Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services
Background In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on de...
Gespeichert in:
| Veröffentlicht in: | Scandinavian journal of trauma, resuscitation and emergency medicine Jg. 28; H. 1; S. 17 - 8 |
|---|---|
| Hauptverfasser: | , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
BioMed Central
04.03.2020
BioMed Central Ltd Springer Nature B.V BMC |
| Schlagworte: | |
| ISSN: | 1757-7241, 1757-7241 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Background
In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.
Methods
We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables.
Results
The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864–0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]).
Conclusions
The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. |
|---|---|
| AbstractList | Background In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). Conclusions The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. Keywords: Emergency medical service, Triage, Artificial intelligence, Deep learning In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. Abstract Background In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864–0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]). Conclusions The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. Background In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864–0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]). Conclusions The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. BackgroundIn emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.MethodsWe conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables.ResultsThe number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864–0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831–0.846]), Korean Triage and Acuity System (0.824 [0.815–0.832]), National Early Warning Score (0.741 [0.734–0.748]), and Modified Early Warning Score (0.696 [0.691–0.699]).ConclusionsThe AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.BACKGROUNDIn emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables.METHODSWe conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables.The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]).RESULTSThe number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]).The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.CONCLUSIONSThe AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores. |
| ArticleNumber | 17 |
| Audience | Academic |
| Author | Cho, Kyung-Jae Park, Jinsik Kwon, Joon-myoung Jeon, Ki-Hyun Lee, Yeha Kwon, Oyeon Oh, Byung-Hee Kang, Da-Young Park, Hyunho |
| Author_xml | – sequence: 1 givenname: Da-Young surname: Kang fullname: Kang, Da-Young organization: Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute – sequence: 2 givenname: Kyung-Jae surname: Cho fullname: Cho, Kyung-Jae organization: VUNO – sequence: 3 givenname: Oyeon surname: Kwon fullname: Kwon, Oyeon organization: VUNO – sequence: 4 givenname: Joon-myoung orcidid: 0000-0001-6754-1010 surname: Kwon fullname: Kwon, Joon-myoung email: kwonjm@sejongh.co.kr organization: Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Department of Emergency Medicine, Mediplex Sejong Hospital – sequence: 5 givenname: Ki-Hyun surname: Jeon fullname: Jeon, Ki-Hyun organization: Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital – sequence: 6 givenname: Hyunho surname: Park fullname: Park, Hyunho organization: VUNO – sequence: 7 givenname: Yeha surname: Lee fullname: Lee, Yeha organization: VUNO – sequence: 8 givenname: Jinsik surname: Park fullname: Park, Jinsik organization: Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital – sequence: 9 givenname: Byung-Hee surname: Oh fullname: Oh, Byung-Hee organization: Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32131867$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9Ustq3DAUNSWlebQf0E0xFEo3TvWWvSkMoY9AoJt2LST5ylbwWBNJE8jfR-6kyUxoixYSV-ece-_hnFZHc5ihqt5idI5xKz4lTBHrGkRQgySmDXtRnWDJZSMJw0d77-PqNKVrhARBnL2qjinBtAjIk-pmFbN33no91X7OME1-gNlCrachRJ_HdZ1DvYnQe5vrPEI9A_S1C7G25dvbwrM6QiEvqDGkjc-lBmuIi9BdvV6opZIg3noL6XX10ukpwZuH-6z69fXLz4vvzdWPb5cXq6vGCspywyxFLeXakp6Z3vXEaMm1oaYHQ5HhrtW6EwLjznEtcKsNwcb0QjhHJO8YPasud7p90NdqE_1axzsVtFe_CyEOSpfd7QRKUkelkEgYAYwZ3tkWOWpcR1rhUI-L1ued1mZryj4W5hz1dCB6-DP7UQ3hVknEpUDLMB8fBGK42ULKau2TLW7rGcI2KUIlbjlFtCvQ98-g12Eb52KVIpJ0nHEm5RNq0GUBP7tQ-tpFVK0EFpSyjpCCOv8Lqpwe1t6WMDlf6geED3uEEfSUxxSmbfZhTofAd_uOPFrxJ1kFgHcAG0NKEdwjBCO1pFft0qtKetWSXrW4JJ9xbAnT0ruM7af_MsmOmUqXeYD4ZNq_SfcerwKa |
| CitedBy_id | crossref_primary_10_31965_infokes_Vol21_Iss4_1105 crossref_primary_10_1038_s41746_020_00333_z crossref_primary_10_3390_healthcare8030295 crossref_primary_10_1093_imaman_dpad027 crossref_primary_10_1186_s12873_025_01233_9 crossref_primary_10_1007_s10462_023_10638_6 crossref_primary_10_1016_j_ijmedinf_2024_105659 crossref_primary_10_1186_s12911_022_01901_x crossref_primary_10_1155_2023_1221704 crossref_primary_10_1186_s12911_021_01558_y crossref_primary_10_1136_tsaco_2021_000712 crossref_primary_10_1186_s13017_022_00469_1 crossref_primary_10_3389_frai_2022_962165 crossref_primary_10_1109_TII_2021_3123588 crossref_primary_10_7759_cureus_63979 crossref_primary_10_3390_app14166876 crossref_primary_10_1002_cnm_3662 crossref_primary_10_1016_j_ajem_2020_10_050 crossref_primary_10_1007_s11910_024_01351_0 crossref_primary_10_1016_j_ajem_2022_10_011 crossref_primary_10_3390_clinpract13050114 crossref_primary_10_1186_s12873_024_01135_2 crossref_primary_10_3390_brainsci15050494 crossref_primary_10_7759_cureus_91542 crossref_primary_10_1080_10903127_2022_2064946 crossref_primary_10_1016_j_ajo_2023_04_007 crossref_primary_10_1155_2022_5413202 crossref_primary_10_3390_bdcc9090219 crossref_primary_10_1016_j_ajem_2021_07_060 crossref_primary_10_1186_s13054_020_03103_1 crossref_primary_10_1016_j_bbe_2020_12_002 crossref_primary_10_1016_j_procs_2025_03_029 crossref_primary_10_1001_jamanetworkopen_2021_5700 crossref_primary_10_12968_jpar_2023_15_5_214 crossref_primary_10_3389_fpos_2024_1518067 crossref_primary_10_1111_phn_13500 crossref_primary_10_1002_hcs2_72 crossref_primary_10_1016_j_knosys_2025_113431 crossref_primary_10_1016_j_ijmedinf_2023_105274 crossref_primary_10_3390_jcdd10020088 crossref_primary_10_1038_s41746_024_01194_6 crossref_primary_10_2196_40243 crossref_primary_10_1155_2021_9590131 crossref_primary_10_3390_clinpract13040089 crossref_primary_10_3390_diagnostics14121292 crossref_primary_10_1002_emp2_13251 crossref_primary_10_1016_j_jrras_2023_100602 crossref_primary_10_1177_20552076231205736 crossref_primary_10_1109_ACCESS_2021_3070618 crossref_primary_10_2196_51375 crossref_primary_10_1136_emermed_2022_212853 crossref_primary_10_1017_dmp_2024_284 crossref_primary_10_1097_MCC_0000000000001104 crossref_primary_10_3390_math11092030 crossref_primary_10_47606_ACVEN_MV0270 crossref_primary_10_2196_26646 crossref_primary_10_1017_S1049023X24000414 crossref_primary_10_1016_j_bspc_2024_106518 crossref_primary_10_1186_s12873_022_00643_3 crossref_primary_10_3389_fpubh_2024_1439412 crossref_primary_10_1186_s12911_025_03010_x crossref_primary_10_1016_j_burns_2024_03_015 crossref_primary_10_47606_ACVEN_PH0343 |
| Cites_doi | 10.1016/j.resuscitation.2012.12.016 10.1016/j.jen.2011.11.003 10.1016/j.resuscitation.2019.04.007 10.1016/S0196-0644(05)82608-3 10.1016/j.injury.2009.07.065 10.1371/journal.pone.0205836 10.1080/10903129908958963 10.1016/j.annemergmed.2017.09.036 10.1016/S0895-4356(01)00372-9 10.1093/qjmed/94.10.521 10.1109/4235.585893 10.1017/cem.2016.345 10.1016/j.ajem.2017.10.030 10.1016/j.ajem.2018.09.025 10.1016/j.jemermed.2016.02.026 10.1016/j.resuscitation.2016.02.011 10.1016/0895-4356(96)00025-X 10.3346/jkms.2017.32.10.1702 10.1001/jama.2010.1140 10.1186/1757-7241-21-28 10.1097/CCM.0b013e31818c37b2 10.1007/s11739-013-1040-9 10.1038/nature14539 10.1093/intqhc/mzy184 10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F 10.23736/S0393-2249.19.03613-0 10.1214/ss/1009213726 |
| ContentType | Journal Article |
| Copyright | The Author(s). 2020 COPYRIGHT 2020 BioMed Central Ltd. The Author(s). 2020. This work is published under http://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. |
| Copyright_xml | – notice: The Author(s). 2020 – notice: COPYRIGHT 2020 BioMed Central Ltd. – notice: The Author(s). 2020. This work is published under http://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. |
| DBID | C6C AAYXX CITATION NPM 3V. 7RV 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. KB0 M0S NAPCQ PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.1186/s13049-020-0713-4 |
| DatabaseName | Springer Nature OA Free Journals CrossRef PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) 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 ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Health & Medical Collection (Alumni Edition) Nursing & Allied Health Premium ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest 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) DOAJ Open Access Full Text |
| 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 ProQuest Central (New) ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database PubMed MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Open Access Full Text url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7RV name: Nursing & Allied Health Database url: https://search.proquest.com/nahs sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Public Health |
| EISSN | 1757-7241 |
| EndPage | 8 |
| ExternalDocumentID | oai_doaj_org_article_73f376706b6e44b59c80f3bf9286f0d1 PMC7057604 A616334922 32131867 10_1186_s13049_020_0713_4 |
| Genre | Journal Article |
| GeographicLocations | South Korea |
| GeographicLocations_xml | – name: South Korea |
| GrantInformation_xml | – fundername: Ministry of Trade, industry & Energy (Korea) grantid: 2000486, – fundername: ; grantid: 2000486, |
| GroupedDBID | --- 0R~ 123 2WC 53G 5VS 7RV 7X7 8FI 8FJ AABWD AAFWJ AAJSJ AASML ABUWG ACGFO ACGFS ADBBV ADRAZ ADUKV AFKRA AFPKN AHBYD AHYZX AIAGR ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC C6C CCPQU CS3 DIK E3Z EBD EBLON EBS EMOBN F5P FYUFA GROUPED_DOAJ GX1 HMCUK HYE IAO IEA IHR IHW INH INR ITC KQ8 M48 M~E NAPCQ O5R O5S OK1 OVT P2P P6G PGMZT PHGZM PHGZT PIMPY PPXIY PUEGO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 UKHRP AAYXX AFFHD CITATION -5E -5G -A0 -BR ACRMQ ADINQ ALIPV C24 NPM 3V. 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PQEST PQQKQ PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c634t-4c30835ac2d4bdfd2ba75ab3bdeb30b5f8aa966119f5a618ab21bbd66ff275943 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 80 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000520492600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1757-7241 |
| IngestDate | Mon Nov 10 04:34:26 EST 2025 Tue Nov 04 01:34:08 EST 2025 Fri Sep 05 07:40:07 EDT 2025 Tue Oct 21 12:46:54 EDT 2025 Tue Nov 11 10:10:45 EST 2025 Tue Nov 04 17:44:30 EST 2025 Thu May 22 21:13:48 EDT 2025 Thu Jan 02 23:00:03 EST 2025 Sat Nov 29 05:52:09 EST 2025 Tue Nov 18 21:59:31 EST 2025 Sat Sep 06 07:29:31 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Triage Emergency medical service Artificial intelligence |
| Language | English |
| License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c634t-4c30835ac2d4bdfd2ba75ab3bdeb30b5f8aa966119f5a618ab21bbd66ff275943 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 |
| ORCID | 0000-0001-6754-1010 |
| OpenAccessLink | https://doaj.org/article/73f376706b6e44b59c80f3bf9286f0d1 |
| PMID | 32131867 |
| PQID | 2729545477 |
| PQPubID | 5642774 |
| PageCount | 8 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_73f376706b6e44b59c80f3bf9286f0d1 pubmedcentral_primary_oai_pubmedcentral_nih_gov_7057604 proquest_miscellaneous_2371853039 proquest_journals_2729545477 gale_infotracmisc_A616334922 gale_infotracacademiconefile_A616334922 gale_healthsolutions_A616334922 pubmed_primary_32131867 crossref_primary_10_1186_s13049_020_0713_4 crossref_citationtrail_10_1186_s13049_020_0713_4 springer_journals_10_1186_s13049_020_0713_4 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-03-04 |
| PublicationDateYYYYMMDD | 2020-03-04 |
| PublicationDate_xml | – month: 03 year: 2020 text: 2020-03-04 day: 04 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scandinavian journal of trauma, resuscitation and emergency medicine |
| PublicationTitleAbbrev | Scand J Trauma Resusc Emerg Med |
| PublicationTitleAlternate | Scand J Trauma Resusc Emerg Med |
| PublicationYear | 2020 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | S Ioffe (713_CR13) 2015 CP Subbe (713_CR19) 2001; 94 J Kwon (713_CR9) 2019; 139 GB Smith (713_CR20) 2013; 84 F Dojmi Di Delupis (713_CR7) 2014; 9 SC Bagley (713_CR29) 2001; 54 Y LeCun (713_CR30) 2015; 521 M Christ (713_CR33) 2010; 107 J Kwon (713_CR31) 2018; 7 CW Seymour (713_CR4) 2010; 304 TG Dietterich (713_CR24) 2007 L Moore (713_CR1) 1999; 3 Joon-myoung Kwon (713_CR11) 2018; 13 Guo-Wen Sun (713_CR28) 1996; 49 M Abadi (713_CR14) 2016 TA Williams (713_CR21) 2016; 102 AF Dugas (713_CR34) 2016; 50 B Lee (713_CR18) 2017; 32 HM Buschhorn (713_CR26) 2013; 39 RC Fong (713_CR37) 2017 Eileen Shu (713_CR25) 2019; 37 713_CR35 713_CR10 JM Kahn (713_CR5) 2008; 36 713_CR17 X Chen (713_CR36) 2016 M Leeies (713_CR27) 2017; 19 DH Wolpert (713_CR32) 1997; 1 WG Baxt (713_CR2) 1990; 19 N Gilboy (713_CR15) 2012 B Mistry (713_CR16) 2018; 71 RJ Schalkoff (713_CR12) 1992 V Gulshan (713_CR8) 2016; 304 J Carpenter (713_CR23) 2000; 19 R Jouffroy (713_CR22) 2018; 36 SM Evans (713_CR6) 2010; 41 IB Lidal (713_CR3) 2013; 21 |
| References_xml | – volume: 84 start-page: 465 year: 2013 ident: 713_CR20 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2012.12.016 – volume: 39 start-page: e55 year: 2013 ident: 713_CR26 publication-title: J Emerg Nurs doi: 10.1016/j.jen.2011.11.003 – volume: 107 start-page: 892 year: 2010 ident: 713_CR33 publication-title: Dtsch Arztebl Int – volume: 304 start-page: 649 year: 2016 ident: 713_CR8 publication-title: Jama – volume: 139 start-page: 84 year: 2019 ident: 713_CR9 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2019.04.007 – volume-title: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets year: 2016 ident: 713_CR36 – volume: 19 start-page: 1401 year: 1990 ident: 713_CR2 publication-title: Ann Emerg Med doi: 10.1016/S0196-0644(05)82608-3 – volume: 41 start-page: 460 year: 2010 ident: 713_CR6 publication-title: Injury doi: 10.1016/j.injury.2009.07.065 – volume: 13 start-page: e0205836 issue: 10 year: 2018 ident: 713_CR11 publication-title: PLOS ONE doi: 10.1371/journal.pone.0205836 – volume: 3 start-page: 325 year: 1999 ident: 713_CR1 publication-title: Prehospital Emerg Care doi: 10.1080/10903129908958963 – volume: 71 start-page: 581 year: 2018 ident: 713_CR16 publication-title: Ann Emerg Med doi: 10.1016/j.annemergmed.2017.09.036 – volume: 54 start-page: 979 year: 2001 ident: 713_CR29 publication-title: J Clin Epidemiol doi: 10.1016/S0895-4356(01)00372-9 – volume: 94 start-page: 521 year: 2001 ident: 713_CR19 publication-title: QJM doi: 10.1093/qjmed/94.10.521 – volume-title: Pattern recognition - statistical, structural and neural approaches year: 1992 ident: 713_CR12 – volume: 1 start-page: 67 year: 1997 ident: 713_CR32 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.585893 – start-page: 265 volume-title: 12th USENIX Symp Oper Syst Des Implement (OSDI ‘16) year: 2016 ident: 713_CR14 – volume: 19 start-page: 26 year: 2017 ident: 713_CR27 publication-title: CJEM doi: 10.1017/cem.2016.345 – volume: 36 start-page: 820 year: 2018 ident: 713_CR22 publication-title: Am J Emerg Med doi: 10.1016/j.ajem.2017.10.030 – volume: 37 start-page: 1273 issue: 7 year: 2019 ident: 713_CR25 publication-title: The American Journal of Emergency Medicine doi: 10.1016/j.ajem.2018.09.025 – volume: 50 start-page: 910 year: 2016 ident: 713_CR34 publication-title: J Emerg Med doi: 10.1016/j.jemermed.2016.02.026 – volume-title: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift year: 2015 ident: 713_CR13 – volume: 7 year: 2018 ident: 713_CR31 publication-title: J Am Heart Assoc – volume: 102 start-page: 35 year: 2016 ident: 713_CR21 publication-title: Resuscitation doi: 10.1016/j.resuscitation.2016.02.011 – volume: 49 start-page: 907 issue: 8 year: 1996 ident: 713_CR28 publication-title: Journal of Clinical Epidemiology doi: 10.1016/0895-4356(96)00025-X – volume-title: Ensemble methods in machine learning year: 2007 ident: 713_CR24 – volume: 32 start-page: 1702 year: 2017 ident: 713_CR18 publication-title: J Korean Med Sci doi: 10.3346/jkms.2017.32.10.1702 – start-page: 3449 volume-title: Proc IEEE Int Conf Comput Vis 2017 year: 2017 ident: 713_CR37 – volume: 304 start-page: 747 year: 2010 ident: 713_CR4 publication-title: JAMA doi: 10.1001/jama.2010.1140 – volume-title: Rosenau AM. Emergency Severity Index (ESI): A Triage Tool for Emergency Department Care, Version 4: Implementation Handbook 2012 Edition year: 2012 ident: 713_CR15 – volume: 21 start-page: 28 year: 2013 ident: 713_CR3 publication-title: Scand J Trauma Resusc Emerg Med doi: 10.1186/1757-7241-21-28 – volume: 36 start-page: 3085 year: 2008 ident: 713_CR5 publication-title: Crit Care Med doi: 10.1097/CCM.0b013e31818c37b2 – volume: 9 start-page: 575 year: 2014 ident: 713_CR7 publication-title: Intern Emerg Med doi: 10.1007/s11739-013-1040-9 – volume: 521 start-page: 436 year: 2015 ident: 713_CR30 publication-title: Nature doi: 10.1038/nature14539 – ident: 713_CR17 doi: 10.1093/intqhc/mzy184 – volume: 19 start-page: 1141 year: 2000 ident: 713_CR23 publication-title: Stat Med doi: 10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F – ident: 713_CR35 doi: 10.23736/S0393-2249.19.03613-0 – ident: 713_CR10 doi: 10.1214/ss/1009213726 |
| SSID | ssj0062054 |
| Score | 2.4727383 |
| Snippet | Background
In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification... In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those... Background In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification... BackgroundIn emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early identification... Abstract Background In emergency medical services (EMSs), accurately predicting the severity of a patient’s medical condition is important for the early... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 17 |
| SubjectTerms | Algorithms Artificial intelligence Blood pressure Body temperature Critical care Critical care medicine Deep learning Emergency medical care Emergency medical service Emergency medical services Emergency Medicine Emergency services Heart rate Hospital emergency services Hospitals Medical care needs assessment Medical research Medicine Medicine & Public Health Methods Original Research Patients Technology application Time Traumatic Surgery Triage Vital signs |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection (ProQuest) dbid: 7X7 link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELagcEBC5VkIFDASEhLIavyIHZ9QQVScKg4g7c2ynbi7Upvd7qb8_nocb9oU0QvX9XgV2-NvZjz2Nwh9oFIG7YIn3CpNhOWa6Eo7IirnVMsDZbRJxSbU8XE9m-mf-cBtk69VbjExAXWz9HBGfsAUZKQqodSX1TmBqlGQXc0lNO6ie1A2G_RczcaAS7IyVUGLFlIRFU1VzmrSWh5sKOSXCARPEKcRMbFLib7_b5C-ZqVu3qC8kUZN1uno0f-O6zHazX4pPhwU6Qm603ZP0cPhUA8Pb5WeoXNoHign8OIalye2pyfxP_v5Ge6XeLWG5E-Po2uJu2gccfSLsc8lFTBcNYudQWqea5bgdvsGFJ8NeSO8yRD2HP0--v7r2w-SazYQL7noifAcnDrrWSNcExrmrKqs466JUXvpqlBbGyMsSnWorKS1dYw610SNCUxVWvA9tNMtu_YlwhFOGu5to1vhhIpQ0QYRqI1OBdUuBrYFKrcrZnwmNIe6GqcmBTa1NMMim7jIBhbZiAJ9GrusBjaP24S_ghqMgkDEnX5Yrk9M3tdG8QB8OKV0shXCVdrXZeAuaFbLUDa0QO9AiczwqnWEE3MooyMMzJCsQB-TBABK_Hxv87uIOAlAzTWR3J9IRiDw0-athpkMRBtzpV4Fej82Q0-4XNe1y4sowxV4bSXXBXox6PU4aM4oB87DAqmJxk9mZdrSLeaJplzFUECWcRo_b_fG1Wf9c9Jf3T6I1-gBS3uWk1Lso51-fdG-Qff9n36xWb9Nu_8SOsNf5A priority: 102 providerName: ProQuest – databaseName: Springer Journals New Starts & Take-Overs Collection dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bi9UwEA66igjiZb1VV40gCC7F5tKkeVzFxRcXwQv7FpK02XNgt2dtu_5-M2lat-sF9LWZlHY6-TLTyXyD0AsihFfWu5wZqXJumMpVqWzOS2tlwzyhpI7NJuTBQXV4qD6mOu5-Ou0-pSQjUsdlXYnXPYGMUA7hDkRWOb-MrpRANgMh-qevE_wKGpyQlL787bTFBhR5-n9F43Pb0cWjkhfypXEb2r_1Xy9wG91MXifeG83kDrrUtNvo2oeUV99GN8a_d3gsSrqLvoHkyC2B1-dIO7E5Ptp062F1gocNPu3gBgMOPiRuwy6IgwOMXeqdgOFMWZgMUqvUnAQ3U7EnPhkTRLhPWHUPfdl_9_nt-zw1Z8idYHzIuWPgvRlHa25rX1NrZGkss3UIzwtb-sqYEEoRonxpBKmMpcTaOpiGp7JUnN1HW-2mbR4iHHCjZs7UquGWy4AJjeeemOA9EGVDBJuhYvpi2iXmcmigcaxjBFMJPapWB9VqUK3mGXo1TzkdaTv-JvwGzGAWBMbteGHTHem0gLVkHohvCmFFw7ktlasKz6xXtBK-qEmGnoER6bF8dcYNvSeCxwsUkDRDL6MEIEd4fGdSAURQAnBwLSR3FpJhxbvl8GSoOiFOr6mEjG3JpczQ83kYZsIpurbZnAUZJsE9K5jK0IPRrueXZpQwIDfMkFxY_EIry5F2vYp85DL4_KIIatyd7P7nY_1R6Y_-Sfoxuk7jwmF5wXfQ1tCdNU_QVfd9WPfd0wgAPwA_E1aY priority: 102 providerName: Springer Nature |
| Title | Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services |
| URI | https://link.springer.com/article/10.1186/s13049-020-0713-4 https://www.ncbi.nlm.nih.gov/pubmed/32131867 https://www.proquest.com/docview/2729545477 https://www.proquest.com/docview/2371853039 https://pubmed.ncbi.nlm.nih.gov/PMC7057604 https://doaj.org/article/73f376706b6e44b59c80f3bf9286f0d1 |
| Volume | 28 |
| WOSCitedRecordID | wos000520492600001&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: PRVADU databaseName: Open Access: BioMedCentral Open Access Titles customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: RBZ dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Open Access Full Text customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: DOA dateStart: 20080101 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: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: M~E dateStart: 20080101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection (ProQuest) customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: 7X7 dateStart: 20081201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: 7RV dateStart: 20081201 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: BENPR dateStart: 20081201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: PIMPY dateStart: 20081201 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: Springer Journals New Starts & Take-Overs Collection customDbUrl: eissn: 1757-7241 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062054 issn: 1757-7241 databaseCode: RSV dateStart: 20081201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3db9MwELdg8ICEEN8ERjESEhIoWvwRO37c0CZ4oKoKTOXJspOYVtrS0Wb8_dzFaWmGgBdeIjk-S8592Hc5-3eEvGJKBeNDmQqnTSqdMKnJjU9l7r2uRWCcVV2xCT0eF7OZmeyU-sIzYREeODLuQIuAgCOZ8qqW0uemLLIgfDC8UCGrusAn02YTTMU1WHHwRPocJivUwZphNinFUAmjslQOdqEOrP_3JXlnT7p6XvJK0rTbi07ukju9E0kP4-TvkWt1c5_cjn_gaLxY9IB8x-6ID0EXO8Cb1J19W64W7fyctkt6scJMTUvBD6QN7GQUnFha9vUPKJ4Lg8FINe8LjNB6c2GTnsckD133681D8uXk-PO792lfYCEtlZBtKkuBHpgreSV9FSrunc6dF76CEDvzeSicg3CIMRNyp1jhPGfeVyDewHVupHhE9pplUz8hFGy_EqWrTC291GDXdZCBOfAAmPEQhSYk2zDclj36OBbBOLNdFFIoG2VkQUYWZWRlQt5sh1xE6I2_ER-hFLeEiJrdvQBdsr0u2X_pUkJeoA7YeAV1a_v2UIHXijCOPCGvOwq0fph-6fpLDMAExNEaUO4PKMFqy2H3Rs9sv2qsLdeYdc2l1gl5ue3GkXgSrqmXl0AjNLpYmTAJeRzVcvvRgjOBAIUJ0QOFHXBl2NMs5h2muAa_XWXAxrcb1f41rT8y_en_YPozcot3hinSTO6TvXZ1WT8nN8sf7WK9GpHrenqKz5nunsWI3Dg6Hk-mo87goTX58HHyFVrTT6c_AUNuVpo |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEF6VFAkkxPthKHSRQEggq96Hvd4DQuVRNWob5VCkclp2bW8TqXXSJAXxp_iNzPiR1kX01gPX7GzkHc98M-PZmSHkFUsSr53PQmGVDqUVOtSxdqGMnVOF8IyzvBo2oQaD9OBAD1fI77YWBq9VtphYAXU-yfAb-QZXmJGKpVIfpichTo3C7Go7QqMWi53i108I2ebv-5_h_b7mfOvL_qftsJkqEGaJkItQZgLdDpvxXLrc59xZFVsnXA5xZeRin1oLMQBj2sc2Yal1nDmXw5k8V7GWAv73GlmVIOxRj6wO-3vDby32Jzyq5q6BTVahAuPY5FFZmmzMGWa0QgzXMDIMZccSVgMD_jYL5-zixTubFxK3lT3cuvO_cfIuud143nSzVpV7ZKUo75Nb9WdLWldjPSAnuFw31aDjc91KqT06hDMsRsd0MaHTGaa3FhScZ1qC-afg-dOsGRpB8TIdbEaqUTOVhRZtlSs9rjNjdN6A9EPy9UpO_Yj0yklZPCEUADMXmc11IZ1UAIaFl55ZcJuYdhC6ByRqJcRkTct2nBxyZKrQLU1MLVQGhMqgUBkZkLfLLdO6X8llxB9R7JaE2Gq8-mEyOzQNchklPHb8iRKXFFK6WGdp5IXzmqeJj3IWkHUUWlPX7S4B02wm4Opj70sekDcVBUImPH5mm8oPYAI2H-tQrnUoAeqy7nIr0aaB2rk5E-eAvFwu4068PlgWk1OgEQr90kjogDyu9Wh5aMGZwK6OAVEdDetwpbtSjkdVI3YFwU4SARvftbp49lj_ZPrTyw-xTm5s7-_tmt3-YOcZuckrvBBhJNdIbzE7LZ6T69mPxXg-e9FgDyXfr1pJ_wDytcCo |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3db9MwELdGhxAS4vsjMJiRQEigqPFH4vgBoY2tYhqqKgTS3oydxGulLe3aDsS_xl_HXT66ZYi97YHX-lzFzt3v7pez7wh5xZLEa-ezUFilQ2mFDnWsXShj51QhPOMsr5pNqOEwPTjQozXyu70Lg8cqW0ysgDqfZviNvM8VZqRiqVTfN8ciRjuDD7OTEDtIYaa1badRq8h-8esn0LfF-70deNevOR_sfv34KWw6DIRZIuQylJnAEMRmPJcu9zl3VsXWCZcDx4xc7FNrgQ8wpn1sE5Zax5lzOazPcxVrKeB_r5F1JYD09Mj69u5w9KX1AwmPqh5s4J9VqMBRNjlVlib9BcPsVojUDVliKDtesWoe8LeLOOcjL57fvJDErXzj4M7_vKt3ye0mIqdbtQndI2tFeZ_cqj9n0vqW1gNygsN1sQ06OVfFlNqjQ1jDcnxMl1M6m2Paa0khqKYlhAUUGAHNmmYSFA_ZwWSUGjfdWmjR3n6lx3XGjC4a8H5Ivl3Jqh-RXjktiyeEApDmIrO5LqSTCkCy8NIzC-EU0w4ofUCiVltM1pRyx44iR6aidGliagUzoGAGFczIgLxdTZnVdUwuE95GFVwJYgny6ofp_NA0iGaU8FgJKEpcUkjpYp2lkRfOa54mPspZQDZRgU19n3cFpGYrAQqANTF5QN5UEgil8PiZbW6EwCZgUbKO5EZHEiAw6w632m0aCF6YM9UOyMvVMM7EY4VlMT0FGaEwXo2EDsjj2qZWixacCaz2GBDVsbbOrnRHysm4KtCugAQlEWzju9Yuzx7rn5v-9PJFbJIbYJnm895w_xm5ySvoEGEkN0hvOT8tnpPr2Y_lZDF_0cAQJd-v2kb_AODjyUI |
| 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=Artificial+intelligence+algorithm+to+predict+the+need+for+critical+care+in+prehospital+emergency+medical+services&rft.jtitle=Scandinavian+journal+of+trauma%2C+resuscitation+and+emergency+medicine&rft.au=Da-Young+Kang&rft.au=Kyung-Jae+Cho&rft.au=Oyeon+Kwon&rft.au=Joon-myoung+Kwon&rft.date=2020-03-04&rft.pub=BMC&rft.eissn=1757-7241&rft.volume=28&rft.issue=1&rft.spage=1&rft.epage=8&rft_id=info:doi/10.1186%2Fs13049-020-0713-4&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_73f376706b6e44b59c80f3bf9286f0d1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1757-7241&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1757-7241&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1757-7241&client=summon |