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Analysis

1 / 954
1.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

2.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

3.20230320642SYSTEMS AND METHODS FOR TECHNIQUES TO PROCESS, ANALYZE AND MODEL INTERACTIVE VERBAL DATA FOR MULTIPLE INDIVIDUALS
US - 12.10.2023
Int.Class A61B 5/16
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
16Devices for psychotechnics; Testing reaction times
 Appl.No 18130947 Applicant The Trustees of Columbia University in the City of New York Inventor Baihan Lin

Disclosed are methods, systems, and other implementations for processing, analyzing, and modelling psychotherapy data. The implementations include a method for analyzing psychotherapy data that includes obtaining transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting speech segments from the transcript data related to one or more of the patient or the therapist, applying a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing the weighted topic labels to derive a psychiatric assessment for the patient.

4.20210169349Tumor characterization and outcome prediction through quantitative measurements of tumor-associated vasculature
US - 10.06.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 17116319 Applicant Case Western Reserve University Inventor Anant Madabhushi

Embodiments discussed herein facilitate determination of a response to treatment and/or a prognosis for a tumor based at least in part on features of tumor-associated vasculature (TAV). One example embodiment is a method, comprising: accessing a medical imaging scan of a tumor, wherein the tumor is segmented on the medical imaging scan; segmenting tumor-associated vasculature (TAV) associated with the tumor based on the medical imaging scan; extracting one or more features from the TAV; providing the one or more features extracted from the TAV to a trained machine learning model; and receiving, from the machine learning model, one of a predicted response to a treatment for the tumor or a prognosis for the tumor.

5.20220202373SYSTEMS AND METHODS OF USING MACHINE LEARNING TO DETECT AND PREDICT EMERGENCE OF AGITATION BASED ON SYMPATHETIC NERVOUS SYSTEM ACTIVITIES
US - 30.06.2022
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No 17698407 Applicant BioXcel Therapeutics, Inc. Inventor Frank D. YOCCA

In some embodiments, a method includes receiving first physiological data of sympathetic nervous system activity and establishing a baseline value of at least one physiological parameter by training at least one machine learning model using the first physiological data. The method further includes receiving, from a first monitoring device attached to a subject, second physiological data of sympathetic nervous system activity in the subject. Using the at least one machine learning model and based on the baseline value of at least one physiological parameter, the method includes analyzing the second physiological data to predict an agitation episode of the subject and sending a signal to a second monitoring device to notify of the prediction of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

6.20200227168MACHINE LEARNING IN FUNCTIONAL CANCER ASSAYS
US - 16.07.2020
Int.Class G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
 Appl.No 16739814 Applicant Travera LLC Inventor Rob Kimmerling

The invention provides methods that use machine learning to discover clinical data patterns that are predictive of disease, such as cancer. Clinical data from across a population is provided as input to a machine learning system. The machine learning system discovers associations in data from a plurality of data sources obtained from a population and correlates the associations to cancer status of patients in the population. The methods may further include providing patient data from an individual and predicting, by the machine learning system, a cancer state (e.g., the presence of cancer and a determination of a stage or progression of the cancer, if present) for the individual when the patient data presents one or more of the discovered associations.

7.11900244Attention-based deep reinforcement learning for autonomous agents
US - 13.02.2024
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16588789 Applicant Amazon Technologies, Inc. Inventor Sahika Genc

A data source configured to provide a representation of an environment of one or more agents is identified. Using a data set obtained from the data source, a neural network-based reinforcement learning model with one or more attention layers is trained. Importance indicators generated by the attention layers are used to identify actions to be initiated by an agent. A trained version of the model is stored.

8.WO/2024/035630METHOD AND SYSTEM TO DETERMINE NEED FOR HOSPITAL ADMISSION AFTER ELECTIVE SURGICAL PROCEDURES
WO - 15.02.2024
Int.Class G16H 50/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
30for calculating health indices; for individual health risk assessment
 Appl.No PCT/US2023/029610 Applicant NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL SURGERY Inventor SHEN, Tony S.
A computer-implemented method includes: accessing electronic healthcare records of a group of patients, wherein each patient has received an elective surgical procedure; extracting a data structure encoding a plurality of features of each patient from the group of patients, wherein a subset of the group of patients undergo at least one hospital-based intervention after receiving the elective surgical procedure; determining, using a machine learning algorithm that operates on the data structure, a Shapley value for each of the features, wherein the Shapley value indicates a likelihood for each patient with a corresponding feature to receive at least one hospital-based intervention; identifying a subset of the plurality of features; and based on the identified subset of features, establishing a predictive tool to predict a combined likelihood for a patient to receive a hospital-based intervention.
9.20240057874TUMOR CHARACTERIZATION AND OUTCOME PREDICTION THROUGH QUANTITATIVE MEASUREMENTS OF TUMOR-ASSOCIATED VASCULATURE
US - 22.02.2024
Int.Class A61B 5/02
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
02Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
 Appl.No 18495821 Applicant Case Western Reserve University Inventor Anant Madabhushi

The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and/or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.

10.WO/2024/137377SKILL LEARNING FOR DYNAMIC TREATMENT REGIMES
WO - 27.06.2024
Int.Class G16H 20/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
 Appl.No PCT/US2023/084239 Applicant NEC LABORATORIES AMERICA, INC. Inventor YU, Wenchao
Methods and systems for training a healthcare treatment machine learning model include segmenting (304) a patient trajectory, which includes a sequence of patient states and treatment actions. A machine learning model is trained (308) based on segments of the patient trajectory, including a prototype layer that learns prototype vectors representing respective classes of trajectory segments and an imitation learning layer that learns a policy to select a treatment action based on an input state and a skill embedding.
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