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公开(公告)号：US20200227147A1
公开(公告)日：2020-07-16
申请号：US16738549
申请日：2020-01-09
摘要： A computer implemented method includes receiving text-based clinical documentation corresponding to a patient treated at a healthcare facility, converting the text-based clinical documentation to create a machine compatible converted input having multiple features, providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted clinical documentation by the first entity, and receiving a prediction from the trained machine learning model, wherein the prediction corresponds to at least one of a predicted diagnostic related group (DRG) code or a set of predictions comprising a predicted principal diagnosis code for provision to a DRG calculator to determine the DRG code.
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公开(公告)号：US20240277449A1
公开(公告)日：2024-08-22
申请号：US18292217
申请日：2022-08-08
发明人： Benjamin D. Zimmer , Cody J. Olson , Nicholas A. Stark , Nicholas J. Raddatz , Alexandra R. Cunliffe , Guruprasad Somasundaram
IPC分类号： A61C7/00 , A61C7/08 , G06N3/0475
CPC分类号： A61C7/002 , A61C7/08 , G06N3/0475
摘要： Methods for generating intermediate stages for orthodontic aligners using machine learning or deep learning techniques. The method receives a malocclusion of teeth and a planned setup position of the teeth. The malocclusion can be represented by translations and rotations, or by digital 3D models. The method generates intermediate stages for aligners, between the malocclusion and the planned setup position, using one or more deep learning methods. The intermediate stages can be used to generate setups that are output in a format, such as digital 3D models, suitable for use in manufacturing the corresponding aligners.
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公开(公告)号：US20200227175A1
公开(公告)日：2020-07-16
申请号：US16738442
申请日：2020-01-09
发明人： Julie A. Salomon , Julie L. Imburgia , Jeffrey S. Seese , Nicholas J. Raddatz , Dominick R. Rocco
IPC分类号： G16H50/70 , G16H10/60 , G16H50/20 , G16H70/20 , G16H70/60 , G06F40/253 , G06F40/284 , G06F40/295
摘要： A computer implemented method includes receiving text-based clinical documentation corresponding to a patient treated at a healthcare facility, converting the text-based clinical documentation to create a machine compatible converted input having multiple features, providing the converted input to a trained machine learning model that has been trained based on a training set of historical converted clinical documentation by the first entity, receiving a prediction from the trained machine learning model, wherein the prediction corresponds to at least one of a predicted diagnostic related group (DRG) code or a set of predictions comprising a predicted principal diagnosis code for provision to a DRG calculator to determine the DRG code, and assign a priority score at least partially based on the prediction.
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