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Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
Citations
To refer to the 2019 Challenge, please cite:
Challenge results
The conference papers for Computing in Cardiology 2019 are available on the CinC and IEEE websites.
The ranks of the teams and their papers and entry code are available in the table below.
| Rank | Team Name | Team Member(s) | Code | Conference Paper | Journal Paper |
|---|---|---|---|---|---|
| 1 | Can I get your signature? | James Morrill, Andrey Kormilitzin, Alejo Nevado-Holgado, Sumanth Swaminathan, Sam Howison, Terry Lyons | Link (13.5 MB) | The Signature-Based Model for Early Detection of Sepsis from Electronic Health Records in the Intensive Care Unit | Utilization of the Signature Method to Identify the Early Onset of Sepsis From Multivariate Physiological Time Series in Critical Care Monitoring |
| 2 | Sepsyd | John Anda Du, Nadi Sadr, Philip de Chazal | Link (0.2 MB) | Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees | |
| 3 | Separatrix | Morteza Zabihi, Serkan Kiranyaz, Moncef Gabbouj | Link (3 MB) | Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models | |
| 4 | FlyingBubble | Xiang Li, Yanni Kang, Xiaoyu Jia, Junmei Wang, Guotong Xie | Link (8.2 MB) | TASP: A Time-Phased Model for Sepsis Prediction | A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care |
| 5 | CTL-Team | Janmajay Singh, Kentaro Oshiro, Raghava Krishnan, Masahiro Sato, Tomoko Ohkuma, Noriji Kato | Link (0.8 MB) | Utilizing Informative Missingness for Early Prediction of Sepsis | |
| 6 | SBU | Ibrahim Hammoud, IV Ramakrishnan, Mark Henry | Link (0.7 MB) | Early Prediction of Sepsis Using Gradient Boosting Decision Trees with Optimal Sample Weighting | |
| 7 | Ping An Health Technology | Xiang Li, Yanni Kang, Xiaoyu Jia, Junmei Wang, Guotong Xie | Link (1.5 MB) | ||
| 8 | prna | Yale Chang, Jonathan Rubin, Gregory Boverman, Shruti Vij, Asif Rahman, Annamalai Natarajan, Saman Parvaneh | Link (9.2 MB) | A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series | |
| 9 | Antanas Kascenas | Antanas Kascenas, Alison O’Neil | Link (7 MB) | ||
| 10 | NN-MIH | Naoki Nonaka, Jun Seita | Link (0.9 MB) | Demographic Information Initialized Stacked Gated Recurrent Unit for an Early Prediction of Sepsis | |
| 11 | RadAsadi | Sepideh Rezaeirad, Atefeh Baniasadi, Mohammad Ghassemi, Habil Zare | Link (0.03 MB) | Two-Step Imputation and AdaBoost-Based Classification for Early Prediction of Sepsis on Imbalanced Clinical Data | |
| 12 | SFAA | Sajad Mousavi | Link (0.1 MB) | ||
| 13 | PKU_DLIB | Luchen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming Zhang | Link (0.6 MB) | Early Prediction of Sepsis From Clinical Data via Heterogeneous Event Aggregation | |
| 14 | SOS: Searching for Sepsis | Ben Sweely, Austin Park, Lia Winter, Longjian Liu, Xiaopeng Zhao | Link (1 MB) | Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development | |
| 15 | CQUPT_Just_Try | Yongchao Wang, Bin Xiao, Xiuli Bi, Weisheng Li, Junhui Zhang, Xu Ma | Link (17.6 MB) | Prediction of Sepsis from Clinical Data Using Long Short-Term Memory and eXtreme Gradient Boosting | |
| 16 | UCAS_DataMiner | Zhengling He, Xianxiang Chen, Zhen Fang, Chenshuo Wang, Li Jiang, Zhongkai Tong, Zhongrui Bai, Yichen Pan, Yueqi Li | Link (23.1 MB) | Early Sepsis Prediction Using Ensemble Learning with Features Extracted from LSTM Recurrent Neural Network | Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records |
| 17 | IADI | Benjamin Roussel, Julien Oster | Link (8.6 MB) | A Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU | |
| 18 | Shivpatidar | Shivnarayan Patidar | Link (1.5 MB) | Diagnosis of Sepsis Using Ratio Based Features | Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features |
| 19 | R&Hope | Lu Meng | Link (6.1 MB) | ||
| 20 | UVA CAMA | Douglas Lake | Link (0.01 MB) | ||
| 21 | The Septic Think Tank | Simon Lyra, Steffen Leonhardt, Christoph Hoog Antink | Link (1 MB) | Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data | |
| 22 | vn | Byeong Tak Lee, KyungJae Cho, Oyeon Kwon, Yeha Lee | Link (46.2MB) | Improving the Performance of a Neural Network for Early Prediction of Sepsis | Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record |
| 23 | 404: Sepsis not found | Sven Schellenberger, Kilin Shi, Jan Philipp Wiedemann, Fabian Lurz, Robert Weigel, Alexander Koelpin | Link (86.4 MB) | An Ensemble LSTM Architecture for Clinical Sepsis Detection | |
| 24 | Infolab USC | Luan Tran, Cyrus Shahabi, Manh Nguyen | Link (57.4 MB) | Representation Learning for Early Sepsis Prediction | |
| 25 | njuedu | Qiang Yu, Xiaolin Huang, Cheng Wang, Qiyuan Wang, Yi Zhang, Yuqi Zhang | Link (164.3 MB) | Using Features Extracted from Vital Time Series for Early Prediction of Sepsis | |
| 26 | ECGuru10 | Tomas Vicar, Jakub Hejc, Petra Novotna, Marina Ronzhina, Radovan Smisek | Link (46.5 MB) | Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss | |
| 27 | ISIBrno | Nejedly Petr, Plesinger Filip, Viscor Ivo, Halamek Josef, Jurak Pavel | Link (0.7 MB) | Prediction of Sepsis Using LSTM with Hyperparameter Optimization with a Genetic Algorithm | |
| 28 | QLab | Congmin Xu, Peng Qiu, Kuang Chen | Link (2 MB) | Early Prediction of Sepsis Using LSTM | |
| 29 | UHN_rand_num_generator | Osvald Nitski, Yuchen Wang, Augustin Toma, Bo Wang | Link (0.4 MB) | ||
| 30 | SepsisFinder | Chloé Pou-Prom, Zhen Yang, Maitreyee Sidhaye, David Dai | Link (63.3MB) | Development of a Sepsis Early Warning Indicator | |
| 31 | Terminator_CUET | Fahim Mahmud, Naqib Sad Pathan | Link (.5 MB) | ||
| 32 | Doctor Who | Michael Moor, Max Horn | Link (8 MB) | ||
| 33 | AI4Sepsis | Anamika Paul Rupa, Al Amin, Sanjay Purushotham | Link (67.1 MB) | Benchmark of Machine Learning Models for Early Sepsis Prediction | |
| 34 | BRIC_LB | Mohammed Baydoun, Lise Safatly, Hassan Ghaziri, Ali ElHajj | Link (42.9 MB) | Convolutional Neural Networks Based Model to Provide Early Prediction of Sepsis from Clinical Data | |
| 35 | UBC - DHIL | Roshan Pawar, Jeffrey Bone, Mark Ansermino, Matthias Görges | Link (0.2 MB) | An Algorithm for Early Detection of Sepsis Using Traditional Statistical Regression Modeling | |
| 36 | ABC | Zhaowei Zhu, Zhuoyang Xu, Tingting Zhao | Link (8.6 MB) | Extreme Gradient Boosting Method for Early Detection of Sepsis | |
| 37 | ESL | Dionisije Sopic, Tomas Teijeiro, Amir Aminifar, David Atienza | Link (10 MB) | A Real-Time Technique for Early Prediction of Sepsis Using Wearable Devices | |
| 38 | WIN-UAB | Edwar Macias, Guillem Boquet, Javier Serrano, Jose Lopez Vicario, Jose Ibeas, Antoni Morell | Link (0.6 MB) | Novel Imputing Method for the Early Prediction of Sepsis in ICU Using Deep Learning Techniques | |
| 39 | AlgTeam | Reza Firoozabadi, Saeed Babaeizadeh | Link (31.6MB) | An Ensemble of Bagged Decision Trees for Early Prediction of Sepsis | |
| 40 | Leicester Fox | Xin Li, G Andre Ng, Fernando Schlindwein | Link (8.2 MB) | Convolutional and Recurrent Neural Networks for Early Detection of Sepsis using Hourly Physiological Data from Patients in Intensive Care Unit | |
| 41 | ywangda | Wang Yiwen | Link (7.4 MB) | A Large Margin Deep Neural Network for Sepsis Classification | |
| 42 | pqlab | Yuhan Zhou | Link (14.3 MB) | ||
| 43 | USF-Sepsis-Phys | Soodabeh Sarafrazi, Chiral Mehta, Rohini Choudhari, Himanshi Mehta, Patricia Francis-Lyon | Link (0.2 MB) | Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR Data, and Using Deep Learning for Early Sepsis Prediction | |
| 44 | UM Antiseptic | Sardar Ansari | Link (0.05 MB) | ||
| 45 | AvivInnovation | Tzvika Aviv | Link (4.6 MB) | ||
| 46 | The memristive agents | Vasileios Athanasiou, Zoran Konkoli | Link (0.2 MB) | Memristor Models for Early Detection of Sepsis in ICU Patients | |
| 47 | OneMoreSecond | Kai Wang, Lei Zuo, Yanxuan Li | Link (3 MB) | Sample-and-hold/mean Imputation and XGBoost for Sepsis Prediction | |
| 48 | Team_Tesseract | Shailesh Nirgudkar, Tianyu Ding | Link (104 MB) | Early Detection of Sepsis Using Ensemblers | |
| 49 | AI-Neuroimmune | Kaveh Samiee | Link (3.1 MB) | Prediction of Sepsis in Intensive Care Unit Using Electronic Medical Records and Convolutional Bidirectional Recurrent Neural Networks | |
| 50 | IMSAT | Saman Noorzadeh, Shahrooz FaghihRoohi, Mojtaba Zarei | Link (0.2MB) | A Comparative Analysis of HMM and CRF for Early Prediction of Sepsis | |
| 51 | PLUX | Miquel Alfaras, Rui Varandas, Hugo Gamboa | Link (0.3MB) | Ring-Topology Echo State Networks for ICU Sepsis Classification | |
| 52 | LDBR | Lakshman Narayanaswamy, Devendra Garg, Bhargavi Narra, Ramkumar Narayanswamy | Link (0.6 MB) | Machine Learning Algorithmic and System Level Considerations for Early Prediction of Sepsis | |
| 53 | Kriss | Ines Krissaane, Kingsley Hampton, Jumanah Alshenaifi, Richard Wilkinson | Link (0.6 MB) | Anomaly Detection Semi-supervised Framework for Sepsis Treatment | |
| 54 | PhysioNet Example | PhysioNet Team | Link (0.3 MB) | Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 | |
| 55 | CIS2216 | Shenda Hong, Junyuan Shang, Meng Wu, Yuxi Zhou, Yongyue Sun, Yen Hsiu Chou, Moxian Song, Hongyan Li | Link (0.4 MB) | Early Sepsis Prediction with Deep Recurrent Reinforcement Learning | |
| 56 | anni | Annie Zheng | Link (5 MB) | ||
| 57 | Tricog | Manmay Nakhashi, Anoop Toffy, Achuth PV, Lingaselvan Palanichamy, Vikas C M | Link (0.3 MB) | Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs | |
| 58 | USST | Wenjie Cai, Danqin Hu, Shuaicong Hu | Link (30.1 MB) | ||
| 59 | ARUL | Induparkavi Murugesan, Karthikeyan Murugesan, Lingeshwaran Balasubramanian, Malathi Murugesan | Link (0.4 MB) | Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis | |
| 60 | TU Dresden - IBMT | Matthieu Scherpf, Miriam Goldammer, Hagen Malberg, Felix Gräßer | Link (1.5 MB) | Sepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine | |
| 61 | Sepsis ReSepsion | Po-Ya Hsu, Chester Holtz | Link (36.8 MB) | A Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data | |
| 62 | Purdue University | Aaron Althoff | Link (7.3 MB) | ||
| 63 | Amini-Univ-Tehran | Morteza Amini | Link (0.01 MB) | Early Prediction of Sepsis from Clinical Data Using a Specialized Hidden Markov Model | |
| 64 | Py | Parivash FahamAzad, Ruhallah Amandi, Mohammad Farhadi | Link (40 MB) | ||
| 65 | Whitaker’s Warriors | Erik Gilbertson, Khristian Jones, Abigail Strohl, Bradley Whitaker | Link (54.2 MB) | Early Detection of Sepsis Using Feature Selection, Feature Extraction, and Neural Network Classification | |
| 66 | VGTU | Vytautas Abromavicius, Artūras Serackis | Link (23.3 MB) | Sepsis Prediction Model Based on Vital Signs Related Features | |
| 67 | RoBusto | Diogo Nunes | Link (55.4 MB) | Trend and Filtered State Extraction through Savitzky-Golay Filtering for the Early Detection of Sepsis Events | |
| 68 | SHODH | Aruna Deogire | Link (0.4 MB) | A Low Dimensional Algorithm for Detection of Sepsis from Electronic Medical Record Data | |
| 69 | UND_BERCLAB | Soufiane Chami, Guragain Bijay, Hoffmann Bradley, Majumder Shubha, Naima Kaabouche, Kouhyar Tavakolian | Link (0.3 MB) | Early Prediction of Sepsis From Clinical Data Using Single Light-GBM Model | |
| 70 | Sepsis’ debugger | Aya Tello, Yazan Shikhani | Link (16.5 MB) | ||
| 71 | claguet | Clementine Aguet, Jérôme Van Zaen, Mathieu Lemay | Link (0.6 MB) | Sepsis Detection Using Missingness Information | |
| 71 | CRASHers | Marco AF Pimentel, Adam Mahdi, Oliver Redfern, Mauro Santos | Link (22.3 MB) | Uncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU | |
| 71 | PIPI | Xiaofeng Tang | Link (76.4 MB) | ||
| 74 | ScuDicaLab | Yao Chen, Jiancheng Lv | Link (0.1 MB) | Contextual LSTM (CLSTM) Models for Early Prediction of Sepsis | |
| 75 | cinc_sepsis_pass | Mengsha Fu, Jiabin Yuan, Menglin Lu, Pengfei Hong, Mei Zeng | Link (0.1 MB) | An Ensemble Machine Learning Model for the Early Detection of Sepsis from Clinical Data | |
| 76 | CH | Kucharski D, Pabian M, Rzepka D | Link (25.6 MB) | ||
| 77 | Kent Ridge AI | Shiyu Liu, Ming Lun Ong, Kar Kin Mun, Jia Yao, Mehul Motani | Link (2.2 MB) | Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information based Downsampling | |
| 78 | The Sepsis Detectives | Akram Mohammed, Franco van Wyk, Anahita Khojandi, Rishikesan Kamaleswaran | Link (0.8 MB) | When to Start Sepsis Bundle? A Machine Learning Approach to Earlier Detection Using Electronic Medical Records | |
| x | A_UNSW_Sepsis | Unknown | |||
| x | Bolloknoon Institute | Unknown | |||
| x | B-Secur | Peter Doggart, Megan Rutherford | Link (0.2 MB) | Randomly under Sampled Boosted Tree for Predicting Sepsis from Intensive Care Unit Databases | |
| * | CIBIM | Oliver Carr, Stefan Bostock, Nicolas Basty, John Prince, Kirubin Pillay, Navin Cooray, Maarten De Vos | Link (1.6 MB) | Novelty Detection for the Early Prediction of Sepsis | |
| x | Main Lab | Unknown | |||
| x | MELAB | Unknown | |||
| x | NCHC-Physionet | Tim Huang | |||
| * | Rx-LR | Edward Ho, Cathy Ong-Ly, Alex Zhou | Link (7.8 MB) | Developing an Interpretable Predictive Model for Early Diagnosis of Sepsis Using Automatic Feature Extraction | |
| * | SailOcean | Meicheng Yang, Hongxiang Gao, Xingyao Wang, Yuwen Li, Jianqing Li, Chengyu Liu | Link (0.7 MB) | Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization. | An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis |
| x | Sepsis_2G | Marcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu Sinha, Sven-Olaf Kuhn | Link (0.2 MB) | Time-specific Metalearners for the Early Prediction of Sepsis | |
| ? | s(k)eptic | Tanuka Bhattacharjee, Sakyajit Bhattacharya, Varsha Sharma, Anirban Dutta Choudhury, Sunil Kumar Kopparapu, Rupayan Chakraborty, Upasana Tiwari, Murali Poduval, Sundeep Khandelwal, Kayapanda Muthana Mandana | Link (31.3 MB) | Early Sepsis Prediction by Cascaded Classification of Multi-Modal Clinical Parameters | |
| x | SUN | Yanbo Xu, Siddharth Biswal, Rahul Duggal, Yu Jing, Jimeng Sun | Hand Crafted Features and an LSTM for Predicting Sepsis | ||
| x | thb100 | Unknown | |||
| x | The Way Code Should Be | Clare Bates Congdon | A Naïve Neural-Net Approach to Prediction of Sepsis with Time-Series Data | ||
| * | UAlberta | Humza Haider | Link (7.7 MB) | ||
| ? | UCAS_BigBird | Unknown | Link (0.2 MB) | ||
| ? | ucas-star | Unknown | Link (0.02 MB) | ||
| x | UlsterTeam | Pardis Biglarbeigi, Donal McLaughlin, Khaleed Rjoob, Abdullah Abdullah, Niamh McCallan, Alicja Jasinska-Piadlo, Raymond Bond, Dewar Finlay, Mark Kok Yew Ng, Alan Kennedy, James McLaughlin | Link (0.2 MB) | Early prediction of sepsis considering Early Warning Scoring systems | |
| ? | WML | Unknown | Link (5.6 MB) | ||
| ? | XLS-IMECAS | Unknown | Link (9.2 MB) | ||
| * | Yuanfang Guan | Yuanfang Guan | Link (5.9 MB) | ||
| # | obama | Unknown | |||
| # | PATH | Unknown | |||
| # | smile | Unknown | |||
| # | snow | Unknown | |||
| # | strawc | Unknown |
(*) Failed to satisfy all conditions of competition.
(x) Failed to submit successful entry.
(?) Did not identify team members.
(#) Disqualified for submitting code substantially similar to another team .
Hakathon results
The ranks of the teams in hakathon are available in the table below.
| Rank | Team name | Team members |
|---|---|---|
| 1 | SailOcean | Meicheng Yang, Hongxiang Gao, Xingyao Wang, Yuwen Li, Xing Liu, Jianqing Li, Chengyu Liu |
| 2 | prna | Jonathan Rubin, Yale Chang, Saman Parvaneh, Gregory Boverman |
| 3 | Sepsyd | John Anda Du, Miquel Alfaras, Naoki Nonaka, Inès Krissaane, Edwar Hernando Macias Toro, Matthieu Scherpf |
| 4 | TAG | Xiaopeng Zhao, Simon Lyra, Marcus Vollmer, Christoph Hoog Antink |
| 5 | vn | ByeongTak Lee, KyungJae Cho, Oyeon Kwon |
| 6 | cinc_sepsis_pass | Mengsha Fu |
| 7 | Ulster University | Pardis Biglarbeigi, Donal McLaughlin, Khaled Rjoob, Abdullah Abdullah, Niamh McCallan, Alicja Jasinska-Piadlo, Raymond Bond, Dewar Finlay, Kok Yew Ng, Alan Kennedy, Jim McLaughlin |
| 8 | CRASHers | Marco AF Pimentel, Adam Mahdi, Oliver Redfern, Mauro Santos |
| x | ARUL | Induparkavi Murugesan, Lingeshwaran Balasubramanian, Malathi Murugesan |
(x) Failed to submit successful entry.
Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362.
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