DEEP LEARNING FOR MODELING DISEASE PROGRESSION

DEEP LEARNING FOR MODELING DISEASE PROGRESSION

A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: HASHEMIFAR, Somaye Sadat, HEJRATI, Seyed Mohammadmohsen, IRIONDO, Claudia
Format: Patent
Sprache:eng
Schlagworte:
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator HASHEMIFAR, Somaye Sadat
HEJRATI, Seyed Mohammadmohsen
IRIONDO, Claudia
description A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machine learning model, a second feature representation based on an image of a brain of the patient. The method may also include generating, by the machine learning model, a set representation by at least fusing the first feature representation and the second feature representation. The method may also include predicting, by the machine learning model, a change in the baseline cognitive state over a time period based at least on the set representation. Related systems and articles of manufacture are also disclosed.
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2024312639A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2024312639A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2024312639A13</originalsourceid><addsrcrecordid>eNrjZNBzcXUNUPBxdQzy8_RzV3DzD1Lw9Xdx9QFxXDyDXR2DXRUCgvzdg1yDgz39_XgYWNMSc4pTeaE0N4Oym2uIs4duakF-fGpxQWJyal5qSXxosJGBkYmxoZGZsaWjoTFxqgBw8Sam</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>DEEP LEARNING FOR MODELING DISEASE PROGRESSION</title><source>esp@cenet</source><creator>HASHEMIFAR, Somaye Sadat ; HEJRATI, Seyed Mohammadmohsen ; IRIONDO, Claudia</creator><creatorcontrib>HASHEMIFAR, Somaye Sadat ; HEJRATI, Seyed Mohammadmohsen ; IRIONDO, Claudia</creatorcontrib><description>A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machine learning model, a second feature representation based on an image of a brain of the patient. The method may also include generating, by the machine learning model, a set representation by at least fusing the first feature representation and the second feature representation. The method may also include predicting, by the machine learning model, a change in the baseline cognitive state over a time period based at least on the set representation. Related systems and articles of manufacture are also disclosed.</description><language>eng</language><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240919&amp;DB=EPODOC&amp;CC=US&amp;NR=2024312639A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240919&amp;DB=EPODOC&amp;CC=US&amp;NR=2024312639A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HASHEMIFAR, Somaye Sadat</creatorcontrib><creatorcontrib>HEJRATI, Seyed Mohammadmohsen</creatorcontrib><creatorcontrib>IRIONDO, Claudia</creatorcontrib><title>DEEP LEARNING FOR MODELING DISEASE PROGRESSION</title><description>A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machine learning model, a second feature representation based on an image of a brain of the patient. The method may also include generating, by the machine learning model, a set representation by at least fusing the first feature representation and the second feature representation. The method may also include predicting, by the machine learning model, a change in the baseline cognitive state over a time period based at least on the set representation. Related systems and articles of manufacture are also disclosed.</description><subject>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNBzcXUNUPBxdQzy8_RzV3DzD1Lw9Xdx9QFxXDyDXR2DXRUCgvzdg1yDgz39_XgYWNMSc4pTeaE0N4Oym2uIs4duakF-fGpxQWJyal5qSXxosJGBkYmxoZGZsaWjoTFxqgBw8Sam</recordid><startdate>20240919</startdate><enddate>20240919</enddate><creator>HASHEMIFAR, Somaye Sadat</creator><creator>HEJRATI, Seyed Mohammadmohsen</creator><creator>IRIONDO, Claudia</creator><scope>EVB</scope></search><sort><creationdate>20240919</creationdate><title>DEEP LEARNING FOR MODELING DISEASE PROGRESSION</title><author>HASHEMIFAR, Somaye Sadat ; HEJRATI, Seyed Mohammadmohsen ; IRIONDO, Claudia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2024312639A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>HASHEMIFAR, Somaye Sadat</creatorcontrib><creatorcontrib>HEJRATI, Seyed Mohammadmohsen</creatorcontrib><creatorcontrib>IRIONDO, Claudia</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HASHEMIFAR, Somaye Sadat</au><au>HEJRATI, Seyed Mohammadmohsen</au><au>IRIONDO, Claudia</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>DEEP LEARNING FOR MODELING DISEASE PROGRESSION</title><date>2024-09-19</date><risdate>2024</risdate><abstract>A method is provided for deep learning for modeling disease progression. The method may include generating, by a machine learning model, a first feature representation based on clinical data associated with a baseline cognitive state of a patient. The method may also include generating, by the machine learning model, a second feature representation based on an image of a brain of the patient. The method may also include generating, by the machine learning model, a set representation by at least fusing the first feature representation and the second feature representation. The method may also include predicting, by the machine learning model, a change in the baseline cognitive state over a time period based at least on the set representation. Related systems and articles of manufacture are also disclosed.</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language eng
recordid cdi_epo_espacenet_US2024312639A1
source esp@cenet
subjects HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
title DEEP LEARNING FOR MODELING DISEASE PROGRESSION
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T11%3A24%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=HASHEMIFAR,%20Somaye%20Sadat&rft.date=2024-09-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2024312639A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true