage and/or a treatment recommendation as a function of the SRT.

Techniques for evaluating dynamically modified plans are provided. A selection of a treatment plan template is received, where the treatment plan template specifies a plurality of treatment stages, where each treatment stage defines a plurality of treatment options. A plurality of modifications to the treatment plan template is generated. It is determined, for each respective modification of the plurality of modifications, whether the respective modification is permissible, based on one or more predefined institutional criteria. Upon determining that a first modification of the plurality of modifications is permissible, a first treatment plan is generated based on the first modification to the treatment plan template. Further, a first predicted efficacy measure is generated for the first treatment plan based on analyzing a knowledge graph. Finally, the first treatment plan is provided, along with at least an indication of the first predicted efficacy measure.