PhysioNet/CinC 2019 Challenge Results
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RankTeam nameTeam membersTitle of abstractUtility score on full test setUtility score on test set AUtility score on test set BUtility score on test set CAUROC on test set AAUROC on test set BAUROC on test set CAUPRC on test set AAUPRC on test set BAUPRC on test set CAccuracy on test set AAccuracy on test set BAccuracy on test set CF-measure on test set AF-measure on test set BF-measure on test set CExecution time on test set A (h:m:s)Number of successful entries submitted
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*SailOceanMeicheng Yang, Hongxiang Gao, Xingyao Wang, Yuwen Li, Jianqing Li, Chengyu LiuEarly Prediction of Sepsis Using a Sliding Window-based AdaBoost Learning and Bayesian Regression0.3640.4300.422-0.0480.8230.8590.8140.1060.1290.0710.7810.8830.7750.1230.1360.0505:49:4610
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1Can I get your signature?James Morrill, Andrey Kormilitzin, Alejo Nevado-Holgado, Sumanth Swaminathan, Sam Howison, Terry LyonsThe Signature-based Model for Early Detection of Sepsis from Electronic Health Records in the Intensive Care Unit0.3600.4330.434-0.1230.0000.0000.0000.0000.0000.0000.8280.8880.7560.1390.1400.04618:23:488
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2SepsydJohn Anda Du, Nadi Sadr, Philip de ChazalA Comparison of Neural Network Approaches for Sepsis Prediction0.3450.4090.396-0.0420.8110.8530.8050.1050.1190.0650.8190.9010.7850.1310.1420.0502:40:463
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*Yuanfang GuanYuanfang GuanEarly Sepsis Detection Using LightGBM0.3400.4220.410-0.1660.8150.8450.7900.1080.1160.0810.8160.8850.7380.1320.1330.04416:50:148
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3SeparatrixMorteza Zabihi, Serkan Kiranyaz, Moncef GabboujSepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models0.3390.4220.395-0.1460.8140.8440.7930.1020.1100.0580.8030.8820.7650.1280.1300.0447:18:169
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4FlyingBubbleXiang Li, Yanni Kang, Xiaoyu Jia, Junmei Wang, Guotong XieTASP: A Time-phased Model for Sepsis Prediction0.3370.4200.401-0.1560.8130.8550.7990.1080.1170.0730.7980.8780.7480.1260.1290.04421:17:039
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5CTL-TeamJanmajay Singh, Kentaro Oshiro, Raghava Krishnan, Masahiro Sato, Tomoko Ohkuma, Noriji KatoUtilizing Informative Missingness for Early Prediction of Sepsis0.3370.4010.407-0.0940.8060.8460.8050.1010.1160.0560.7970.8910.7800.1220.1370.0470:43:576
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6SBUIbrahim HammoudEarly Prediction of Sepsis using Gradient Boosting Decision Trees With Custom Label Weighting0.3320.4080.402-0.1540.8090.8530.8040.1010.1200.0740.8300.9020.7480.1350.1440.0441:20:504
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7Ping An Health TechnologyJiawen XiaoEarly Prediction of Sepsis from Clinical Data Using XGBoost0.3310.4140.400-0.1820.8120.8570.8010.1040.1230.0750.7960.8770.7400.1240.1270.0424:47:3310
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*UAlbertaHumza HaiderUsing Missing Indicators and Difference Features to Predict Sepsis with XGBoost0.3290.3960.375-0.0600.8060.8380.8240.1030.1110.0710.8480.9180.7710.1410.1520.0509:19:313
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8prnaJonathan Rubin, Yale Chang, Gregory Boverman, Shruti Vij, Saman Parvaneh, Asif Rahman, Annamalai NatarajanA Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series0.3280.4110.389-0.1590.1650.1230.0920.0130.0090.0030.8070.8780.7270.1270.1260.04522:03:537
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9Antanas KascenasAntanas Kascenas, Alison O'Neil0.3230.4060.397-0.1950.8110.8450.8050.1080.1330.0750.7810.8670.7150.1180.1210.04314:43:233
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10NN-MIHNaoki Nonaka, Jun SeitaDemographic Information Initialized Stacked Gated Recurrent Unit for an Early Prediction of Sepsis0.3230.4140.373-0.1740.8120.8280.7960.0980.0770.0330.8230.8870.7260.1340.1270.04420:58:484
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11RadAsadiSepideh Rezaeirad, Atefeh Baniasadi, Mohammad Ghassemi, Habil Zare0.3230.3820.3350.0480.7170.7230.7180.0510.0430.0220.8120.8850.8270.1240.1190.05721:25:328
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12SFAASajad Mousavi0.3230.4050.376-0.1480.8030.8390.7930.0980.1050.0710.7890.8770.7510.1210.1230.0440:35:395
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13PKU_DLIBLuchen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming ZhangEarly Prediction of Sepsis from Clinical Data via Heterogeneous Event Aggregation0.3210.4020.386-0.1690.8100.8380.8120.1040.1150.0660.7940.8740.7280.1220.1230.0440:19:5110
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14SOS: Searching for SepsisBen Sweely, Austin Park, Lia Winter, Longjian Liu, Xiaopeng ZhaoTime-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development0.3140.3990.392-0.2200.0000.0000.0000.0000.0000.0000.8370.8970.7350.1370.1380.0416:35:416
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15CQUPT_Just_TryYongchao Wang, Bin Xiao, Xiuli Bi, Weisheng Li, Junhui Zhang, Xu MaPrediction of Sepsis from Clinical Data Using LSTM and XGBoost0.3130.3920.381-0.1740.8020.8490.7710.1010.1100.0520.8520.9110.7640.1420.1460.04213:06:586
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16UCAS_DataMinerZhengling He, Xianxiang Chen, Zhen Fang, Chenshuo Wang, Li Jiang, Zhongkai Tong, Zhongrui Bai, Yichen Pan, Yueqi LiEarly Sepsis Prediction Using Ensemble Learning with Features Extracted from LSTM Recurrent Neural Network0.3130.4060.373-0.2150.8160.8470.7980.1030.0940.0580.7460.8500.7140.1110.1120.04213:19:039
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17IADIBenjamin Roussel, Julien OsterA Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU0.3090.3870.365-0.1480.7920.8170.8010.0940.1040.0640.8220.8900.7410.1280.1280.0454:39:246
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18ShivpatidarShivnarayan PatidarDiagnosis of Sepsis Using Ratio Based Features0.3090.3900.386-0.2120.0000.0000.0000.0000.0000.0000.8290.8900.7320.1320.1320.04115:57:2210
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19R&HopeLu Meng0.3040.3740.344-0.0800.8000.8260.7870.1020.1130.0700.8860.9250.7860.1600.1500.0476:48:254
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20UVA CAMADouglas Lake0.3030.4020.318-0.1450.4050.4510.2750.0370.0310.0080.8300.9140.7570.1340.1330.0440:02:058
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21The Septic Think TankSimon Lyra, Steffen Leonhardt, Christoph Hoog AntinkEarly Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data0.2960.3720.378-0.2180.7880.8280.7800.0830.0890.0430.8080.8920.7300.1210.1320.0417:28:403
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?UCAS_BigBirdUnknown0.2950.3730.360-0.1790.7820.8240.7710.0930.0910.0510.8410.8880.7620.1340.1260.0410:02:076
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22vnByeongTak Lee, KyungJae Cho, Oyeon KwonStrategies to improve the performance of neural networks for early detection of sepsis0.2910.3870.351-0.2510.7930.8120.7710.0920.0830.0380.8250.8890.7220.1290.1230.0397:25:058
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23404: Sepsis not foundKilin Shi, Sven Schellenberger, Jan Philipp WiedemannAn Ensemble LSTM Architecture for Clinical Sepsis Detection0.2900.3690.346-0.1720.7890.8070.7970.0850.0720.0750.7720.8800.7360.1110.1190.04322:50:469
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24Infolab USCLuan Tran, Manh Nguyen, Cyrus ShahabiRepresentation Learning for Early Sepsis Prediction0.2840.3780.347-0.2620.7890.8220.7960.0930.1010.0560.8360.8940.7090.1310.1250.0398:46:089
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?s(k)epticTanuka Bhattacharjee, Sakyajit Bhattacharya, Varsha Sharma, Anirban Dutta Choudhury, Sunil Kumar Kopparapu, Rupayan Chakraborty, Upasana Tiwari, Murali Poduval, Sundeep Khandelwal, Kayapanda Muthana MandanaEarly Sepsis Prediction by Cascaded Classification of Multi-Modal Clinical Parameters0.2820.3710.343-0.2300.0000.0000.0000.0000.0000.0000.8270.8980.7430.1270.1280.03916:50:353
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25njueduQiang Yu, Xiaolin Huang, Cheng Wang, Qiyuan Wang, Yi Zhang, Yuqi ZhangUsing Features Extracted from Vital Time Series for Early Prediction of Sepsis0.2820.4010.278-0.2070.7980.7460.7160.0970.0660.0470.8350.9120.7650.1360.1220.0395:47:352
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26ECGuru10Tomas Vicar, Jakub Hejc, Petra Novotna, Marina Ronzhina, Radovan SmisekSepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss0.2810.3720.358-0.2800.7880.8030.7770.0900.0880.0570.8570.8940.7060.1400.1280.03919:23:401
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27ISIBrnoNejedly Petr, Plesinger Filip, Viscor Ivo, Halamek Josef, Jurak PavelPrediction of Sepsis Using LSTM with Hyperparameter Optimization with a Genetic Algorithm0.2780.3610.327-0.1820.7740.8050.7790.0870.0640.0480.8180.8860.7580.1220.1180.0410:50:096
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28QLabCongmin Xu, Peng Qiu, Kuang ChenEarly Prediction of Sepsis Using LSTM0.2700.3420.324-0.1610.0000.0000.0000.0000.0000.0000.8930.9120.7720.1560.1320.0420:43:343
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29UHN_rand_num_generatorOsvald Nitski, Yuchen Wang, Augustin Toma, Bo Wang,0.2690.3700.316-0.2630.7770.7800.7880.0910.0700.0780.7930.8930.6880.1150.1180.0401:01:516
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30SepsisFinderChloé Pou-Prom, Zhen Yang, Maitreyee Sidhaye, David DaiDevelopment of a Sepsis Early Warning Indicator0.2660.3780.315-0.3180.7900.8070.7640.0940.0750.0570.7740.8530.6940.1130.1040.03711:29:417
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31Terminator_CUETFahim Mahmud, Naqib Sad Pathan0.2640.3490.337-0.2590.7670.8110.7860.0880.0740.0440.7910.8800.7150.1120.1160.0390:32:304
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32Doctor WhoMichael Moor, Max Horn0.2590.3560.320-0.2850.7800.8160.7950.0770.0850.0440.7560.8530.6850.1060.1060.04014:38:566
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33AI4SepsisAnamika Paul Rupa, Al Amin, Sanjay PurushothamBenchmark of Machine Learning Models for Early Sepsis Prediction0.2550.3440.314-0.2580.7560.7870.7440.0860.0690.0350.8010.8810.7300.1140.1120.0380:19:334
40
?ucas-starUnknown0.2530.3130.2520.0030.7550.7780.7870.0740.0620.0490.7890.8590.8100.1060.0950.0532:27:128
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*CIBIMOliver Carr, Stefan Bostock, Nicolas Basty, John Prince, Kirubin Pillay, Navin Cooray, Maarten De VosNovelty Detection for the Early Prediction of Sepsis0.2510.3180.324-0.2010.0000.0000.0000.0000.0000.0000.6940.8560.7030.0910.1060.04315:50:472
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34BRIC_LBMohammed Baydoun, Lise Safatly, Hassan Ghaziri, Ali ElHajjConvolutional Neural Networks Based Model to Provide Early Prediction of Sepsis from Clinical Data0.2500.3760.338-0.4810.7750.7970.6000.0630.0600.0140.8050.8790.6880.1210.1160.0301:13:096
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35UBC - DHILRoshan Pawar, Jeffrey Bone, Mark Ansermino, Matthias GörgesAn Algorithm for Early Detection of Sepsis Using Traditional Statistical Regression Modeling0.2490.2960.2680.0070.7470.7600.7830.0720.0670.0880.7950.8890.8150.1040.1070.0542:41:513
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36ABCZhaowei Zhu, Zhuoyang Xu, Tingting ZhaoExtreme Gradient Boosting Method for Early Detection of Sepsis0.2470.3630.278-0.3140.7860.8020.7810.0940.0870.0530.7790.8320.6850.1120.0920.0388:37:419
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37ESLDionisije Sopic, Tomas Teijeiro, Amir Aminifar, David AtienzaA Real-Time Technique for Early Prediction of Sepsis Using Wearable Devices0.2450.3140.296-0.1630.7450.7750.7560.0790.0720.0390.8410.8930.7590.1210.1150.04317:20:044
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38WIN-UABEdwar Macias, Guillem Boquet, Antoni Morell, Javier Serrano, Jose Lopez Vicario, Jose IbeasNovel Imputing Method for the Early Prediction of Sepsis in ICU Using Deep Learning Techniques0.2410.3440.267-0.2470.7610.7660.7620.0840.0640.0520.8340.8660.7300.1240.0980.0392:09:223
47
39AlgTeamReza Firoozabadi, Saeed BabaeizadehAn Ensemble of Bagged Decision Trees for Early Prediction of Sepsis0.2400.3350.268-0.2260.7640.7680.7410.0840.0550.0330.8710.9120.7540.1390.1180.03914:23:219
48
40Leicester FoxXin Li, Fernando Schlindwein, G Andre NgConvolutional and Recurrent Neural Networks for Early Detection of Sepsis using Hourly Physiological Data from Patients in Intensive Care Unit0.2370.2880.2390.0140.7450.7680.7380.0790.0720.0380.9060.9340.8530.1510.1270.0531:11:3610
49
41ywangdaWang YiwenA Large Margin Deep Neural Network for Sepsis Classification0.2330.3270.267-0.2430.5390.5170.6250.0670.0550.0800.8020.8550.7160.1110.0940.0401:24:296
50
42pqlabYuhan Zhou0.2310.2870.260-0.0730.7050.7080.7320.0740.0610.0520.8920.9300.8260.1400.1300.0462:19:505
51
43USF-Sepsis-PhysSoodabeh Sarafrazi, Chiral Mehta, Rohini Choudhari, Himanshi Mehta, Patricia Francis-LyonCracking the “Sepsis” Code: Assessing Time Series Nature of EHR Data, and Using Deep Learning for Early Sepsis Prediction0.2170.3030.348-0.4480.8040.8410.8030.1000.1060.0670.6020.8060.6100.0820.0970.03616:48:081
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44UM AntisepticSardar Ansari0.2150.2880.268-0.2120.7190.7660.7540.0610.0480.0290.8110.8580.7490.1060.0960.0400:14:131
53
45AvivInnovationTzvika Aviv0.2020.2490.210-0.0120.6270.5900.6310.0630.0420.0330.9370.9370.8560.1710.1200.0510:19:2610
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46The memristive agentsVasileios Athanasiou, Zoran KonkoliMemristor Models for Early Detection of Sepsis in ICU Patients0.2000.2700.236-0.1760.6240.6210.6820.0660.0450.0330.9170.9130.7820.1550.1100.0400:03:496
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47OneMoreSecondKai Wang, Lei Zuo, Yanxuan LiSample-and-hold/mean Imputation and XGBoost for Sepsis Prediction0.1950.3050.223-0.3320.3510.3530.2450.0270.0190.0060.8470.8730.7220.1210.0930.0347:15:153
56
*Rx-LREdward Ho, Cathy Ong-Ly, Alex ZhouDeveloping an Interpretable Predictive Model for Early Diagnosis of Sepsis Using Automatic Feature Extraction0.1940.2470.198-0.0370.6830.6870.7310.0680.0490.0930.8890.9040.8190.1250.0970.0490:05:551
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48Team_TesseractShailesh Nirgudkar, Tianyu DingEarly Detection of Sepsis Using Ensemblers0.1920.2740.233-0.2460.5790.6210.6260.0570.0380.0140.9290.9280.7650.1710.1200.0360:45:183
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49AI-NeuroimmuneKaveh SamieePrediction of Sepsis in Intensive Care Unit Using Electronic Medical Records and Convolutional Bidirectional Recurrent Neural Networks0.1920.3250.243-0.4850.7530.7650.7610.0840.0640.0590.8460.8840.6430.1250.0990.03212:19:392
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50IMSATSaman Noorzadeh, Shahrooz FaghihRoohi, Mojtaba ZareiA Comparative Analysis of HMM and CRF for Early Prediction of Sepsis0.1900.2740.231-0.2610.4630.4220.3470.0450.0270.0110.9260.9260.7600.1670.1170.0360:01:023
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51PLUXMiquel Alfaras, Rui Varandas, Hugo GamboaRing-Topology Echo State Networks for ICU Sepsis Classification0.1880.2060.2140.0550.6670.6880.7020.0680.0510.0780.9430.9420.8770.1600.1270.0580:24:125
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52LDBRLakshman Narayanaswamy, Devendra Garg, Bhargavi Narra, Ramkumar NarayanswamyMachine Learning Algorithmic and System Level Considerations for Early Prediction of Sepsis0.1790.1990.1930.0620.6290.6330.7010.0610.0430.0690.9380.9370.8810.1490.1140.0598:52:381
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53KrissInes KrissaaneAnomaly Detection Semi-supervised Framework for Sepsis Treatment0.1770.2510.169-0.1170.5830.5620.6140.0320.0180.0160.8460.8650.7900.1080.0800.0440:37:1810
63
?WMLUnknown0.1640.1910.289-0.2410.6880.7760.7400.0520.0600.0310.7260.8560.7340.0790.1010.03912:26:301
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54PhysioNet ExamplePhysioNet TeamEarly Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 20190.1590.2200.0990.0440.6920.6980.7630.0600.0530.0760.7400.7930.8250.0840.0650.0570:01:423
65
?XLS-IMECASUnknown0.1580.2550.184-0.3070.2200.2600.1430.0160.0130.0050.7970.8440.7060.0970.0800.03714:20:411
66
55CIS2216Shenda Hong, Junyuan Shang, Meng Wu, Yuxi Zhou, Yongyue Sun, Yen Hsiu Chou, Moxian Song, Hongyan LiEarly Sepsis Prediction with Deep Recurrent Reinforcement Learning0.1550.1560.1360.1930.0410.0400.0600.0050.0030.0030.9550.9580.9370.1510.1130.0940:49:383
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56anniAnnie Zheng0.1540.2140.254-0.3290.7010.7750.7700.0460.0550.0260.6840.8380.6830.0790.0920.0380:07:314
68
57TricogManmay Nakhashi, Anoop Toffy, Achuth PV, Lingaselvan PalanichamyEarly Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs0.1420.2490.152-0.3270.7210.7360.6690.0450.0300.0120.8550.8660.7420.1130.0800.03322:01:303
69
58USSTWenjie Cai, Danqin Hu, Shuaicong Hu0.1330.2820.200-0.6520.1570.1800.1250.0110.0090.0040.5890.7220.5400.0800.0730.0328:56:133
70
59ARULInduparkavi Murugesan, Karthikeyan Murugesan, Lingeshwaran Balasubramanian, Malathi MurugesanInterpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis0.1310.1390.0940.1820.3780.3050.2560.0170.0090.0060.9440.9760.9620.1250.1170.11310:37:351
71
60TU Dresden - IBMTMatthieu Scherpf, Miriam Goldammer, Hagen Malberg, Felix GräßerSepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine0.1140.1940.106-0.2040.0000.0000.0000.0000.0000.0000.6750.7830.7170.0750.0650.0421:16:411
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61Sepsis ReSepsionPo-Ya Hsu, Chester HoltzA Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data0.0760.2270.036-0.4570.4180.4420.4610.0180.0120.0080.7790.7820.6720.0890.0570.0313:54:153
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62Purdue UniversityAaron Althoff0.0660.1610.062-0.3230.6010.5890.6230.0290.0200.0120.7880.8300.7430.0800.0610.03410:22:441
74
63Amini-Univ-TehranMorteza AminiEarly Prediction of Sepsis from Clinical Data Using a Specialized Hidden Markov Model0.0300.0550.013-0.0340.5760.5720.6790.0350.0240.0300.9100.9080.8920.0660.0510.0461:29:375
75
64PyParivash FahamAzad, Ruhallah Amandi, Mohammad Farhadi0.0250.0220.0210.0460.4150.4150.4180.0200.0130.0080.9760.9840.9900.0440.0430.0820:45:431
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65Whitaker's WarriorsErik Gilbertson, Khristian Jones, Abigail Strohl, Bradley WhitakerEarly Detection of Sepsis Using Feature Selection, Feature Extraction, and Neural Network Classification0.0220.0400.006-0.0160.4200.4690.4770.0220.0160.0100.9320.9590.9630.0630.0420.0371:17:222
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66VGTUVytautas Abromavicius, Artūras SerackisSepsis Prediction Model Based on Vital Signs Related Features0.0140.0360.013-0.0780.0000.0000.0000.0000.0000.0000.8830.9360.9450.0600.0540.0257:27:113
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67RoBustoDiogo NunesTrend and Filtered State Extraction through Savitzky-Golay Filtering for the Early Detection of Sepsis Events0.0140.0620.156-0.5180.3480.5160.1670.0200.0310.0050.7640.8900.6630.0610.0820.0298:58:381
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68SHODHAruna DeogireA Low Dimensional Algorithm for Detection of Sepsis from Electronic Medical Record Data0.0130.0120.0130.0170.5290.5060.5860.0230.0150.0160.9680.9780.9840.0330.0400.0480:02:387
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69UND_BERCLABSoufiane Chami, Guragain Bijay, Hoffmann Bradley, Majumder Shubha, Naima Kaabouche, Kouhyar TavakolianComparative Study of Light-GBM and a Combination of Survival Analysis with Deep Learning for Early Detection of Sepsis0.0050.1720.000-0.6820.2720.2640.1460.0180.0120.0050.7810.7680.6020.0790.0520.0260:02:263
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70Sepsis' debuggerAya Tello, Yazan Shikhani0.0020.0150.004-0.0620.0720.0850.0930.0030.0030.0020.9200.9420.9430.0490.0470.0315:12:011
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71claguetClementine Aguet, Jérôme Van Zaen, Mathieu LemaySepsis Detection Using Missingness Information0.0000.0000.0000.0000.5000.4970.4940.0220.0140.0090.9780.9860.9910.0000.0000.00013:29:575
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71CRASHersMarco AF Pimentel, Adam Mahdi, Oliver Redfern, Mauro SantosUncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU0.0000.0000.0000.0000.5000.5000.5000.0220.0140.0090.9780.0220.9910.0000.0000.00018:00:000
84
71PIPIXiaofeng Tang0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9780.9860.9910.0000.0000.00013:11:362
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74ScuDicaLabYao Chen, Jiancheng LvContextual LSTM (CLSTM) Models for Early Prediction of Sepsis-0.023-0.003-0.032-0.0830.6260.6230.6010.0300.0190.0110.9610.9600.9570.0240.0170.0133:58:311
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75cinc_sepsis_passMengsha Fu, Jiabin Yuan, Menglin Lu, Pengfei Hong, Mei Zeng, Zhenhua XuAn Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data-0.0360.142-0.086-0.6650.7020.7130.7380.0480.0370.0330.5390.6310.5580.0660.0490.0303:24:205
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76CHKucharski D, Pabian M, Rzepka D-0.1210.104-0.350-0.5270.6410.6030.6780.0310.0170.0150.5830.5050.6500.0640.0380.03013:47:201
88
77Kent Ridge AIShiyu Liu, Ming Lun Ong, Kar Kin Mun, Jia Yao, Mehul MotaniEarly Prediction of Sepsis via SMOTE Upsampling and Mutual Information based Downsampling-0.164-0.047-0.288-0.3610.5100.5260.5600.0230.0150.0110.7730.7000.7950.0440.0300.0250:52:137
89
78The Sepsis DetectivesAkram Mohammed, Franco van Wyk, Anahita Khojandi, Rishikesan KamaleswaranWhen to Start Sepsis Bundle? A Machine Learning Approach to Earlier Detection Using Electronic Medical Records-0.841-0.321-1.146-2.3070.5670.3790.4960.0290.0110.0090.0310.1660.0150.0420.0200.01713:57:104
90
xA_UNSW_SepsisUnknownN/A0
91
xB-SecurPeter Doggart, Megan RutherfordRandomly under Sampled Boosted Tree for Predicting Sepsis from Intensive Care Unit DatabasesN/A0
92
xBolloknoon InstituteUnknownN/A0
93
xMain LabUnknownN/A0
94
xMELABUnknownN/A0
95
xNCHC-PhysionetTim HuangN/A0
96
xSepsis_2GMarcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu Sinha, Sven-Olaf KuhnTime-specific Metalearners for the Early Prediction of SepsisN/A0
97
xSUNYanbo Xu, Siddharth Biswal, Rahul Duggal, Yu Jing, Jimeng SunHand Crafted Features and an LSTM for Predicting SepsisN/A0
98
xthb100UnknownN/A0
99
xThe Way Code Should BeClare Bates CongdonA Naïve Neural-Net Approach to Prediction of Sepsis with Time-Series DataN/A0
100
xUlsterTeamPardis Biglarbeigi, Donal McLaughlin, Khaleed Rjoob, Abdullah Abdullah, Niamh McCallan, Alicja Jasinska-Piadlo, Raymond Bond, Dewar Finlay, Mark Kok Yew Ng, Alan Kennedy, James McLaughlinEarly prediction of sepsis considering Early Warning Scoring systemsN/A0
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