A method for ranking an unmeasured feature for an instance given at least one feature is measured is provided. The method includes imputing a first value to the unmeasured feature in the instance while holding the other remaining unmeasured features constant and evaluating a first outcome with a model using the first value in the instance. The method includes imputing a second value to the unmeasured feature in the dataset while holding the other remaining unmeasured features constant, evaluating a second outcome with the model using the second value in the instance, and determining a statistical parameter with the first outcome and the second outcome. The method also includes assigning the unmeasured feature a ranking corresponding to the determined statistical parameter. A system and a non-transitory, computer readable medium storing instructions to perform the above method are also presented.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

A support device for an endoscope comprises a tubular member configured for removable attachment to an outer surface of the endoscope near, or at, its distal end and a plurality of projecting elements extending outward from the outer surface of the tubular member and circumferentially spaced from each other. The device includes an optically transparent cover coupled to the tubular member and configured for covering the distal end of the endoscope when the tubular member is attached to the outer surface of the endoscope. The projecting elements provide support for the endoscope, improve visualization and center the scope as it passes through a body lumen, such as the colon. In addition, the cover seals the distal end of the endoscope to protect the scope and its components from debris, fluid, pathogens and other biomatter.

Provided are computer-implemented methods and systems for generating and/or utilizing data analysis algorithm(s) for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.

A method for supplementing scan data by using library data according to the present invention comprises: a scan step of acquiring scan data of a subject including a structure; a step of selecting library model data corresponding to the structure; and a step of post-processing the scan data, wherein the library model data is added at the step of post-processing of the scan data and is post-processed together with the scan data. Accordingly, the scan data in which distortion or a data gap has occurred is supplemented by the library model data, and thus a user acquires a highly reliable three-dimensional model.

A brain stimulation simulation system and method according to a preset guide system using an anonymized data-based external server are provided. According to various embodiments of the present invention, provided is a brain stimulation simulation method according to a preset guide system using an external server, the method performed by a computing device, the method including: a first server generating a global matrix for performing brain stimulation simulation on a plurality of objects by using a plurality of brain models for each of the plurality of objects; and a second server being provided with the generated global matrix from the first server and performing the brain stimulation simulation on the plurality of objects by using the provided global matrix.

A method for providing a therapy plan includes receiving a dataset. The dataset maps an examination object including a first tissue area at at least one first timepoint. The first tissue area is identified in the dataset as a first state. A second state is specified for the first tissue area. A therapy plan for a therapy apparatus is determined by application of a model to input data. A change in the first tissue area between the at least one first timepoint and at least one second timepoint is createable by the control of the therapy apparatus in accordance with the therapy plan. The model maps the first state to the second state and/or the second state to the first state. The therapy plan is provided as output data of the model.

A method for providing a therapy plan includes receiving a dataset. The dataset maps an examination object including a first tissue area at at least one first timepoint. The first tissue area is identified in the dataset as a first state. A second state is specified for the first tissue area. A therapy plan for a therapy apparatus is determined by application of a model to input data. A change in the first tissue area between the at least one first timepoint and at least one second timepoint is createable by the control of the therapy apparatus in accordance with the therapy plan. The model maps the first state to the second state and/or the second state to the first state. The therapy plan is provided as output data of the model.

A predictive patient health machine learning model is trained based on baseline health data configured as directed graphs. Patient-healthcare system encounter data formed at least in part by electronic medical records (EMRs) is gathered. The patient-healthcare system encounter data is configured as directed graphs to generate graphed health data and the predictive patient health machine learning model is trained on that graphed health data.