Publications from Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019

Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019.
Reyna MA, Josef CS, Jeter R, Shashikumar SP, Westover MB, Nemati S, Clifford GD, Sharma A. Computing in Cardiology 2019.

1. James Morrill, Andrey Kormilitzin, Alejo Nevado-Holgado, Sumanth Swaminathan, Sam Howison, Terry Lyons. The Signature-Based Model for Early Detection of Sepsis From Electronic Health Records in the Intensive Care Unit
2. Meicheng Yang, Xingyao Wang, Hongxiang Gao, Yuwen Li, Xing Liu, Jianqing Li, Chengyu Liu. Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization
3. Luan Tran, Manh Nguyen, Cyrus Shahabi. Representation Learning for Early Sepsis Prediction
4. Petr Nejedly, Filip Plesinger, Ivo Viscor, Josef Halamek, Pavel Jurak. Prediction of Sepsis Using LSTM Neural Network With Hyperparameter Optimization With a Genetic Algorithm
5. Reza Firoozabadi, Saeed Babaeizadeh. An Ensemble of Bagged Decision Trees for Early Prediction of Sepsis
6. Marcus Vollmer, Christian F Luz, Philipp Sodmann, Bhanu Sinha, Sven-Olaf Kuhn. Time-Specific Metalearners for the Early Prediction of Sepsis
7. Chloe Pou-Prom, Zhen Yang, Maitreyee Sidhaye, David Dai. Development of an Early Warning System for Sepsis
8. Aruna Deogire. A Low Dimensional Algorithm for Detection of Sepsis From Electronic Medical Record Data
9. E Macias, G Boquet, J Serrano, JL Vicario, J Ibeas, A Morel. Novel Imputing Method and Deep Learning Techniques for Early Prediction of Sepsis in Intensive Care Units
10. Xiang Li, Yanni Kang, Xiaoyu Jia, Junmei Wang, Guotong Xie. TASP: A Time-Phased Model for Sepsis Prediction
11. P Biglarbeigi, D McLaughlin, K Rjoob, Abdullah, N McCallan, A Jasinska-Piadlo, R Bond, D Finlay, KY Ng, A Kennedy, J McLaughlin. Early Prediction of Sepsis Considering Early Warning Scoring Systems
12. Xin Li, G Andre Ng, Fernando S Schlindwein. Convolutional and Recurrent Neural Networks for Early Detection of Sepsis Using Hourly Physiological Data from Patients in Intensive Care Unit
13. Roshan Pawar, Jeffrey Bone, J Mark Ansermino, Matthias Görges1. An Algorithm for Early Detection of Sepsis Using Traditional Statistical Regression Modeling
14. Qiang Yu, Xiaolin Huang, Weifeng Li, Cheng Wang, Ying Chen, Yun Ge. Using Features Extracted From Vital Time Series for Early Prediction of Sepsis
15. Benjamin Roussel, Joachim Behar, Julien Oster. A Recurrent Neural Network for the Prediction of Vital Sign Evolution and Sepsis in ICU
16. Matthieu Scherpf, Miriam Goldammer, Hagen Malberg, Felix Graßer. Sepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine
17. Vytautas Abromavicius, Arturas Serackis. Sepsis Prediction Model Based on Vital Signs Related Features
18. Victor G Marques, Miguel Rodrigo, María S Guillem, João Salinet. Effect of Reducing the Number of Leads from Body Surface Potential Mapping in Computer Models of Atrial Arrhythmias
19. Yale Chang, Jonathan Rubin, Gregory Boverman, Shruti Vij, Asif Rahman, Annamalai Natarajan, Saman Parvaneh. A Multi-Task Imputation and Classification Neural Architecture for Early Prediction of Sepsis from Multivariate Clinical Time Series
20. Naoki Nonaka, Jun Seita. Demographic Information Initialized Stacked Gated Recurrent Unit for an Early Prediction of Sepsis
21. Luchen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming Zhang. Early Prediction of Sepsis From Clinical Data via Heterogeneous Event Aggregation
22. Lakshman Narayanaswamy, Devendra Garg, Bhargavi Narra, Ramkumar Narayanswamy. Machine Learning Algorithmic and System Level Considerations for Early Prediction of Sepsis
23. ByeongTak Lee, KyungJae Cho, Oyeon Kwon, Yeha Lee. Improving the Performance of a Neural Network for Early Prediction of Sepsis
24. Ines Krissaane, Kingsley Hampton, Jumanah Alshenaifi, Richard Wilkinson. Anomaly Detection Semi-Supervised Framework for Sepsis Treatment
25. Shailesh Nirgudkar, Tianyu Ding. Early Detection of Sepsis Using Ensemblers
26. Yongchao Wang, Bin Xiao, Xiuli Bi, Weisheng Li, Junhui Zhang, Xu Ma. Prediction of Sepsis from Clinical Data Using Long Short-Term Memory and eXtreme Gradient Boosting
27. Po-Ya Hsu, Chester Holtz. A Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data
28. Shivnarayan Patidar. Diagnosis of Sepsis Using Ratio Based Features
29. Marco AF Pimentel, Adam Mahdi, Oliver Redfern, Mauro D Santos, Lionel Tarassenko. Uncertainty-Aware Model for Reliable Prediction of Sepsis in the ICU
30. Vasileios Athanasiou, Zoran Konkoli. Memristor Models for Early Detection of Sepsis in ICU Patients
31. Francesco Renna, Miguel Coimbra. Source Separation of the Second Heart Sound Using Gaussian Mixture Models
32. Morteza Zabihi, Serkan Kiranyaz, Moncef Gabbouj. Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models
33. Shiyu Liu, Ming Lun Ong, Kar Kin Mun, Jia Yao, Mehul Motani. Early Prediction of Sepsis via SMOTE Upsampling and Mutual Information Based Downsampling
34. Jia Yao, Ming Lun Ong, Kar Kin Mun, Shiyu Liu, Mehul Motani. Hybrid Feature Learning Using Autoencoders for Early Prediction of Sepsis
35. Saman Noorzadeh, Shahrooz Faghihroohi, Mojtaba Zarei. A Comparative Analysis of HMM and CRF for Early Prediction of Sepsis
36. Manmay Nakhashi, Anoop Toffy, Achuth P V, Lingaselvan Palanichamy, Vikas C M. Early Prediction of Sepsis: Using State-of-the-art Machine Learning Techniques on Vital Sign Inputs
37. Zhengling He, Xianxiang Chen, Zhen Fang, Weidong Yi, Chenshuo Wang, Li Jiang, Zhongkai Tong, Zhongrui Bai, Yueqi Li, Yichen Pan. Early Sepsis Prediction Using Ensemble Learning with Features Extracted from LSTM Recurrent Neural Network
38. Simon Lyra, Steffen Leonhardt, Christoph Hoog Antink. Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data
39. Janmajay Singh, Kentaro Oshiro, Raghava Krishnan, Masahiro Sato, Tomoko Ohkuma, Noriji Kato. Utilizing Informative Missingness for Early Prediction of Sepsis
40. Ben Sweely, Austin Park, Lia Winter, Longjian Liu, Xiaopeng Zhao. Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development
41. Sven Schellenberger, Kilin Shi, Jan P Wiedemann, Fabian Lurz, Robert Weigel, Alexander Koelpin. An Ensemble LSTM Architecture for Clinical Sepsis Detection
42. AS de la Nava, MC Fabregat, M Rodrigo, I Hernandez, A Liberos, F Fernández-Avilés, MS Guillem, F Atienza, AM Climent. Non-Invasive Electrophysiological Mapping Entropy Predicts Atrial Fibrillation Ablation Efficacy Better Than Clinical Characteristics
43. Rubén Molero, Andreu M Climent, Ismael Hernández-Romero, Alejandro Liberos, Francisco Fernández-Avilés, Felipe Atienza, María S Guillem, Miguel Rodrigo. Effects of Geometry in Atrial Fibrillation Markers Obtained With Electrocardiographic Imaging
44. Mengsha Fu, Jiabin Yuan, Menglin Lu, Pengfei Hong, Mei Zeng. An Ensemble Machine Learning Model For the Early Detection of Sepsis From Clinical Data
45. Miquel Alfaras, Rui Varandas, Hugo Gamboa. Ring-Topology Echo State Networks for ICU Sepsis Classification
46. Induparkavi Murugesan, Karthikeyan Murugesan, Lingeshwaran Balasubramanian, Malathi Arumugam. Interpretation of Artificial Intelligence Algorithms in the Prediction of Sepsis
47. Tomas Vicar, Petra Novotna, Jakub Hejc, Marina Ronzhina, Radovan Smisek. Sepsis Detection in Sparse Clinical Data Using Long Short-Term Memory Network with Dice Loss
48. Peter Doggart, Megan Rutherford. Randomly Under Sampled Boosted Tree for Predicting Sepsis From Intensive Care Unit Databases
49. Soufiane Chami, Kouhyar Tavakolian. Early Prediction of Sepsis From Clinical Data Using Single Light-GBM Model
50. Erik H Gilbertson, Khristian M Jones, Abigail M Stroh, Bradley M Whitaker. Early Detection of Sepsis Using Feature Selection, Feature Extraction, and Neural Network Classification
51. Clementine Aguet, Jerome Van Zaen, Mathieu Lemay. Sepsis Detection Using Missingness Information
52. Soodabeh Sarafrazi, Rohini S Choudhari, Chiral Mehta, Himanshi K Mehta, Omid K Japalaghi, Jie Han, Kinjal A Mehta, Hyunyoung Han, Patricia A Francis-Lyon. Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR Data, and Using Deep Learning for Early Sepsis Prediction
53. Matthew A Reyna, Chris Josef, Salman Seyedi, Russell Jeter, Supreeth P Shashikumar, M Brandon Westover, Ashish Sharma, Shamim Nemati, Gari D Clifford. Early Prediction of Sepsis from Clinical Data: the PhysioNet/Computing in Cardiology Challenge 2019
54. John Anda Du, Nadi Sadr, Philip de Chazal. Automated Prediction of Sepsis Onset Using Gradient Boosted Decision Trees
55. Ibrahim Hammoud, IV Ramakrishnan, Mark Henry. Early Prediction of Sepsis Using Gradient Boosting Decision Trees with Optimal Sample Weighting