A method comprising receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.

A method for autonomously generating a care plan personalized for a patient is disclosed. The method includes receiving a selection of a type of the care plan to implement for the patient, generating the care plan based on the type selected, wherein the care plan includes an action instruction based on a patient graph of the patient and a knowledge graph including ontological medical data, receiving patient data that indicates health related information associated with the patient, modifying the care plan to generate a modified care plan in real-time or near real-time based on the patient data, and causing the modified care plan to be presented on a computing device of a medical personnel.

A method for autonomously generating a care plan personalized for a patient is disclosed. The method includes receiving a selection of a type of the care plan to implement for the patient, generating the care plan based on the type selected, wherein the care plan includes an action instruction based on a patient graph of the patient and a knowledge graph including ontological medical data, receiving patient data that indicates health related information associated with the patient, modifying the care plan to generate a modified care plan in real-time or near real-time based on the patient data, and causing the modified care plan to be presented on a computing device of a medical personnel.

A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.

A processing unit includes a contribution-degree calculation unit configured to calculate contribution degrees that indicate respective degrees by which a plurality of attribute items included as predetermined attributes of one target contribute to a predicted intervention effect, the calculating being performed on a basis of an estimation model for estimating the predicted intervention effect from the predetermined attributes of the one target. The predicted intervention effect on the one target is a numerical value corresponding to an increase in gain to a beneficiary, the gain being expected to be larger in a case where an intervention is implemented to the one target than in a case where the intervention is unimplemented to the one target.

A system for determining a treatment regimen for chronic pelvic pain (CPP) includes an electromyography (EMG) probe including electrodes. Each electrode detects pelvic floor muscle activity. An EMG sensor array includes bipolar EMG sensors configured to detect muscle activity of muscles associated with or supporting the pelvic floor muscles. An EMG amplifier is in communication with the EMG probe or the EMG sensor array. The EMG amplifier includes a plurality of input channels. Each input channel receives data of muscle activity in the pelvic floor muscles or the muscles associated with or supporting the pelvic floor muscles from the EMG probe or the EMG sensor array. A processor of a computer performs muscle network analysis using the data of muscle activity in the pelvic floor muscles or the muscles associated with or supporting the pelvic floor muscles. A treatment regimen for CPP is recommended based on the muscle network analysis.

Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

A method includes generating an ablation programming language, which defines commands for (i) setting ablation protocol parameters and respective values, (ii) setting a configuration of an ablation system, (iii) applying automatic logic that relates the ablation protocol parameters and the values to the configuration of the ablation system, and (iv) generating one or more graphical user interfaces (GUIs) showing one or more of the parameters of the ablation protocol and the system configuration. The ablation programming language is provided for subsequent use with the ablation system.

A method includes generating an ablation programming language, which defines commands for (i) setting ablation protocol parameters and respective values, (ii) setting a configuration of an ablation system, (iii) applying automatic logic that relates the ablation protocol parameters and the values to the configuration of the ablation system, and (iv) generating one or more graphical user interfaces (GUIs) showing one or more of the parameters of the ablation protocol and the system configuration. The ablation programming language is provided for subsequent use with the ablation system.

This disclosure is directed to systems and techniques for detecting change in patient health and if a change in patient health is detected, direct a medical device to generate for display output indicating the detection of the change in patient health. An example medical system or technique applies a model to values of configurable settings that are programmed into detection logic of a medical device; based on the application, determine whether modified values of the configurable settings, when implemented by the detection logic, would change a determination, by the medical device, regarding whether sensed physiological activity is indicative of cardiac episode for a patient; and in response to a determination that the modified values would change the determination regarding whether the sensed physiological activity is indicative of the cardiac episode for the patient, generate output data indicative of the modified values for the configurable settings for the medical device.