Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach
Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences...
Gespeichert in:
| Veröffentlicht in: | JMIR medical informatics Jg. 7; H. 1; S. e10788 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Canada
JMIR Publications
08.02.2019
|
| Schlagworte: | |
| ISSN: | 2291-9694, 2291-9694 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.
We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.
We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data.
HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models.
By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events. |
|---|---|
| AbstractList | Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.BACKGROUNDBleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.OBJECTIVEWe aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data.METHODSWe expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data.HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models.RESULTSHCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models.By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events.CONCLUSIONSBy incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events. Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events. Background: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. Objective: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. Methods: We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. Results: HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. Conclusions: By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events. |
| Author | McManus, David D Cunningham, Francesca Liu, Weisong Yu, Hong Li, Rumeng Hu, Baotian Liu, Feifan |
| AuthorAffiliation | 6 Center for Healthcare Organization and Implementation Research Bedford Veterans Affairs Medical Center Bedford, MA United States 4 Department of Veterans Affairs Center for Medication Safety Hines, IL United States 5 Cardiology Division Department of Medicine University of Massachusetts Medical School Worcester, MA United States 2 Department of Computer Science University of Massachusetts Lowell Lowell, MA United States 3 Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester, MA United States 1 College of Information and Computer Science University of Massachusetts Amherst Amherst, MA United States |
| AuthorAffiliation_xml | – name: 1 College of Information and Computer Science University of Massachusetts Amherst Amherst, MA United States – name: 4 Department of Veterans Affairs Center for Medication Safety Hines, IL United States – name: 2 Department of Computer Science University of Massachusetts Lowell Lowell, MA United States – name: 5 Cardiology Division Department of Medicine University of Massachusetts Medical School Worcester, MA United States – name: 6 Center for Healthcare Organization and Implementation Research Bedford Veterans Affairs Medical Center Bedford, MA United States – name: 3 Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester, MA United States |
| Author_xml | – sequence: 1 givenname: Rumeng orcidid: 0000-0003-4584-7486 surname: Li fullname: Li, Rumeng – sequence: 2 givenname: Baotian orcidid: 0000-0001-7490-684X surname: Hu fullname: Hu, Baotian – sequence: 3 givenname: Feifan orcidid: 0000-0003-0881-6365 surname: Liu fullname: Liu, Feifan – sequence: 4 givenname: Weisong orcidid: 0000-0003-3825-5597 surname: Liu fullname: Liu, Weisong – sequence: 5 givenname: Francesca orcidid: 0000-0001-7924-8372 surname: Cunningham fullname: Cunningham, Francesca – sequence: 6 givenname: David D orcidid: 0000-0002-9343-6203 surname: McManus fullname: McManus, David D – sequence: 7 givenname: Hong orcidid: 0000-0001-9263-5035 surname: Yu fullname: Yu, Hong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30735140$$D View this record in MEDLINE/PubMed |
| BookMark | eNpdkt9qFDEUxoNUbK37ChIQQZDV_JnJzHghrNvVCmsFsXgZMpkz3dRsMiaZFd_LBzTTbaXdqxM4v3zn4zvnKTpy3gFCM0reMNqIt5RUdf0InTDW0HkjmuLo3vsYzWK8JoTQggohqifomJOKl7QgJ-jvGSTQyXiHfY8_WIDOuCu82oFLERuHVza3g3dG43NQNm3wN9A-dPjCJ4j4Mk740rudt-Mkoyy-gDHclPTbh5_4i-_ARrxyG-U0dPiH2YuMIeQhh_RiTB6czn9CfIfPAAa8BhXcNGYxDMErvXmGHvfKRpjd1lN0-XH1fXk-X3_99Hm5WM91IWiaA2ubEgrFaU-10L0uaV2WioNuu4qzVgjVQM8V60s2ZUNB5Vx027JSg-DAT9H7ve4wtlvodLabrcohmK0Kf6RXRj7sOLORV34nBa-LsmBZ4NWtQPC_RohJbk3UYK1y4McoGSN5c4xXVUZfHKDXfgw5zkyVlDZFTWqSqef3Hf23crfQDLzeAzr4GAP0UpukpsVkg8ZKSuR0MfLmYjL98oC-E3zI_QOcdsHA |
| CitedBy_id | crossref_primary_10_2196_35373 crossref_primary_10_1007_s40264_022_01156_5 crossref_primary_10_1136_bmjhci_2021_100323 crossref_primary_10_1136_bmjopen_2021_053218 crossref_primary_10_2196_14971 crossref_primary_10_1093_jamia_ocz200 crossref_primary_10_2196_18471 crossref_primary_10_1055_a_2121_8380 crossref_primary_10_2196_18089 crossref_primary_10_2196_66189 crossref_primary_10_1371_journal_pone_0234908 crossref_primary_10_1159_000519407 crossref_primary_10_1109_ACCESS_2023_3285596 crossref_primary_10_2196_67837 crossref_primary_10_3389_fnins_2021_700253 |
| Cites_doi | 10.1109/ICDM.2017.93 10.1197/jamia.M2777 10.1160/TH16-05-0400 10.1161/CIR.0000000000000485 10.1001/archinte.167.13.1414 10.1212/01.wnl.0000208408.98482.99 10.1109/EMBC.2016.7590783 10.1182/blood-2014-07-590323 10.1016/j.jbi.2017.06.010 10.1093/jamia/ocw011 10.1126/science.1127647 10.1148/radiol.2017171115 10.2196/jmir.8344 10.1093/nar/gkh061 10.1371/journal.pone.0192360 10.1007/s10916-009-9424-0 10.3115/v1/D14-1162 10.1016/j.jclinane.2016.06.016 10.1109/5.726791 10.1007/s11239-013-0899-7 10.1109/ICDM.2003.1250918 10.1212/01.WNL.0000138428.40673.83 10.7326/0003-4819-139-11-200312020-00007 10.1016/j.jbi.2015.07.010 10.1109/JBHI.2017.2767063 10.1093/jamia/ocx039 10.1162/neco.1997.9.8.1735 10.3115/v1/D14-1179 |
| ContentType | Journal Article |
| Copyright | Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.02.2019. 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.02.2019. 2019 |
| Copyright_xml | – notice: Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.02.2019. – notice: 2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.02.2019. 2019 |
| DBID | AAYXX CITATION NPM 3V. 7X7 7XB 88C 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M0T PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM |
| DOI | 10.2196/10788 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central (New) (NC LIVE) ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Healthcare Administration Database ProQuest Databases ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest One Academic Eastern Edition ProQuest Health Management ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2291-9694 |
| ExternalDocumentID | PMC6384542 30735140 10_2196_10788 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NHLBI NIH HHS grantid: R01 HL125089 |
| GroupedDBID | 53G 5VS 7X7 8FI 8FJ AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV BENPR CCPQU CITATION DIK FYUFA GROUPED_DOAJ HMCUK HYE KQ8 M0T M48 M~E OK1 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY RPM UKHRP ALIPV NPM 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c461t-e2b95e4a31f1c6cfc51855a3ecbd732b66a9ef3a2f5214161ea073cbb25ce63e3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000462892100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2291-9694 |
| IngestDate | Tue Nov 04 01:53:28 EST 2025 Fri Sep 05 13:40:03 EDT 2025 Tue Oct 07 07:31:04 EDT 2025 Wed Feb 19 02:36:53 EST 2025 Tue Nov 18 22:31:48 EST 2025 Sat Nov 29 06:43:41 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | autoencoder bleeding convolutional neural networks BiLSTM electronic health record |
| Language | English |
| License | Rumeng Li, Baotian Hu, Feifan Liu, Weisong Liu, Francesca Cunningham, David D McManus, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.02.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c461t-e2b95e4a31f1c6cfc51855a3ecbd732b66a9ef3a2f5214161ea073cbb25ce63e3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0881-6365 0000-0001-9263-5035 0000-0003-4584-7486 0000-0003-3825-5597 0000-0001-7924-8372 0000-0001-7490-684X 0000-0002-9343-6203 |
| OpenAccessLink | https://www.proquest.com/docview/2511948080?pq-origsite=%requestingapplication% |
| PMID | 30735140 |
| PQID | 2511948080 |
| PQPubID | 4997117 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_6384542 proquest_miscellaneous_2200782377 proquest_journals_2511948080 pubmed_primary_30735140 crossref_citationtrail_10_2196_10788 crossref_primary_10_2196_10788 |
| PublicationCentury | 2000 |
| PublicationDate | 20190208 |
| PublicationDateYYYYMMDD | 2019-02-08 |
| PublicationDate_xml | – month: 2 year: 2019 text: 20190208 day: 8 |
| PublicationDecade | 2010 |
| PublicationPlace | Canada |
| PublicationPlace_xml | – name: Canada – name: Toronto – name: Toronto, Canada |
| PublicationTitle | JMIR medical informatics |
| PublicationTitleAlternate | JMIR Med Inform |
| PublicationYear | 2019 |
| Publisher | JMIR Publications |
| Publisher_xml | – name: JMIR Publications |
| References | ref35 Flibotte, JJ (ref7) 2004; 63 ref12 ref34 ref15 ref37 ref36 ref31 ref30 ref11 ref33 ref10 ref32 Linkins, L (ref3) 2003; 139 ref2 ref1 Jagannatha, AN (ref14) 2016; 2016 ref17 Hochreiter, S (ref19) 1997; 9 ref39 ref16 ref38 Wilcox, C (ref4) 2009; 2 Rumeng, L (ref13) 2017; 2017 ref24 ref23 ref45 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 ref28 ref27 ref29 ref8 ref9 ref6 ref5 ref40 Lecun, Y (ref18) 1998; 86 |
| References_xml | – ident: ref39 – ident: ref12 doi: 10.1109/ICDM.2017.93 – ident: ref10 doi: 10.1197/jamia.M2777 – ident: ref25 doi: 10.1160/TH16-05-0400 – ident: ref29 – ident: ref41 – volume: 2 start-page: 21 year: 2009 ident: ref4 publication-title: Clin Exp Gastroenterol – ident: ref2 doi: 10.1161/CIR.0000000000000485 – ident: ref9 doi: 10.1001/archinte.167.13.1414 – ident: ref22 – ident: ref6 doi: 10.1212/01.wnl.0000208408.98482.99 – volume: 2016 start-page: 473 year: 2016 ident: ref14 publication-title: Proc Conf – ident: ref23 doi: 10.1109/EMBC.2016.7590783 – ident: ref27 – ident: ref5 doi: 10.1182/blood-2014-07-590323 – ident: ref36 doi: 10.1016/j.jbi.2017.06.010 – ident: ref38 doi: 10.1093/jamia/ocw011 – ident: ref32 – ident: ref21 doi: 10.1126/science.1127647 – ident: ref17 – volume: 2017 start-page: 1149 year: 2017 ident: ref13 publication-title: AMIA Annu Symp Proc – ident: ref15 – ident: ref30 – ident: ref34 doi: 10.1148/radiol.2017171115 – ident: ref35 doi: 10.2196/jmir.8344 – ident: ref40 doi: 10.1093/nar/gkh061 – ident: ref33 doi: 10.1371/journal.pone.0192360 – ident: ref24 doi: 10.1007/s10916-009-9424-0 – ident: ref28 – ident: ref43 doi: 10.3115/v1/D14-1162 – ident: ref8 doi: 10.1016/j.jclinane.2016.06.016 – volume: 86 start-page: 2278 year: 1998 ident: ref18 publication-title: Proceedings of the IEEE doi: 10.1109/5.726791 – ident: ref42 – ident: ref1 doi: 10.1007/s11239-013-0899-7 – ident: ref26 – ident: ref37 doi: 10.1109/ICDM.2003.1250918 – volume: 63 start-page: 1059 issue: 6 year: 2004 ident: ref7 publication-title: Neurology doi: 10.1212/01.WNL.0000138428.40673.83 – volume: 139 start-page: 893 issue: 11 year: 2003 ident: ref3 publication-title: Ann Intern Med doi: 10.7326/0003-4819-139-11-200312020-00007 – ident: ref44 doi: 10.1016/j.jbi.2015.07.010 – ident: ref11 doi: 10.1109/JBHI.2017.2767063 – ident: ref45 doi: 10.1093/jamia/ocx039 – ident: ref16 – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: ref19 publication-title: Neural Comput doi: 10.1162/neco.1997.9.8.1735 – ident: ref20 doi: 10.3115/v1/D14-1179 – ident: ref31 |
| SSID | ssj0001416667 |
| Score | 2.3706775 |
| Snippet | Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with... Background: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e10788 |
| SubjectTerms | Anticoagulants Automation Classification Data collection Deep learning Electronic health records Machine translation Neural networks Original Paper Support vector machines |
| Title | Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/30735140 https://www.proquest.com/docview/2511948080 https://www.proquest.com/docview/2200782377 https://pubmed.ncbi.nlm.nih.gov/PMC6384542 |
| Volume | 7 |
| WOSCitedRecordID | wos000462892100007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: 7X7 dateStart: 20130101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Healthcare Administration Database customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: M0T dateStart: 20130101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthmanagement providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central (New) (NC LIVE) customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: BENPR dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2291-9694 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001416667 issn: 2291-9694 databaseCode: PIMPY dateStart: 20130101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwED7BhiYkxM8BhVEZaa8Ra-zEDi-o2zKBRKsKDShPkeM4rFKVlCXdX8YfyJ3jZluReOEllZJrEunO5-_sL98BHBZHSpciLIKwLFQgdJ4ESo10oGzBhRJcKseq_PZZTqdqPk9mfsGt8bTKTU50ibqoDa2RvyMonAhSQfyw-hVQ1yjaXfUtNO7CLrXNpjiXc3m9xiJoU0zuwQNiPGOsYY6QrsfKjSnoL1y5TY-8Md-cPfrfN30MDz3SZOMuNJ7AHVs9hb2J30t_Br9Pbet4WBWrS3a87KYxlhIBsmGLiqV9hxzWfazEulqVTWvEp8yRDdhJXV356MWHkdSH-3HcckaN1pYNS6sLxzNg3xfdTTpNqG3r8bqtSVaTqNXv2am1K-bVX3-ysZc-34evZ-n5ycfA93AIjIhHbWDDPIms0HxUjkxsShMhQIg0tyYvJA_zONaJLbkOS8QRVGxZjUnH5HkYGRtzy5_DTlVX9iV9Xa5tLIURVEIirtVY2VssIZUpeJHHYgCHG7dmxgucU5-NZYaFDnk_c94fwLA3W3WKHtsGBxu_Zn5AN9m1Uwfwtr-MQ5H2V3Rl6zXahA5wcSkH8KILof4JlEoRm-K_5a3g6g1I5vv2lWpx4eS-MUOKSISv_v1ar-E-YrnEEcrVAey0l2v7Bu6Zq3bRXA7duHBHNYTd43Q6-zJ0yw94nByd47nZp8nsxx98eCMo |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHRpIiPulMIaRxmMEtZ3YQUKorJ1Wra36MGA8BcdxWKUqKUs6xJ_iiR-Ij3PZViTe9sBTHnJyUfL5XOzP3wHYTd5IlXKaeDRNpMdVHHpS9pQnTcK45ExIx6r8NBbTqTw-Dmcb8KvZC4O0ysYnOked5BrnyF9jKhxyVEF8v_zuYdcoXF1tWmhUsDg0P3_Ykq14NxrY__uK0v3h0d6BV3cV8DQPeqVnaBz6hivWS3s60Kn2bcjyFTM6TgSjcRCo0KRM0dRGNkz_jbLDQMcx9bUJmGH2vtdgkyPYO7A5G01mX85ndTguw4ktuIUca4tu65WE6-pyIej9lcmuEzIvRLj9O__bt7kLt-tcmvQr8N-DDZPdh61JzRZ4AL8HpnRMs4zkKfmwqAI1GSLFsyDzjAzbHkCk2o5FqmqcTHObgRNHpyB7eXZWj0_7MBQzcQfHnifYSm5RkGF24pgU5PO8ukmlerVu3V-VOQqHInn8LRkYsyS1vu030q_F3R_Cxyv5Zo-gk-WZeYL755UJBNcci2SbuSsehMYWyVInLIkD3oXdBkaRriXcsZPIIrKlHKItcmjrwk5rtqw0S9YNthscRbXLKqJzEHXhZXvaOhtcQVKZyVfWhrqUkgnRhccVZNsnYLCw2be9WlwCc2uAQuaXz2TzEydobmMA9zl9-u_XegE3Do4m42g8mh4-g5s2cw0dfV5uQ6c8XZnncF2flfPidKcelQS-XjXY_wBQ1302 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1bb9MwFD4aHaomIe6DwhhGGo8R1HYSBwmhsrai2lb1gcv2FBzHYZWqpCzpEP-LJ34dx46TbUXibQ885SEnFyWfz8X-_B2AvfS1kBmnqUezVHhcJpEnRF96QqeMC85CYVmVnw_D6VQcH0ezDfjd7IUxtMrGJ1pHnRbKzJG_MqlwxK0KYuZoEbPh-N3yu2c6SJmV1qadRg2RA_3zB5Zv5dvJEP_1S0rHo4_7HzzXYcBTPOhXnqZJ5GsuWT_rq0Blysfw5UumVZKGjCZBICOdMUkzjHKmFNASh4RKEuorHTDN8L43YBNTck47sDmbHM1OLmZ4uFmSC7twy_CtEenooULb4eVSAPwrq10nZ16KduM7__N3ugu3XY5NBvWguAcbOr8P3SPHIngAv4a6sgy0nBQZeb-oAzgZGepnSeY5GbW9gUi9TYvUVTqZFpiZE0uzIPtFfu7GLT7MiJzYg2XVE9NiblGSUX5qGRbky7y-Sa2GtW49WFWFERQ1pPI3ZKj1kjjd229k4ETfH8Kna_lm29DJi1w_NvvqpQ5CrrgpnjGjlzyINBbPQqUsTQLeg70GUrFy0u6mw8gixhLPIC-2yOvBbmu2rLVM1g12GkzFzpWV8QWgevCiPY1OyKwsyVwXK7ShNtVkYdiDRzV82yeYIIJZOV4dXgF2a2AEzq-eyeenVugcYwP3OX3y79d6Dl1EeHw4mR48hS1MaCPLqhc70KnOVvoZ3FTn1bw823UDlMDX68b6H-tChfY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+Bleeding+Events+in+Electronic+Health+Record+Notes+Using+Convolutional+Neural+Network+Models+Enhanced+With+Recurrent+Neural+Network+Autoencoders%3A+Deep+Learning+Approach&rft.jtitle=JMIR+medical+informatics&rft.au=Li%2C+Rumeng&rft.au=Hu%2C+Baotian&rft.au=Liu%2C+Feifan&rft.au=Liu%2C+Weisong&rft.date=2019-02-08&rft.pub=JMIR+Publications&rft.eissn=2291-9694&rft.volume=7&rft.issue=1&rft_id=info:doi/10.2196%2F10788&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2291-9694&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2291-9694&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2291-9694&client=summon |