Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards
Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Observational cohort study. Five hospitals, from No...
Saved in:
| Published in: | Critical care medicine Vol. 44; no. 2; pp. 368 - 374 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
01.02.2016
|
| Subjects: | |
| ISSN: | 1530-0293 |
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.
Observational cohort study.
Five hospitals, from November 2008 until January 2013.
Hospitalized ward patients
None
Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]).
In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards. |
|---|---|
| AbstractList | OBJECTIVEMachine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database.DESIGNObservational cohort study.SETTINGFive hospitals, from November 2008 until January 2013.PATIENTSHospitalized ward patientsINTERVENTIONSNoneMEASUREMENTS AND MAIN RESULTSDemographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]).CONCLUSIONSIn this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards. Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Observational cohort study. Five hospitals, from November 2008 until January 2013. Hospitalized ward patients None Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards. |
| Author | Winslow, Christopher Kattan, Michael W Yuen, Trevor C Edelson, Dana P Meltzer, David O Churpek, Matthew M |
| Author_xml | – sequence: 1 givenname: Matthew M surname: Churpek fullname: Churpek, Matthew M organization: 1Department of Medicine, University of Chicago, Chicago, IL. 2Department of Medicine, NorthShore University HealthSystem, Evanston, IL. 3Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH – sequence: 2 givenname: Trevor C surname: Yuen fullname: Yuen, Trevor C – sequence: 3 givenname: Christopher surname: Winslow fullname: Winslow, Christopher – sequence: 4 givenname: David O surname: Meltzer fullname: Meltzer, David O – sequence: 5 givenname: Michael W surname: Kattan fullname: Kattan, Michael W – sequence: 6 givenname: Dana P surname: Edelson fullname: Edelson, Dana P |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26771782$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkE1LxDAQhoMo7of-A5EcvXRN0qZJj1I_YYsiisclTWZ3I91kTVrBo__cLK7gMDDM8LwzvDNBh847QOiMkhkllbis62ZG_gXlgh6gMeU5yQir8hGaxPie5gUX-TEasVIIKiQbo-9m6HqrwfUQcO03WxVs9A77JW6UXlsHeA4qOOtWuIF-7U3EypmEus8kst6pDj_DKkCMqcFLH_BTAGN1v5PUnXVWJ-Qa0gHrg9pJcMp-DfhNBRNP0NFSdRFO93WKXm9vXur7bP5491BfzTNdiLzMkqUWSFEwzkq5pK3iKpdcMFKQKtmk3EjSVjIHY1irucx1lSYV19wIaUpgU3Txu3cb_McAsV9sbNTQdcqBH-KCipLISjJZJPR8jw7tBsxiG-xGha_F39vYD4RQcLk |
| CitedBy_id | crossref_primary_10_2196_59520 crossref_primary_10_1016_j_ijmedinf_2020_104248 crossref_primary_10_1016_j_ijmedinf_2021_104643 crossref_primary_10_1016_j_amjcard_2019_06_032 crossref_primary_10_1016_j_ijnurstu_2025_105094 crossref_primary_10_1016_j_tox_2023_153431 crossref_primary_10_7861_clinmed_2022_0349 crossref_primary_10_1177_14799731231198865 crossref_primary_10_3389_fmed_2021_621861 crossref_primary_10_1097_MCC_0000000000001160 crossref_primary_10_1002_cpt_1034 crossref_primary_10_1016_j_jad_2022_07_045 crossref_primary_10_18632_oncotarget_24468 crossref_primary_10_1093_ajhp_zxab237 crossref_primary_10_1097_CCM_0000000000002105 crossref_primary_10_1097_ACO_0000000000000657 crossref_primary_10_1007_s10877_022_00874_6 crossref_primary_10_1002_clc_23541 crossref_primary_10_1016_j_ccc_2023_02_001 crossref_primary_10_1097_ALN_0000000000003843 crossref_primary_10_1111_nicc_70063 crossref_primary_10_1111_jocs_16060 crossref_primary_10_1371_journal_pone_0179641 crossref_primary_10_2196_44483 crossref_primary_10_1371_journal_pone_0318502 crossref_primary_10_1038_s41598_022_16195_2 crossref_primary_10_1001_jamanetworkopen_2024_38986 crossref_primary_10_1016_j_jacc_2018_03_521 crossref_primary_10_4187_respcare_08397 crossref_primary_10_1097_MLR_0000000000001140 crossref_primary_10_1109_ACCESS_2021_3070618 crossref_primary_10_1007_s41666_023_00128_3 crossref_primary_10_1016_j_resplu_2024_100628 crossref_primary_10_1186_s13049_020_00791_0 crossref_primary_10_1016_j_msksp_2025_103321 crossref_primary_10_1097_CCM_0000000000006845 crossref_primary_10_2196_medinform_8680 crossref_primary_10_7326_M19_0872 crossref_primary_10_1038_s41598_020_73740_7 crossref_primary_10_1186_s40635_020_00302_6 crossref_primary_10_3389_fmed_2021_810195 crossref_primary_10_3389_fonc_2022_816427 crossref_primary_10_1002_clc_23688 crossref_primary_10_1007_s11606_018_4335_8 crossref_primary_10_1371_journal_pone_0316452 crossref_primary_10_1016_j_jcrc_2019_09_024 crossref_primary_10_1136_bmjqs_2022_015275 crossref_primary_10_1016_j_prrv_2021_06_002 crossref_primary_10_1016_j_bpa_2020_12_013 crossref_primary_10_1186_s12877_025_05688_0 crossref_primary_10_1186_s12933_023_01741_7 crossref_primary_10_1213_ANE_0000000000005952 crossref_primary_10_3389_fpubh_2025_1505541 crossref_primary_10_1097_CCM_0000000000004982 crossref_primary_10_1097_CCM_0000000000003891 crossref_primary_10_1016_j_jcrc_2018_07_009 crossref_primary_10_1038_s41598_017_10558_w crossref_primary_10_1016_j_mayocpiqo_2025_100663 crossref_primary_10_1097_PCC_0000000000001184 crossref_primary_10_1186_s12885_022_09217_9 crossref_primary_10_1590_0034_7167_2021_0570 crossref_primary_10_1186_s13256_022_03385_9 crossref_primary_10_1007_s11831_023_09915_y crossref_primary_10_1097_CCM_0000000000004966 crossref_primary_10_1016_j_gerinurse_2025_103530 crossref_primary_10_1016_j_jtcvs_2024_11_003 crossref_primary_10_1007_s12012_021_09708_4 crossref_primary_10_1097_AIA_0000000000000479 crossref_primary_10_3389_fonc_2020_00832 crossref_primary_10_1177_08850666231166349 crossref_primary_10_1186_s13054_025_05484_7 crossref_primary_10_1186_s12872_022_02960_8 crossref_primary_10_1016_j_resuscitation_2019_04_007 crossref_primary_10_1016_j_jcjq_2018_04_014 crossref_primary_10_1111_nicc_70172 crossref_primary_10_1109_ACCESS_2020_3047186 crossref_primary_10_1016_j_jss_2018_03_028 crossref_primary_10_1177_10760296231171082 crossref_primary_10_3389_fnut_2022_740898 crossref_primary_10_1038_s41598_022_17916_3 crossref_primary_10_1088_1742_6596_1964_6_062076 crossref_primary_10_3390_diagnostics11071299 crossref_primary_10_1097_CCM_0000000000005837 crossref_primary_10_2196_26646 crossref_primary_10_25259_SNI_312_2023 crossref_primary_10_1007_s12672_025_03298_1 crossref_primary_10_1371_journal_pone_0205836 crossref_primary_10_1111_jnu_13001 crossref_primary_10_1016_j_asoc_2020_106612 crossref_primary_10_1186_s13054_018_2194_7 crossref_primary_10_1016_j_surg_2021_08_031 crossref_primary_10_1038_s41598_019_40192_7 crossref_primary_10_4103_jpcc_jpcc_21_25 crossref_primary_10_1177_23337214241279531 crossref_primary_10_1016_j_ijpharm_2022_122203 crossref_primary_10_1016_j_resuscitation_2021_08_024 crossref_primary_10_3389_fpsyt_2022_936283 crossref_primary_10_3390_s22166104 crossref_primary_10_1177_20552076241233135 crossref_primary_10_3390_jcm10071425 crossref_primary_10_1016_j_jtumed_2025_05_007 crossref_primary_10_1002_pros_24233 crossref_primary_10_1016_j_ijoa_2024_104288 crossref_primary_10_1371_journal_pone_0264184 crossref_primary_10_1007_s00330_020_07534_w crossref_primary_10_1016_j_jbi_2020_103410 crossref_primary_10_1016_j_ejso_2022_08_034 crossref_primary_10_1038_s41598_020_77548_3 crossref_primary_10_1080_10255842_2024_2427118 crossref_primary_10_1161_CIRCEP_117_005499 crossref_primary_10_1155_2022_8044550 crossref_primary_10_1038_s41598_025_06723_1 crossref_primary_10_1016_j_jbi_2020_103528 crossref_primary_10_1001_jamanetworkopen_2019_7447 crossref_primary_10_1097_SAP_0000000000004016 crossref_primary_10_1016_j_puhe_2025_105744 crossref_primary_10_1016_j_resuscitation_2018_05_013 crossref_primary_10_1093_jamia_ocaa295 crossref_primary_10_1093_jamia_ocaa296 crossref_primary_10_3389_fcvm_2022_964894 crossref_primary_10_3390_brainsci12091232 crossref_primary_10_1097_PCC_0000000000002414 crossref_primary_10_1007_s00395_023_00982_7 crossref_primary_10_1097_CCE_0000000000001116 crossref_primary_10_1016_j_ijnurstu_2022_104411 crossref_primary_10_1016_j_urolonc_2021_08_007 crossref_primary_10_1007_s00404_021_05994_z crossref_primary_10_1093_jamia_ocac109 crossref_primary_10_1097_CCE_0000000000000023 crossref_primary_10_1097_CCE_0000000000001232 crossref_primary_10_1016_j_resuscitation_2018_05_007 crossref_primary_10_1186_s12879_023_08547_8 crossref_primary_10_1055_s_0044_1780508 crossref_primary_10_1097_TP_0000000000003700 crossref_primary_10_1136_bmjopen_2023_082540 crossref_primary_10_1097_CCM_0000000000002648 crossref_primary_10_1093_jamiaopen_ooae033 crossref_primary_10_1016_j_bja_2021_07_016 crossref_primary_10_1097_CCE_0000000000000031 crossref_primary_10_3390_children5030038 crossref_primary_10_1016_j_bja_2024_10_045 crossref_primary_10_1016_j_heliyon_2024_e32655 crossref_primary_10_1161_JAHA_118_008678 crossref_primary_10_1007_s12065_023_00836_0 crossref_primary_10_1186_s13098_021_00711_x crossref_primary_10_1186_s12872_024_03723_3 crossref_primary_10_1097_MCC_0000000000000945 crossref_primary_10_1097_CCM_0000000000003803 crossref_primary_10_1016_j_aucc_2024_09_011 crossref_primary_10_1016_j_ijmedinf_2018_01_001 crossref_primary_10_1016_j_resuscitation_2019_06_045 crossref_primary_10_7717_peerj_11988 crossref_primary_10_3389_fneur_2022_791547 crossref_primary_10_1038_s41591_025_03609_7 crossref_primary_10_1016_j_bpa_2020_09_003 crossref_primary_10_1007_s10877_017_0054_7 crossref_primary_10_3390_jcm8091336 crossref_primary_10_15829_1560_4071_2021_4618 crossref_primary_10_1016_j_ajem_2022_10_011 crossref_primary_10_1016_j_ijnurstu_2023_104623 crossref_primary_10_1016_j_jcrc_2018_02_010 crossref_primary_10_1016_j_medine_2024_07_004 crossref_primary_10_1164_rccm_202002_0347OC crossref_primary_10_1016_j_ijmedinf_2021_104572 crossref_primary_10_1016_j_cmpb_2019_06_010 crossref_primary_10_3389_fmed_2025_1564545 crossref_primary_10_3390_biomedinformatics5030052 crossref_primary_10_1001_jamanetworkopen_2019_20733 crossref_primary_10_3389_fmed_2021_629671 crossref_primary_10_1186_s12912_021_00742_9 crossref_primary_10_1097_EA9_0000000000000014 crossref_primary_10_1016_j_compbiomed_2018_07_018 crossref_primary_10_1016_j_ijrobp_2018_08_010 crossref_primary_10_2147_COPD_S379700 crossref_primary_10_1007_s00134_017_4806_0 crossref_primary_10_1097_CCM_0000000000002620 crossref_primary_10_3389_fphar_2022_1027230 crossref_primary_10_1371_journal_pone_0224502 crossref_primary_10_3389_fonc_2023_1117420 crossref_primary_10_3390_jcm12051755 crossref_primary_10_1016_j_jcrc_2023_154353 crossref_primary_10_1136_bmjopen_2018_027750 crossref_primary_10_1186_s12911_020_01144_8 crossref_primary_10_1001_jamanetworkopen_2020_5191 crossref_primary_10_1097_NCQ_0000000000000600 crossref_primary_10_1097_CCE_0000000000001161 crossref_primary_10_1007_s10877_024_01235_1 crossref_primary_10_1016_j_jclinane_2023_111194 crossref_primary_10_2147_PPA_S294402 crossref_primary_10_3389_fmed_2022_1043589 crossref_primary_10_1016_j_amjsurg_2019_03_005 crossref_primary_10_1002_phar_2147 crossref_primary_10_1016_j_resuscitation_2020_05_044 crossref_primary_10_1161_JAHA_119_013924 crossref_primary_10_1186_s40560_025_00814_x crossref_primary_10_1177_1460458219894494 crossref_primary_10_3389_fpubh_2022_940905 crossref_primary_10_1371_journal_pone_0220640 crossref_primary_10_3390_jpm10030104 crossref_primary_10_1016_j_jclinepi_2020_03_002 crossref_primary_10_1007_s12551_018_0495_3 crossref_primary_10_12788_jhm_3151 crossref_primary_10_1016_j_jclinepi_2020_03_005 crossref_primary_10_1016_j_resplu_2020_100046 crossref_primary_10_2196_16349 crossref_primary_10_2196_30798 crossref_primary_10_1001_jamanetworkopen_2019_19396 crossref_primary_10_1097_CCE_0000000000000897 crossref_primary_10_2196_28209 crossref_primary_10_1097_CCM_0000000000006277 crossref_primary_10_1097_NUR_0000000000000644 crossref_primary_10_1177_1460458218813600 crossref_primary_10_2196_40474 crossref_primary_10_3390_biomedicines13010090 crossref_primary_10_3389_fpubh_2021_754348 crossref_primary_10_1038_s41598_023_42657_2 crossref_primary_10_1007_s12029_020_00544_3 crossref_primary_10_1186_s13613_019_0524_9 crossref_primary_10_1016_j_cnc_2021_05_010 crossref_primary_10_1177_1060028020959042 crossref_primary_10_1016_j_bja_2018_07_032 crossref_primary_10_3389_fonc_2025_1502404 crossref_primary_10_3748_wjg_v28_i32_4681 crossref_primary_10_3389_fneur_2025_1512297 crossref_primary_10_1089_end_2018_0035 crossref_primary_10_1007_s44254_023_00031_5 crossref_primary_10_1097_CCM_0000000000002926 crossref_primary_10_3390_jcm14124026 crossref_primary_10_1159_000543646 crossref_primary_10_4187_respcare_07500 crossref_primary_10_1016_j_resuscitation_2016_02_011 crossref_primary_10_1097_MD_0000000000035082 crossref_primary_10_1186_s12913_020_05148_y crossref_primary_10_1007_s11739_022_02988_w crossref_primary_10_1007_s12016_020_08805_6 crossref_primary_10_1097_DCC_0000000000000584 crossref_primary_10_2196_46807 crossref_primary_10_3390_jcm11195815 crossref_primary_10_1007_s10462_018_9625_3 crossref_primary_10_1007_s00586_025_08785_1 crossref_primary_10_1111_epi_17320 crossref_primary_10_1038_s41440_022_01081_1 crossref_primary_10_1007_s10462_021_09982_2 crossref_primary_10_3389_fneur_2020_539509 crossref_primary_10_1097_CCM_0000000000006243 crossref_primary_10_3389_fneur_2022_981752 crossref_primary_10_1097_MEG_0000000000002424 crossref_primary_10_1177_0044118X241227563 crossref_primary_10_1038_s41598_021_85157_x crossref_primary_10_1186_s12890_022_02031_w crossref_primary_10_1093_jamia_ocab111 crossref_primary_10_1002_acr_25013 crossref_primary_10_1007_s13311_020_00846_1 crossref_primary_10_1097_SLA_0000000000002665 crossref_primary_10_3390_medicina61091543 crossref_primary_10_1186_s13054_023_04609_0 crossref_primary_10_1186_s12911_020_01245_4 crossref_primary_10_1007_s00268_019_05087_8 crossref_primary_10_1186_s12905_025_03584_8 crossref_primary_10_1186_s12871_021_01543_y crossref_primary_10_1016_j_iccn_2025_104101 crossref_primary_10_1007_s00438_024_02217_3 crossref_primary_10_1016_j_medin_2024_06_014 crossref_primary_10_1016_j_aucc_2016_06_002 crossref_primary_10_1016_j_cct_2022_106767 crossref_primary_10_1016_j_cmpb_2021_106395 crossref_primary_10_1016_j_resuscitation_2020_11_020 crossref_primary_10_1530_ERP_18_0081 crossref_primary_10_3390_ijms23137132 crossref_primary_10_1097_CCM_0000000000001800 crossref_primary_10_1097_PCC_0000000000002965 crossref_primary_10_1007_s00134_018_5384_5 crossref_primary_10_4187_respcare_07405 crossref_primary_10_1016_j_surg_2018_06_022 crossref_primary_10_1002_nav_21929 crossref_primary_10_1155_2022_7896218 crossref_primary_10_3389_fpubh_2022_978338 crossref_primary_10_1016_j_jbi_2018_10_008 crossref_primary_10_3390_diagnostics13213380 crossref_primary_10_1164_rccm_202007_2700OC crossref_primary_10_1007_s10877_019_00415_8 crossref_primary_10_1007_s10877_021_00753_6 crossref_primary_10_1016_j_resuscitation_2024_110161 crossref_primary_10_1186_s12911_023_02323_z crossref_primary_10_1097_CCM_0000000000006137 crossref_primary_10_1007_s10877_021_00666_4 crossref_primary_10_1097_CCM_0000000000005286 crossref_primary_10_1186_s13054_019_2485_7 crossref_primary_10_1007_s00784_022_04742_0 crossref_primary_10_3389_fmed_2022_942356 crossref_primary_10_1016_j_ejim_2017_09_018 crossref_primary_10_1097_CRD_0000000000000708 crossref_primary_10_1186_s12890_020_1089_y crossref_primary_10_3389_fpubh_2022_939758 crossref_primary_10_1016_j_ejrad_2024_111508 crossref_primary_10_1186_s12884_022_04631_0 crossref_primary_10_1111_echo_14220 crossref_primary_10_3390_ijerph20085575 crossref_primary_10_1186_s13054_024_04860_z crossref_primary_10_3390_ijerph17030897 crossref_primary_10_1186_s40560_017_0261_9 crossref_primary_10_1016_j_medine_2019_03_010 crossref_primary_10_1080_02564602_2021_1936224 crossref_primary_10_1016_j_jclinepi_2019_02_004 crossref_primary_10_1007_s11886_023_01964_w crossref_primary_10_1016_j_medin_2019_03_011 crossref_primary_10_1016_j_resuscitation_2021_04_013 crossref_primary_10_2196_24246 crossref_primary_10_1097_CCM_0000000000003168 crossref_primary_10_1038_s41598_021_99628_8 crossref_primary_10_1016_j_ejim_2017_09_031 crossref_primary_10_1186_s13054_023_04437_2 crossref_primary_10_3390_medicina56090455 crossref_primary_10_1038_s41746_018_0029_1 crossref_primary_10_1093_ajcp_aqx078 crossref_primary_10_1002_ams2_70083 crossref_primary_10_1016_j_jss_2023_05_015 crossref_primary_10_1016_j_resplu_2021_100193 crossref_primary_10_1016_j_jpeds_2020_02_039 crossref_primary_10_1016_j_resplu_2021_100089 crossref_primary_10_1111_acem_14190 crossref_primary_10_1016_j_acra_2025_07_008 crossref_primary_10_1590_0034_7167_2021_0570pt crossref_primary_10_1016_j_ejim_2021_12_024 crossref_primary_10_1016_S2213_2600_17_30187_X crossref_primary_10_1097_CCM_0000000000006333 crossref_primary_10_1177_10760296241285565 crossref_primary_10_3390_healthcare11071008 crossref_primary_10_1016_j_chest_2018_04_037 crossref_primary_10_1371_journal_pone_0225229 crossref_primary_10_1177_1403494817736944 crossref_primary_10_1016_j_jhin_2021_02_025 crossref_primary_10_1111_jocn_17396 crossref_primary_10_1016_j_csbj_2021_10_025 crossref_primary_10_1111_jdi_13937 crossref_primary_10_1038_s41598_024_66952_8 crossref_primary_10_1108_IDD_02_2019_0013 crossref_primary_10_1186_s13756_021_00928_5 crossref_primary_10_1001_jamanetworkopen_2022_51849 crossref_primary_10_2196_29982 crossref_primary_10_1016_j_jval_2022_03_022 crossref_primary_10_1016_j_jenvman_2020_110956 crossref_primary_10_1097_PEC_0000000000001858 crossref_primary_10_1513_AnnalsATS_201710_787OC crossref_primary_10_1016_j_iswa_2023_200197 crossref_primary_10_1016_j_ijmedinf_2018_05_006 crossref_primary_10_3389_fcvm_2022_928948 crossref_primary_10_3390_forecast7030051 crossref_primary_10_1371_journal_pone_0199246 crossref_primary_10_1371_journal_pone_0206662 crossref_primary_10_1177_0194599818823200 crossref_primary_10_1097_CCM_0000000000003123 crossref_primary_10_1111_anae_16007 crossref_primary_10_1038_s41598_023_38858_4 crossref_primary_10_1016_j_chest_2022_02_001 crossref_primary_10_1177_03611981221141435 crossref_primary_10_1371_journal_pone_0169772 crossref_primary_10_1093_milmed_usx164 crossref_primary_10_2174_0118749445304699240416074458 crossref_primary_10_1097_HCO_0000000000000812 crossref_primary_10_1016_j_bjao_2022_100002 crossref_primary_10_1093_eurheartj_ehz902 crossref_primary_10_1016_j_bulcan_2021_12_005 crossref_primary_10_1186_s12911_022_02052_9 crossref_primary_10_1016_j_radonc_2025_111139 crossref_primary_10_2196_48244 crossref_primary_10_36930_40340711 crossref_primary_10_1007_s10439_020_02639_1 crossref_primary_10_1016_j_artmed_2018_05_005 crossref_primary_10_3389_fonc_2022_1049305 crossref_primary_10_1097_PTS_0000000000001069 crossref_primary_10_1016_j_ajem_2019_04_006 crossref_primary_10_1016_j_cmpb_2021_106568 crossref_primary_10_1136_bmj_m3216 crossref_primary_10_1038_s41598_022_05445_y crossref_primary_10_1080_10106049_2022_2106315 crossref_primary_10_1097_CCM_0000000000004236 crossref_primary_10_1111_ijcp_13685 crossref_primary_10_1515_dx_2024_0034 crossref_primary_10_1097_CCM_0000000000003148 crossref_primary_10_1097_CCE_0000000000000515 crossref_primary_10_2196_jmir_6025 crossref_primary_10_1183_13993003_01755_2019 crossref_primary_10_3389_fpubh_2022_947204 crossref_primary_10_1371_journal_pone_0262895 crossref_primary_10_1159_000546397 |
| ContentType | Journal Article |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1097/CCM.0000000000001571 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| 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: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1530-0293 |
| EndPage | 374 |
| ExternalDocumentID | 26771782 |
| Genre | Multicenter Study Journal Article Observational Study Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NIA NIH HHS grantid: K24 AG031326 – fundername: NCATS NIH HHS grantid: UL1 TR000430 – fundername: NHLBI NIH HHS grantid: K08 HL121080 – fundername: NCRR NIH HHS grantid: UL1 RR024999 |
| GroupedDBID | --- .-D .3C .55 .GJ .XZ .Z2 01R 0R~ 1J1 354 3O- 40H 4Q1 4Q2 4Q3 53G 5GY 5RE 5VS 6J9 6PF 71W 77Y 7O~ AAAAV AAAXR AAGIX AAHPQ AAIQE AAJCS AAMOA AAMTA AAQKA AAQQT AARTV AASCR AASOK AASXQ AAUEB AAWTL AAXQO AAYEP AAYOK ABASU ABBUW ABDIG ABJNI ABOCM ABPPZ ABVCZ ABXVJ ABXYN ABZAD ABZZY ACCJW ACDDN ACDOF ACEWG ACGFO ACGFS ACIJW ACILI ACLDA ACOAL ACWDW ACWRI ACXJB ACXNZ ACZKN ADFPA ADGGA ADHPY ADNKB AE3 AE6 AEBDS AEETU AENEX AFBFQ AFDTB AFEXH AFFNX AFMBP AFNMH AFSOK AFUWQ AGINI AHOMT AHQNM AHQVU AHRYX AHVBC AI. AIJEX AINUH AJCLO AJIOK AJNWD AJNYG AJZMW AKCTQ AKULP ALKUP ALMA_UNASSIGNED_HOLDINGS ALMTX AMJPA AMKUR AMNEI AOHHW AOQMC BOYCO BQLVK BS7 BYPQX C45 CGR CS3 CUY CVF DIWNM DU5 DUNZO E.X EBS ECM EEVPB EIF EJD ERAAH EX3 F2K F2L F2M F2N F5P FCALG FL- FW0 GNXGY GQDEL H0~ HLJTE HZ~ IKREB IKYAY IN~ IPNFZ J5H JF9 JG8 JK3 JK8 K-A K-F K8S KD2 KMI L-C L7B M18 N4W N9A NEJ NPM N~7 N~B N~M O9- OAG OAH OB4 OBH OCUKA ODA ODMTH ODZKP OHH OHT OHYEH OL1 OLB OLG OLH OLU OLV OLY OLZ ONV OPUJH ORVUJ OUVQU OVD OVDNE OVIDH OVLEI OVOZU OWBYB OWU OWV OWW OWX OWY OWZ OXXIT P-K P2P PONUX R58 RIG RLZ S4R S4S T8P TEORI TSPGW V2I VH1 VVN W3M WOQ WOW X3V X3W X7M XXN XYM YCJ YFH YOC YOJ ZCG ZFV ZGI ZXP ZY1 ZZMQN ~9M 7X8 ABPXF ADKSD ADSXY |
| ID | FETCH-LOGICAL-c4736-157be04425268f1ba5a38572040953015d80b983edd2bc583c9d8095c5d78d6e2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 450 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=00003246-201602000-00016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Thu Oct 02 17:44:35 EDT 2025 Thu Apr 03 07:11:09 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c4736-157be04425268f1ba5a38572040953015d80b983edd2bc583c9d8095c5d78d6e2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 |
| PMID | 26771782 |
| PQID | 1760898284 |
| PQPubID | 23479 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_1760898284 pubmed_primary_26771782 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-Feb 20160201 |
| PublicationDateYYYYMMDD | 2016-02-01 |
| PublicationDate_xml | – month: 02 year: 2016 text: 2016-Feb |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Critical care medicine |
| PublicationTitleAlternate | Crit Care Med |
| PublicationYear | 2016 |
| References | 25102315 - Ann Am Thorac Soc. 2014 Sep;11(7):1130-5 23295778 - Resuscitation. 2013 Apr;84(4):465-70 21169824 - Crit Care Med. 2011 Mar;39(3):469-73 22777999 - Biom J. 2012 Sep;54(5):657-73 25532820 - BMC Med Res Methodol. 2014;14:137 25068389 - Am J Respir Crit Care Med. 2014 Sep 15;190(6):606-10 25466337 - Lancet Respir Med. 2015 Jan;3(1):42-52 14610404 - J Urol. 2003 Dec;170(6 Pt 2):S6-9; discussion S10 20935559 - Crit Care Med. 2011 Jan;39(1):65-72 8254858 - JAMA. 1993 Dec 22-29;270(24):2957-63 22447632 - J Hosp Med. 2012 May-Jun;7(5):388-95 24361673 - Resuscitation. 2014 Mar;85(3):418-23 25637693 - Resuscitation. 2015 Apr;89:99-105 22052772 - Chest. 2012 May;141(5):1170-6 22026551 - BMC Med Res Methodol. 2011;11:143 1959406 - Chest. 1991 Dec;100(6):1619-36 9618776 - Stat Med. 1998 May 30;17(10):1169-86 20959788 - Crit Care Med. 2011 Jan;39(1):34-9 23440923 - J Hosp Med. 2013 May;8(5):236-42 11588210 - QJM. 2001 Oct;94(10):521-6 25089847 - Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55 11185421 - Artif Intell Med. 2000 Aug;20(1):59-75 22479598 - PLoS One. 2012;7(3):e34312 |
| References_xml | – reference: 22026551 - BMC Med Res Methodol. 2011;11:143 – reference: 24361673 - Resuscitation. 2014 Mar;85(3):418-23 – reference: 21169824 - Crit Care Med. 2011 Mar;39(3):469-73 – reference: 25089847 - Am J Respir Crit Care Med. 2014 Sep 15;190(6):649-55 – reference: 1959406 - Chest. 1991 Dec;100(6):1619-36 – reference: 11185421 - Artif Intell Med. 2000 Aug;20(1):59-75 – reference: 25532820 - BMC Med Res Methodol. 2014;14:137 – reference: 25466337 - Lancet Respir Med. 2015 Jan;3(1):42-52 – reference: 23295778 - Resuscitation. 2013 Apr;84(4):465-70 – reference: 25102315 - Ann Am Thorac Soc. 2014 Sep;11(7):1130-5 – reference: 25637693 - Resuscitation. 2015 Apr;89:99-105 – reference: 20959788 - Crit Care Med. 2011 Jan;39(1):34-9 – reference: 22052772 - Chest. 2012 May;141(5):1170-6 – reference: 11588210 - QJM. 2001 Oct;94(10):521-6 – reference: 20935559 - Crit Care Med. 2011 Jan;39(1):65-72 – reference: 14610404 - J Urol. 2003 Dec;170(6 Pt 2):S6-9; discussion S10 – reference: 22777999 - Biom J. 2012 Sep;54(5):657-73 – reference: 25068389 - Am J Respir Crit Care Med. 2014 Sep 15;190(6):606-10 – reference: 9618776 - Stat Med. 1998 May 30;17(10):1169-86 – reference: 22479598 - PLoS One. 2012;7(3):e34312 – reference: 22447632 - J Hosp Med. 2012 May-Jun;7(5):388-95 – reference: 23440923 - J Hosp Med. 2013 May;8(5):236-42 – reference: 8254858 - JAMA. 1993 Dec 22-29;270(24):2957-63 |
| SSID | ssj0014573 |
| Score | 2.6415987 |
| Snippet | Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different... OBJECTIVEMachine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 368 |
| SubjectTerms | Age Factors Cohort Studies Diagnostic Techniques and Procedures Heart Arrest - mortality Humans Intensive Care Units - organization & administration Logistic Models Machine Learning - utilization Models, Statistical Neural Networks (Computer) Risk Assessment ROC Curve Socioeconomic Factors Support Vector Machine Survival Analysis Time Factors Vital Signs |
| Title | Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/26771782 https://www.proquest.com/docview/1760898284 |
| Volume | 44 |
| WOSCitedRecordID | wos00003246-201602000-00016&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA7qinjx_VhfRPAatm3aJjmJVBcvXRZR3FvJq4sg7bpV7_5zJ23WPQmCpfRQ2lKmk_SbzDfzIXTliiUZD0sSyFKRmBtLFC0p0UrHKuaCWSFbsQk2GvHJRIz9glvjaZWLObGdqE2t3Rr5IGRpwAXEB_H17I041SiXXfUSGquoRwHKOK9mk2UWIU4YXZTLCTbIsrxrV-i3MGHh78Cy_cEMt__7ajtoy0NLfNP5wi5asdUe2sh98nwffbXFto6Naec4-9EfxHWJ85ZSabHvtjrFeSss3WBZGbh0SUzHD3baMWcrDHAXj-fu8Y46jX2H0Vd86xg2L961MOyAMfGzK-46QE_Du8fsnngFBqJjRlMCFlI2iGFcRykvQyUTSXnidG1cmzowoOGBEpxaYyKlE061gDMi0Ylh3KQ2OkRrVV3ZY4R1GkgbGcqNa9LmwizloKeQqTWAgVQfXS6MW4CHu7SFrGz90RRL8_bRUfeFilnXiqOIUgbxKI9O_nD3KdoEtOMp12eoV8L4tudoXX--vzTzi9Z14Dga59-N2tAi |
| linkProvider | ProQuest |
| 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=Multicenter+Comparison+of+Machine+Learning+Methods+and+Conventional+Regression+for+Predicting+Clinical+Deterioration+on+the+Wards&rft.jtitle=Critical+care+medicine&rft.au=Churpek%2C+Matthew+M&rft.au=Yuen%2C+Trevor+C&rft.au=Winslow%2C+Christopher&rft.au=Meltzer%2C+David+O&rft.date=2016-02-01&rft.eissn=1530-0293&rft.volume=44&rft.issue=2&rft.spage=368&rft_id=info:doi/10.1097%2FCCM.0000000000001571&rft_id=info%3Apmid%2F26771782&rft_id=info%3Apmid%2F26771782&rft.externalDocID=26771782 |