Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for the disease states based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models .

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models.

Abstract: Implementations set forth herein relate to a peer-to-peer search system for patient medical records for leveraging benefits of identifying similar patient cases in a secured singular network. The peer-to-peer search system can use a distributed ledger to securely correlate similar instances of medical data, located in various other systems, to a globally accessible network that is available via the peer-to-peer search system. For instance, medical data from various sources can be hashed by a hash technique, such as locality-sensitive hashing, and stored in a hash database. When the hash database is queried via the peer-to-peer search system, hash data corresponding to query results can be provided and, optionally, ranked according to similarities between a hashing of input query to a hashing of documents embodying the query results.

Abstract: A non-transitory computer-readable medium stores a preferences database (16); instructions readable and executable by at least one electronic processor (20) to perform a proposed radiation treatment plan review process (100), including: via a reviewing graphical user interface (GUI) (28), presenting a proposed radiation treatment plan to a reviewer; via the reviewing GUI, receiving one of (i) an acceptance of the proposed radiation treatment plan or (ii) a rejection of the proposed radiation treatment plan in combination with annotations of the rejected proposed radiation treatment plan from the reviewer; and updating radiation treatment plan preferences of the reviewer stored in the preferences database based on the acceptance of the proposed radiation treatment plan or based on the annotations of the rejected proposed radiation treatment plan; and instructions readable and executable by at least one electronic processor (32) to perform a radiation treatment planning process (200) including: optimizing radia

Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models .

Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.

Abstract: A method, system, and computer readable medium which automatically determine the side, field and a level of image quality of fundus images of the retina of a human eye is disclosed. The disclosure combines image processing, computer vision and pattern recognition techniques in a unique way to provide a robust process to identify and grade the quality of fundus images with application to improve efficiency and reduce errors in clinical or diagnostic retinal imaging workflows.

Abstract: A method, system, and computer readable medium which automatically determine the side, field and a level of image quality of fundus images of the retina of a human eye is disclosed. The disclosure combines image processing, computer vision and pattern recognition techniques in a unique way to provide a robust process to identify and grade the quality of fundus images with application to improve efficiency and reduce errors in clinical or diagnostic retinal imaging workflows.