BiopestPeptidV1, Main, Exploration, bibRecord, 000298

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

Identifieur interne : 000298 ( Main/Exploration ); précédent : 000297; suivant : 000299

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

Auteurs : Long Vu [États-Unis] ; Sarah Kefayati [États-Unis] ; Tsuyoshi Idé [États-Unis] ; Venkata Pavuluri [États-Unis] ; Gretchen Jackson [États-Unis] ; Lisa Latts [États-Unis] ; Yuxiang Zhong [États-Unis] ; Pratik Agrawal [États-Unis] ; Yuan-Chi Chang [États-Unis]

Source :

AMIA ... Annual Symposium proceedings. AMIA Symposium [ 1942-597X ] ; 2019.

RBID : pubmed:32308884

Descripteurs français

KwdFr :
- Aire sous la courbe (MeSH), Apprentissage machine (MeSH), Autosurveillance glycémique (MeSH), Courbe ROC (MeSH), Diabète de type 1 (sang), Diabète de type 1 (traitement médicamenteux), Glycémie (MeSH), Humains (MeSH), Hypoglycémiants (effets indésirables), Hypoglycémiants (usage thérapeutique), Hypoglycémie (diagnostic), Hypoglycémie (induit chimiquement), Hypoglycémie (prévention et contrôle), Insuline (effets indésirables), Insuline (usage thérapeutique), Modèles biologiques (MeSH), Surveillance électronique ambulatoire (MeSH).
MESH :
- diagnostic : Hypoglycémie.
- effets indésirables : Hypoglycémiants, Insuline.
- induit chimiquement : Hypoglycémie.
- prévention et contrôle : Hypoglycémie.
- sang : Diabète de type 1.
- traitement médicamenteux : Diabète de type 1.
- usage thérapeutique : Hypoglycémiants, Insuline.
- Aire sous la courbe, Apprentissage machine, Autosurveillance glycémique, Courbe ROC, Glycémie, Humains, Modèles biologiques, Surveillance électronique ambulatoire.

English descriptors

KwdEn :
- Area Under Curve (MeSH), Blood Glucose (MeSH), Blood Glucose Self-Monitoring (MeSH), Diabetes Mellitus, Type 1 (blood), Diabetes Mellitus, Type 1 (drug therapy), Humans (MeSH), Hypoglycemia (chemically induced), Hypoglycemia (diagnosis), Hypoglycemia (prevention & control), Hypoglycemic Agents (adverse effects), Hypoglycemic Agents (therapeutic use), Insulin (adverse effects), Insulin (therapeutic use), Machine Learning (MeSH), Models, Biological (MeSH), Monitoring, Ambulatory (MeSH), ROC Curve (MeSH).
MESH :
- chemical , adverse effects : Hypoglycemic Agents, Insulin.
- chemical , therapeutic use : Hypoglycemic Agents, Insulin.
- chemical : Blood Glucose.
- blood : Diabetes Mellitus, Type 1.
- chemically induced : Hypoglycemia.
- diagnosis : Hypoglycemia.
- drug therapy : Diabetes Mellitus, Type 1.
- prevention & control : Hypoglycemia.
- Area Under Curve, Blood Glucose Self-Monitoring, Humans, Machine Learning, Models, Biological, Monitoring, Ambulatory, ROC Curve.

Abstract

Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been limited to a short prediction horizon (~ 30 minutes). To this end, a nocturnal hypoglycemia prediction model with a 6-hour horizon (midnight-6 am) was developed using a random forest machine- learning model based on data from 10,000 users with more than 1 million nights of CGM data. The model demonstrated an overall nighttime hypoglycemia prediction performance of ROC AUC = 0.84, with AUC = 0.90 for early night (midnight-3 am) and AUC = 0.75 for late night (prediction at midnight, looking at 3-6 am window). While instabilities and the absence of late-night blood glucose patterns introduce predictability challenges, this 6-hour horizon model demonstrates good performance in predicting nocturnal hypoglycemia. Additional study and specific patient-specific features will provide refinements that further ensure safe overnight management of glycemia.

PubMed: 32308884
PubMed Central: PMC7153099

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000060
to stream Main, to step Curation: 000060

Le document en format XML

<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.</title>
<author><name sortKey="Vu, Long" sort="Vu, Long" uniqKey="Vu L" first="Long" last="Vu">Long Vu</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Kefayati, Sarah" sort="Kefayati, Sarah" uniqKey="Kefayati S" first="Sarah" last="Kefayati">Sarah Kefayati</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Ide, Tsuyoshi" sort="Ide, Tsuyoshi" uniqKey="Ide T" first="Tsuyoshi" last="Idé">Tsuyoshi Idé</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Pavuluri, Venkata" sort="Pavuluri, Venkata" uniqKey="Pavuluri V" first="Venkata" last="Pavuluri">Venkata Pavuluri</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Jackson, Gretchen" sort="Jackson, Gretchen" uniqKey="Jackson G" first="Gretchen" last="Jackson">Gretchen Jackson</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Latts, Lisa" sort="Latts, Lisa" uniqKey="Latts L" first="Lisa" last="Latts">Lisa Latts</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Zhong, Yuxiang" sort="Zhong, Yuxiang" uniqKey="Zhong Y" first="Yuxiang" last="Zhong">Yuxiang Zhong</name>
<affiliation wicri:level="2"><nlm:affiliation>Medtronic, Northridge, CA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Medtronic, Northridge, CA</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Agrawal, Pratik" sort="Agrawal, Pratik" uniqKey="Agrawal P" first="Pratik" last="Agrawal">Pratik Agrawal</name>
<affiliation wicri:level="2"><nlm:affiliation>Medtronic, Northridge, CA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Medtronic, Northridge, CA</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Chang, Yuan Chi" sort="Chang, Yuan Chi" uniqKey="Chang Y" first="Yuan-Chi" last="Chang">Yuan-Chi Chang</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">PubMed</idno>
<date when="2019">2019</date>
<idno type="RBID">pubmed:32308884</idno>
<idno type="pmid">32308884</idno>
<idno type="pmc">PMC7153099</idno>
<idno type="wicri:Area/Main/Corpus">000060</idno>
<idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000060</idno>
<idno type="wicri:Area/Main/Curation">000060</idno>
<idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000060</idno>
<idno type="wicri:Area/Main/Exploration">000060</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.</title>
<author><name sortKey="Vu, Long" sort="Vu, Long" uniqKey="Vu L" first="Long" last="Vu">Long Vu</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Kefayati, Sarah" sort="Kefayati, Sarah" uniqKey="Kefayati S" first="Sarah" last="Kefayati">Sarah Kefayati</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Ide, Tsuyoshi" sort="Ide, Tsuyoshi" uniqKey="Ide T" first="Tsuyoshi" last="Idé">Tsuyoshi Idé</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Pavuluri, Venkata" sort="Pavuluri, Venkata" uniqKey="Pavuluri V" first="Venkata" last="Pavuluri">Venkata Pavuluri</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Jackson, Gretchen" sort="Jackson, Gretchen" uniqKey="Jackson G" first="Gretchen" last="Jackson">Gretchen Jackson</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Latts, Lisa" sort="Latts, Lisa" uniqKey="Latts L" first="Lisa" last="Latts">Lisa Latts</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Watson Health, Cambridge, MA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Watson Health, Cambridge, MA</wicri:regionArea>
<placeName><region type="state">Massachusetts</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Zhong, Yuxiang" sort="Zhong, Yuxiang" uniqKey="Zhong Y" first="Yuxiang" last="Zhong">Yuxiang Zhong</name>
<affiliation wicri:level="2"><nlm:affiliation>Medtronic, Northridge, CA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Medtronic, Northridge, CA</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Agrawal, Pratik" sort="Agrawal, Pratik" uniqKey="Agrawal P" first="Pratik" last="Agrawal">Pratik Agrawal</name>
<affiliation wicri:level="2"><nlm:affiliation>Medtronic, Northridge, CA, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>Medtronic, Northridge, CA</wicri:regionArea>
<placeName><region type="state">Californie</region>
</placeName>
</affiliation>
</author>
<author><name sortKey="Chang, Yuan Chi" sort="Chang, Yuan Chi" uniqKey="Chang Y" first="Yuan-Chi" last="Chang">Yuan-Chi Chang</name>
<affiliation wicri:level="2"><nlm:affiliation>IBM Research AI, Yorktown Heights, NY, USA.</nlm:affiliation>
<country xml:lang="fr">États-Unis</country>
<wicri:regionArea>IBM Research AI, Yorktown Heights, NY</wicri:regionArea>
<placeName><region type="state">État de New York</region>
</placeName>
</affiliation>
</author>
</analytic>
<series><title level="j">AMIA ... Annual Symposium proceedings. AMIA Symposium</title>
<idno type="eISSN">1942-597X</idno>
<imprint><date when="2019" type="published">2019</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Area Under Curve (MeSH)</term>
<term>Blood Glucose (MeSH)</term>
<term>Blood Glucose Self-Monitoring (MeSH)</term>
<term>Diabetes Mellitus, Type 1 (blood)</term>
<term>Diabetes Mellitus, Type 1 (drug therapy)</term>
<term>Humans (MeSH)</term>
<term>Hypoglycemia (chemically induced)</term>
<term>Hypoglycemia (diagnosis)</term>
<term>Hypoglycemia (prevention & control)</term>
<term>Hypoglycemic Agents (adverse effects)</term>
<term>Hypoglycemic Agents (therapeutic use)</term>
<term>Insulin (adverse effects)</term>
<term>Insulin (therapeutic use)</term>
<term>Machine Learning (MeSH)</term>
<term>Models, Biological (MeSH)</term>
<term>Monitoring, Ambulatory (MeSH)</term>
<term>ROC Curve (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Aire sous la courbe (MeSH)</term>
<term>Apprentissage machine (MeSH)</term>
<term>Autosurveillance glycémique (MeSH)</term>
<term>Courbe ROC (MeSH)</term>
<term>Diabète de type 1 (sang)</term>
<term>Diabète de type 1 (traitement médicamenteux)</term>
<term>Glycémie (MeSH)</term>
<term>Humains (MeSH)</term>
<term>Hypoglycémiants (effets indésirables)</term>
<term>Hypoglycémiants (usage thérapeutique)</term>
<term>Hypoglycémie (diagnostic)</term>
<term>Hypoglycémie (induit chimiquement)</term>
<term>Hypoglycémie (prévention et contrôle)</term>
<term>Insuline (effets indésirables)</term>
<term>Insuline (usage thérapeutique)</term>
<term>Modèles biologiques (MeSH)</term>
<term>Surveillance électronique ambulatoire (MeSH)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="adverse effects" xml:lang="en"><term>Hypoglycemic Agents</term>
<term>Insulin</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Hypoglycemic Agents</term>
<term>Insulin</term>
</keywords>
<keywords scheme="MESH" type="chemical" xml:lang="en"><term>Blood Glucose</term>
</keywords>
<keywords scheme="MESH" qualifier="blood" xml:lang="en"><term>Diabetes Mellitus, Type 1</term>
</keywords>
<keywords scheme="MESH" qualifier="chemically induced" xml:lang="en"><term>Hypoglycemia</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Hypoglycemia</term>
</keywords>
<keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Hypoglycémie</term>
</keywords>
<keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Diabetes Mellitus, Type 1</term>
</keywords>
<keywords scheme="MESH" qualifier="effets indésirables" xml:lang="fr"><term>Hypoglycémiants</term>
<term>Insuline</term>
</keywords>
<keywords scheme="MESH" qualifier="induit chimiquement" xml:lang="fr"><term>Hypoglycémie</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Hypoglycemia</term>
</keywords>
<keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Hypoglycémie</term>
</keywords>
<keywords scheme="MESH" qualifier="sang" xml:lang="fr"><term>Diabète de type 1</term>
</keywords>
<keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Diabète de type 1</term>
</keywords>
<keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Hypoglycémiants</term>
<term>Insuline</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Area Under Curve</term>
<term>Blood Glucose Self-Monitoring</term>
<term>Humans</term>
<term>Machine Learning</term>
<term>Models, Biological</term>
<term>Monitoring, Ambulatory</term>
<term>ROC Curve</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Aire sous la courbe</term>
<term>Apprentissage machine</term>
<term>Autosurveillance glycémique</term>
<term>Courbe ROC</term>
<term>Glycémie</term>
<term>Humains</term>
<term>Modèles biologiques</term>
<term>Surveillance électronique ambulatoire</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been limited to a short prediction horizon (~ 30 minutes). To this end, a nocturnal hypoglycemia prediction model with a 6-hour horizon (midnight-6 am) was developed using a random forest machine- learning model based on data from 10,000 users with more than 1 million nights of CGM data. The model demonstrated an overall nighttime hypoglycemia prediction performance of ROC AUC = 0.84, with AUC = 0.90 for early night (midnight-3 am) and AUC = 0.75 for late night (prediction at midnight, looking at 3-6 am window). While instabilities and the absence of late-night blood glucose patterns introduce predictability challenges, this 6-hour horizon model demonstrates good performance in predicting nocturnal hypoglycemia. Additional study and specific patient-specific features will provide refinements that further ensure safe overnight management of glycemia.</div>
</front>
</TEI>
<pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">32308884</PMID>
<DateCompleted><Year>2020</Year>
<Month>09</Month>
<Day>14</Day>
</DateCompleted>
<DateRevised><Year>2020</Year>
<Month>09</Month>
<Day>14</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1942-597X</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>2019</Volume>
<PubDate><Year>2019</Year>
</PubDate>
</JournalIssue>
<Title>AMIA ... Annual Symposium proceedings. AMIA Symposium</Title>
<ISOAbbreviation>AMIA Annu Symp Proc</ISOAbbreviation>
</Journal>
<ArticleTitle>Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.</ArticleTitle>
<Pagination><MedlinePgn>874-882</MedlinePgn>
</Pagination>
<Abstract><AbstractText>Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, which commonly goes undetected. Continuous glucose monitoring (CGM) devices have enabled prediction of impending nocturnal hypoglycemia, however, prior efforts have been limited to a short prediction horizon (~ 30 minutes). To this end, a nocturnal hypoglycemia prediction model with a 6-hour horizon (midnight-6 am) was developed using a random forest machine- learning model based on data from 10,000 users with more than 1 million nights of CGM data. The model demonstrated an overall nighttime hypoglycemia prediction performance of ROC AUC = 0.84, with AUC = 0.90 for early night (midnight-3 am) and AUC = 0.75 for late night (prediction at midnight, looking at 3-6 am window). While instabilities and the absence of late-night blood glucose patterns introduce predictability challenges, this 6-hour horizon model demonstrates good performance in predicting nocturnal hypoglycemia. Additional study and specific patient-specific features will provide refinements that further ensure safe overnight management of glycemia.</AbstractText>
<CopyrightInformation>©2019 AMIA - All rights reserved.</CopyrightInformation>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Vu</LastName>
<ForeName>Long</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>IBM Research AI, Yorktown Heights, NY, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Kefayati</LastName>
<ForeName>Sarah</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>IBM Watson Health, Cambridge, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Idé</LastName>
<ForeName>Tsuyoshi</ForeName>
<Initials>T</Initials>
<AffiliationInfo><Affiliation>IBM Research AI, Yorktown Heights, NY, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pavuluri</LastName>
<ForeName>Venkata</ForeName>
<Initials>V</Initials>
<AffiliationInfo><Affiliation>IBM Research AI, Yorktown Heights, NY, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Jackson</LastName>
<ForeName>Gretchen</ForeName>
<Initials>G</Initials>
<AffiliationInfo><Affiliation>IBM Watson Health, Cambridge, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Latts</LastName>
<ForeName>Lisa</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>IBM Watson Health, Cambridge, MA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Zhong</LastName>
<ForeName>Yuxiang</ForeName>
<Initials>Y</Initials>
<AffiliationInfo><Affiliation>Medtronic, Northridge, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Agrawal</LastName>
<ForeName>Pratik</ForeName>
<Initials>P</Initials>
<AffiliationInfo><Affiliation>Medtronic, Northridge, CA, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Chang</LastName>
<ForeName>Yuan-Chi</ForeName>
<Initials>YC</Initials>
<AffiliationInfo><Affiliation>IBM Research AI, Yorktown Heights, NY, USA.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>03</Month>
<Day>04</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>United States</Country>
<MedlineTA>AMIA Annu Symp Proc</MedlineTA>
<NlmUniqueID>101209213</NlmUniqueID>
<ISSNLinking>1559-4076</ISSNLinking>
</MedlineJournalInfo>
<ChemicalList><Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D001786">Blood Glucose</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D007004">Hypoglycemic Agents</NameOfSubstance>
</Chemical>
<Chemical><RegistryNumber>0</RegistryNumber>
<NameOfSubstance UI="D007328">Insulin</NameOfSubstance>
</Chemical>
</ChemicalList>
<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D019540" MajorTopicYN="N">Area Under Curve</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D001786" MajorTopicYN="N">Blood Glucose</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D015190" MajorTopicYN="Y">Blood Glucose Self-Monitoring</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003922" MajorTopicYN="N">Diabetes Mellitus, Type 1</DescriptorName>
<QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName>
<QualifierName UI="Q000188" MajorTopicYN="N">drug therapy</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007003" MajorTopicYN="N">Hypoglycemia</DescriptorName>
<QualifierName UI="Q000139" MajorTopicYN="N">chemically induced</QualifierName>
<QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName>
<QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007004" MajorTopicYN="N">Hypoglycemic Agents</DescriptorName>
<QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName>
<QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007328" MajorTopicYN="N">Insulin</DescriptorName>
<QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName>
<QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000069550" MajorTopicYN="Y">Machine Learning</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008954" MajorTopicYN="N">Models, Biological</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018670" MajorTopicYN="Y">Monitoring, Ambulatory</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012372" MajorTopicYN="N">ROC Curve</DescriptorName>
</MeshHeading>
</MeshHeadingList>
</MedlineCitation>
<PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2020</Year>
<Month>4</Month>
<Day>21</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed"><Year>2020</Year>
<Month>4</Month>
<Day>21</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline"><Year>2020</Year>
<Month>9</Month>
<Day>15</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>epublish</PublicationStatus>
<ArticleIdList><ArticleId IdType="pubmed">32308884</ArticleId>
<ArticleId IdType="pmc">PMC7153099</ArticleId>
</ArticleIdList>
<ReferenceList><Reference><Citation>Diabetes Technol Ther. 2013 Aug;15(8):622-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23883408</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Technol Ther. 2017 Mar;19(3):173-182</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28099035</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2014 Jul;37(7):1885-91</Citation>
<ArticleIdList><ArticleId IdType="pubmed">24804697</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Postgrad Med. 2011 Jul;123(4):81-91</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21680992</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2018 Feb;41(2):303-310</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29191844</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Technol Ther. 2013 Aug;15(8):634-43</Citation>
<ArticleIdList><ArticleId IdType="pubmed">23848178</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Diabetes Sci Technol. 2016 May 03;10(3):640-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">26969142</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Nat Rev Endocrinol. 2009 May;5(5):253-61</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19444258</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2010 May;33(5):1013-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20200307</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Med Internet Res. 2018 May 30;20(5):e10775</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29848472</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2005 Oct;28(10):2361-6</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16186263</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2010 May;33(5):1004-8</Citation>
<ArticleIdList><ArticleId IdType="pubmed">20200306</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2017 Jun;40(6):764-770</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28351897</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2017 Dec;40(12):1622-1630</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29162582</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2014 Jul;37(7):1845-53</Citation>
<ArticleIdList><ArticleId IdType="pubmed">24784832</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Res Clin Pract. 2017 Dec;134:17-28</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28951336</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Acta Physiol (Oxf). 2011 Jul;202(3):323-35</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21352505</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>J Clin Endocrinol Metab. 2006 Jun;91(6):2087-92</Citation>
<ArticleIdList><ArticleId IdType="pubmed">16492699</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>QJM. 2009 Sep;102(9):603-7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">19574471</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes. 2007 Jul;56(7):1938-42</Citation>
<ArticleIdList><ArticleId IdType="pubmed">17400929</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Clin Ther. 2017 Sep;39(9):1790-1798.e7</Citation>
<ArticleIdList><ArticleId IdType="pubmed">28781218</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Diabetes Care. 2017 Dec;40(12):1631-1640</Citation>
<ArticleIdList><ArticleId IdType="pubmed">29162583</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Automatica (Oxf). 2012 Aug;48(8):1892-1897</Citation>
<ArticleIdList><ArticleId IdType="pubmed">22865931</ArticleId>
</ArticleIdList>
</Reference>
<Reference><Citation>Best Pract Res Clin Endocrinol Metab. 2010 Oct;24(5):801-15</Citation>
<ArticleIdList><ArticleId IdType="pubmed">21112027</ArticleId>
</ArticleIdList>
</Reference>
</ReferenceList>
</PubmedData>
</pubmed>
<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Californie</li>
<li>Massachusetts</li>
<li>État de New York</li>
</region>
</list>
<tree><country name="États-Unis"><region name="État de New York"><name sortKey="Vu, Long" sort="Vu, Long" uniqKey="Vu L" first="Long" last="Vu">Long Vu</name>
</region>
<name sortKey="Agrawal, Pratik" sort="Agrawal, Pratik" uniqKey="Agrawal P" first="Pratik" last="Agrawal">Pratik Agrawal</name>
<name sortKey="Chang, Yuan Chi" sort="Chang, Yuan Chi" uniqKey="Chang Y" first="Yuan-Chi" last="Chang">Yuan-Chi Chang</name>
<name sortKey="Ide, Tsuyoshi" sort="Ide, Tsuyoshi" uniqKey="Ide T" first="Tsuyoshi" last="Idé">Tsuyoshi Idé</name>
<name sortKey="Jackson, Gretchen" sort="Jackson, Gretchen" uniqKey="Jackson G" first="Gretchen" last="Jackson">Gretchen Jackson</name>
<name sortKey="Kefayati, Sarah" sort="Kefayati, Sarah" uniqKey="Kefayati S" first="Sarah" last="Kefayati">Sarah Kefayati</name>
<name sortKey="Latts, Lisa" sort="Latts, Lisa" uniqKey="Latts L" first="Lisa" last="Latts">Lisa Latts</name>
<name sortKey="Pavuluri, Venkata" sort="Pavuluri, Venkata" uniqKey="Pavuluri V" first="Venkata" last="Pavuluri">Venkata Pavuluri</name>
<name sortKey="Zhong, Yuxiang" sort="Zhong, Yuxiang" uniqKey="Zhong Y" first="Yuxiang" last="Zhong">Yuxiang Zhong</name>
</country>
</tree>
</affiliations>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Bois/explor/BiopestPeptidV1/Data/Main/Exploration

HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000298 | SxmlIndent | more

HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000298 | SxmlIndent | more

Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Bois
   |area=    BiopestPeptidV1
   |flux=    Main
   |étape=   Exploration
   |type=    RBID
   |clé=     pubmed:32308884
   |texte=   Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.
}}

Pour générer des pages wiki

HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i   -Sk "pubmed:32308884" \
       | HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd   \
       | NlmPubMed2Wicri -a BiopestPeptidV1

This area was generated with Dilib version V0.6.38.
Data generation: Thu Nov 19 19:22:35 2020. Site generation: Thu Nov 19 19:33:26 2020

	Serveur d'exploration sur les peptides biopesticides
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.

Serveur d'exploration sur les peptides biopesticides

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

Predicting Nocturnal Hypoglycemia from Continuous Glucose Monitoring Data with Extended Prediction Horizon.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki