Learning representations for the early detection of sepsis with deep neural networks

Cited by (218)

Items 101–200 of 218

1. Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage
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2. Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review
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3. Infection prediction using physiological and social data in social environments
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4. Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis
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5. How AI Could Help Us in the Epidemiology and Diagnosis of Acute Respiratory Infections?
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6. Machine learning mortality classification in clinical documentation with increased accuracy in visual‐based analyses
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7. Recurrent Neural Networks (RNN)
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8. Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques
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9. Continuous-time probabilistic models for longitudinal electronic health records
   Alan David Kaplan, Uttara Tipnis, Jean C. Beckham, Nathan A. Kimbrel, David W. Oslin, Benjamin H. McMahon · 2022 · Journal of Biomedical Informatics
10. Temporal convolution attention model for sepsis clinical assistant diagnosis prediction
    Yong Li, Yang Wang · 2023 · Mathematical Biosciences & Engineering
11. Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods
    Samuel N. Cohen, James Foster, Peter Foster, Hang Lou, Terry Lyons, Sam Morley, James Morrill, Hao Ni, Edward Palmer, Bo Wang et al. · 2024 · Scientific Reports
12. Parsimonious waveform-derived features consisting of pulse arrival time and heart rate variability predicts the onset of septic shock
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13. The future of precision health is data‐driven decision support
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14. Recurrent Neural Networks for Early Detection of Late Onset Sepsis in Premature Infants Using Heart Rate Variability
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15. Deep Learning Based Sepsis Intervention: The Modelling and Prediction of Severe Sepsis Onset
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16. Clinical Text Classification for Tuberculosis Diagnosis Using Natural Language Processing and Deep Learning Model with Statistical Feature Selection Technique
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17. Usage of Auxiliary Systems and Artificial Intelligence in Home-Based Rehabilitation
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18. Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches
    Chenglong Liang, Shuo Pan, Wei Wu, Fanxuan Chen, Chengxi Zhang, Chen Zhou, Yifan Gao, Xiangyuan Ruan, Shichao Quan, Qi Zhao et al. · 2024 · Computational and Structural Biotechnology Journal
19. Sepsis mortality prediction with Machine Learning Tecniques
    Javier Carrillo Pérez-Tome, Tesifón Parrón Carreño, Ana Belén Castaño-Fernández, Bruno José Nievas Soriano, Gracia María Castro-Luna · 2024 · Medicina Intensiva
20. DI++: A deep learning system for patient condition identification in clinical notes
    Jinhe Shi, Xiangyu Gao, William C. Kinsman, Chenyu Ha, Guodong Gordon Gao, Yi Chen · 2021 · Artificial Intelligence in Medicine
21. Identifying the need for infection-related consultations in intensive care patients using machine learning models
    Leslie R. Zwerwer, Christian F. Luz, Dimitrios Soudis, Nicoletta Giudice, Maarten W. Nijsten, Corinna Glasner, Maurits H. Renes, Bhanu N. M. Sinha · 2024 · Scientific Reports
22. miR-331 inhibits CLDN2 expression and may alleviate the vascular endothelial injury induced by sepsis
    Lingchen Kong, Peng Wu, Jianzhong Li · 2020 · Experimental and Therapeutic Medicine
23. Predicting Early Neonatal Sepsis using Neural Networks and Other Classifiers
    Redwan Hasif Alvi, Md Habibur Rahman, Adib Al Shaeed Khan, Rashedur Mohammad Rahman · 2020 · IEEE International Conference on Intelligent Systems
24. Sepsis Deterioration Prediction Using Channelled Long Short-Term Memory Networks
    Peter Svenson, Giannis Haralabopoulos, Mercedes Torres Torres · 2020 · Lecture notes in computer science
25. Health Status Prediction with Local-Global Heterogeneous Behavior Graph
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26. Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization
    Pratibha Verma, Vineet Kumar Awasthi, Sanat Kumar Sahu, Akhilesh Kumar Shrivas · 2021 · International Journal of Applied Metaheuristic Computing
27. Using Features Extracted from Vital Time Series for Early Prediction of Sepsis
    Qiang Yu, Xiaolin Huang, Weifeng Li, Cheng Wang, Ying Chen, Yun Ge · 2019 · Computing in cardiology
28. Medical concept embedding of real-valued electronic health records with application to inflammatory bowel disease
    Hanan Mann, Aharon Bar-Hillel, Raffi Lev‐Tzion, Shira Greenfeld, Revital Kariv, Natan Lederman, Eran Matz, Iris Dotan, Dan Turner, Boaz Lerner · 2023 · Artificial Intelligence in Medicine
29. Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis
    Ghulam Husain Abbas, Palash Sen, Oviya Anjali Giri, Nawaid Hussain Khan · 2025 · Critical Care Explorations
30. SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis
    Qiyu Chen, Ranran Li, ChihChe Lin, Chiming Lai, Yaling Huang, Wenlian Lu, Lei Li · 2022 · Applied Clinical Informatics
31. Sepsis mortality prediction with Machine Learning Tecniques
    Javier Carrillo Pérez-Tome, Tesifón Parrón Carreño, Ana Belén Castaño-Fernández, Bruno José Nievas Soriano, Gracia María Castro-Luna · 2024 · Medicina Intensiva (English Edition)
32. An Easy and Quick Risk-Stratified Early Forewarning Model for Septic Shock in the Intensive Care Unit: Development, Validation, and Interpretation Study
    Guanghao Liu, Shixiang Zheng, Jun He, Zimei Zhang, Riping Wu, Yingying Yu, Hao Fu, Li Han, Haibo Zhu, Yichang Xu et al. · 2025 · Journal of Medical Internet Research
33. On Computer-Aided Prognosis of Septic Shock from Vital Signs
    Hasan Oğul, Alejandro Baldominos, Tunç Aşuroğlu, Ricardo Colomo‐Palacios · 2019 · Proceedings - IEEE Symposium on Computer-Based Medical Systems
34. Infection Diagnosis using Biomedical Signals in Small Data Scenarios
    Alejandro Baldominos, Hasan Oğul, Ricardo Colomo‐Palacios · 2019 · Proceedings - IEEE Symposium on Computer-Based Medical Systems
35. Dynamic prediction of late noninvasive ventilation failure in intensive care unit using a time adaptive machine model
    Xue Feng, Su Pan, Molei Yan, Yanfei Shen, Xiaoqing Liu, Guolong Cai, Gangmin Ning · 2021 · Computer Methods and Programs in Biomedicine
36. Artificial intelligence in medical physics
    Nicola Amoroso, R. Errico, Ester Pantaleo, Alfonso Monaco, R. Bellotti · 2025 · Rivista Del Nuovo Cimento
37. Enhancing sepsis management through machine learning techniques: A review
    N. Ocampo-Quintero, Pablo Vidal, Lorena del Río Carbajo, Florentino Fdez‐Riverola, Miguel Reboiro‐Jato, Daniel Glez‐Peña · 2022 · Medicina Intensiva (English Edition)
38. Improving Early Sepsis Prediction with Multi Modal Learning
    Fred Qin, Vivek Madan, Ujjwal Ratan, Zohar S. Karnin, Vishaal Kapoor, Parminder Bhatia, Taha Kass‐Hout · 2021 · arXiv (Cornell University)
39. Temporal and Spatial Analysis in Early Sepsis Prediction via Causal Disentanglements
    Qiang Li, Dongchen Li, Weizhi Nie, He Jiao, Zhenhua Wu, An-An Liu · 2025 · IEEE Transactions on Knowledge and Data Engineering
40. FL-WOSP: Federated learning with walrus optimization for sepsis prediction using MIMIC-III physiological and clinical data
    Betshrine Rachel Jibinsingh, Leema Nelson · 2025 · Pattern Recognition
41. Prediction of sepsis onset in hospital admissions using survival analysis
    Brandon DeShon, Benjamin Dummitt, Joshua Allen, Byron W. Yount · 2022 · Journal of Clinical Monitoring and Computing
42. Real-time sepsis prediction using fusion of on-chip analog classifier and electronic medical record
    Sudarsan Sadasivuni, Monjoy Saha, Sumukh Prashant Bhanushali, Imon Banerjee, Arindam Sanyal · 2022 · IEEE International Symposium on Circuits and Systems proceedings
43. Unsupervised probabilistic models for sequential Electronic Health Records
    Alan David Kaplan, J Greene, Vincent X. Liu, Priyadip Ray · 2022 · Journal of Biomedical Informatics
44. Death Comes But Why: An Interpretable Illness Severity Predictions in ICU
    Shaofei Shen, Miao Xu, Lin Yue, Robert James Boots, Weitong Chen · 2023 · Lecture notes in computer science
45. Sepsis Detection using Neural Networks
    Babu Sallagundla, Lalitha Anupama Annavarapu, Lakshmi Lahari Appala · 2022 · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON)
46. Early Sepsis Prediction in ICU Trauma Patients with Using An Improved Cascade Deep Forest Model
    Mengsha Fu, Jiabin Yuan, Chen Bei · 2019 · IEEE International Conference on Software Engineering and Service Science (ICSESS)
47. A Machine Learning Understanding of Sepsis
    Manish Shetty, Soumya Mary Alex, Merlin Moni, Fabia Edathadathil, Preetha Prasanna, Veena Menon, Vidya P. Menon, Prashanth Athri, Gowri Srinivasa · 2021 · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
48. Time-Specific Metalearners for the Early Prediction of Sepsis
    Marcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu N. M. Sinha, Sven‐Olaf Kuhn · 2019 · Computing in cardiology
49. Physiomarkers in Real-Time Physiological Data Streams Predict Adult Sepsis Onset Earlier Than Clinical Practice
    Franco van Wyk, Anahita Khojandi, Robert Lowell Davis, Rishikesan Kamaleswaran · 2018 · bioRxiv (Cold Spring Harbor Laboratory)
50. Early Detection of Sepsis on Clinical Data Using Multi-layer Perceptron
    N. Venkata Sailaja, Meghana Yelamarthi, Yendluri Hari Chandana, Prathyusha Karadi, Sreshta Yedla · 2021 · Algorithms for intelligent systems
51. Early Prediction of Sepsis using ML Algorithms on Clinical Data
    N Smitha, Arushi Singh, Abhishek Siddhartha Ghosh, Arya Kumari, R Tanuja, S H Manjula, K R Venugopal · 2023 · International Conference on Computing, Communication and Networking Technologies (ICCCNT)
52. Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network
    Thanh-Cong Do, Hyung-Jeong Yang, Soo-Hyung Kim, Bo‐Gun Kho, Jin Kyung Park · 2024 · International Journal of Medical Informatics
53. DynEdges-TGN: Dynamic-Edges-Based Temporal Graph Network for Early Sepsis Prediction
    Supriya Bajpai, Manohar Kaul · 2025 · SN Computer Science
54. Improving the Performance of a Neural Network for Early Prediction of Sepsis
    ByeongTak Lee, KyungJae Cho, Oyeon Kwon, Yeha Lee · 2019 · Computing in cardiology
55. Early Identification of Patients at Risk of Sepsis in a Hospital Environment
    Everton Osnei Cesario, Yohan Bonescki Gumiel, Márcia C. Marins Martins, Viviane Maria de Carvalho Hessel Dias, Claudia Maria Cabral Moro, Déborah Ribeiro Carvalho · 2021 · Brazilian Archives of Biology and Technology
56. Artificial Intelligence Based Early Diagnosis of Sepsis
    Amer Kareem, Rejwan Bin Sulaiman, Shaik Vaseem Akram, M Maurya, Inavolu Srinivasa Chakrapani, P. Sasikala · 2023 · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)
57. A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study
    Zixin Luo, Jialian Lv, Kang Zou · 2025 · Frontiers in Medicine
58. An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough
    Zhu Zhu, Jing Li, Jian Huang, Zheming Li, Hongjian Zhang, Siyu Chen, Qianhui Zhong, Yulan Xie, Shasha Hu, Yinshuo Wang et al. · 2022 · Translational Pediatrics
59. SEPRES: Sepsis prediction via the clinical data integration system in the ICU
    Qiyu Chen, Ranran Li, ChihChe Lin, Chiming Lai, Yaling Huang, Wenlian Lu, Lei Li · 2022 · medRxiv
60. Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model
    Haiyan Mai, Yaxin Lu, Yu Fu, Tongsen Luo, Xiaoyue Li, Yihan Zhang, Zifeng Liu, Yuenong Zhang, Shaoli Zhou, Chaojin Chen · 2024 · Journal of Medical Internet Research
61. Early Detection of Sepsis Using Artificial Intelligence in Intensive Care Units: A Systematic Review and Meta-Analysis
    Xiaomeng Ji, Huasong Huo, Lihua Dong · 2025 · Journal of Intensive Care Medicine
62. A New Time-Aware LSTM based Framework for Multi-label Classification on Healthcare Data
    Abdelhamid Gaddari, Haytham Elghazel, Rakia Jaziri, Mohand-Saïd Hacid, Comble Pierre-Henri · 2023 · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)
63. An interpretable model for sepsis prediction using multi-objective rule extraction
    Mingzhou Chen, Jiazhen Huo, Yongrui Duan · 2024 · Journal of Intelligent Information Systems
64. Forecasting Emergency Room Patient Volumes Using Extreme Gradient Boosting With Temporal and Seasonal Feature Engineering: A Comparative Study Across Hospitals
    Kian A Huang, William M Hardin, Neelesh S Prakash · 2025 · Cureus
65. Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
    Zina M. Ibrahim, Honghan Wu, Richard Dobson · 2020 · Frontiers in artificial intelligence and applications
66. Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections
    Christian Gosset, Jacques Foguenne, Mickaël Simul, Olivier Tomsin, Hayet Ammar, Nathalie Layios, Paul Bernard Massion, Pierre Damas, André Gothot · 2021 · Scientific Reports
67. LXLMEPS: Leveraging the XGB-lCE-Based Model for Early Prediction of Sepsis
    Zhang Leyi, Yingjie Long, Yingbiao Hu, Huinian Li · 2023 · Lecture notes in computer science
68. Sepsis Patient Outcome Prediction Using Machine Learning
    José Lucas de Alencar Saraiva, Marcus Henrique Victor Júnior, Otávio Monteiro Becker, Visakan Kadirkamanathan, Eliezer Silva, Karl Heinz Kienitz · 2019 · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
69. Developing a deep learning system to drive the work of the critical care outreach team
    Georgina Kennedy, John Rihari‐Thomas, Mark Dras, Blanca Gallego · 2020 · medRxiv
70. Gradient Importance Learning for Incomplete Observations
    Qitong Gao, Dong Wang, Joshua Amason, Siyang Yuan, Chenyang Tao, Ricardo Henao, Majda Hadziahmetovic, Lawrence Carin, Miroslav Pajić · 2021 · arXiv (Cornell University)
71. Clinical Applications of Machine Learning in the Survival Prediction and Classification of Sepsis：Coagulation and Heparin Usage Matter
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72. Deep Learning-Based Bayesian Neural Network for Personalized Decision Support in Leukemia Treatment Using Medical Imaging
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74. Analysis of various health parameters for early and efficient prediction of sepsis
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75. Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis
    Gang Luo, Bryan L. Stone, Xiaoming Sheng, Shan He, Corinna Koebnick, Flory L. Nkoy · 2021 · JMIR Research Protocols
76. Identification of Sepsis Subphenotypes Based on Bi-directional Long Short-Term Memory Auto-encoder Using Real-Time Laboratory Data Collected from Intensive Care Units
    Yongsen Tan, Jiahui Huang, Jinhu Zhuang, Haofan Huang, Yong Liu, Xiaxia Yu · 2023 · Communications in computer and information science
77. A Study on Existing EHR Models Used for Validating the Clinical Records
    Priyanka Sharma, Tapas Kumar, S. S. Tyagi · 2023 · Lecture notes in networks and systems
78. LSTM Model for Sepsis Detection and Classification Using PPG Signals
    Michael A. Alvarez-Navarro, Luis Huallparimachi, Sebastián A. Cruz-Romero, Heidy Sierra · 2024 · Lecture notes in networks and systems
79. Enhancing Sepsis Detection Using BiLSTM with Attention Mechanism: A Time-Series Deep Learning Framework
    Sallauddin Mohmmad, Billakanti Srinivasa Rao, Gulab Singh Chauhan, U Sadhana, Karthik Sridhar, Shabana Shabana · 2025 · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
80. Deep Learning Techniques on Sparsely Sampled Multichannel Data—Identify Deterioration in ICU Patients
    Achilleas Chytas, Katerina Vaporidi, Y. Surlatzis, Dimitrios Georgopoulos, Nicos Maglaveras, Ioanna Chouvarda · 2017 · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
81. Time-Specific Metalearners for the Early Prediction of Sepsis
    Marcus Vollmer, Christian F. Luz, Philipp Sodmann, Bhanu N. M. Sinha, Sven‐Olaf Kuhn · 2019 · Computing in cardiology
82. A Multi-task LSTM Framework for Improved Early Sepsis Prediction
    Theodoros Tsiligkaridis, Jennifer Sloboda · 2020 · Lecture notes in computer science
83. A New Standard for Sepsis Prediction Algorithms: Using Time-Dependent Analysis for Earlier Clinically Relevant Alerts
    Jenish Maharjan, Rahul Thapa, Jacob S. Calvert, Misty M. Attwood, Sepideh Shokouhi, Satish Casie Chetty, Zohora Iqbal, Navan Preet Singh, Rome Arnold, Jana L. Hoffman et al. · 2022 · SSRN Electronic Journal
84. Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers
    Gavin Tsang · 2022
85. Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review
    Dennis Peace Ezeobi, Angella Musiimenta, William Wasswa, Stella Kyoyagala · 2023 · Research Square
86. Data-driven modeling and learning approach to solving ICU risk prediction and survival analysis
    Yilin Yin · 2022
87. Investigation into rule-based inferential modelling and prediction with application in healthcare
    Shuaiyu Yao · 2019 · Research Explorer (The University of Manchester)
88. Predicting Biomedical Timeseries Data using Neural Networks.
    Sangeeta Warrier · 2020 · NCSU Libraries Repository (North Carolina State University Libraries)
89. Sensitivity analysis–based sepsis prognosis using artificial intelligence
    José Lucas de Alencar Saraiva, Otávio Monteiro Becker, Eliezer Silva, Visakan Kadirkamanathan, Karl Heinz Kienitz · 2020 · Research on Biomedical Engineering
90. Predicting Infection-related Consultations on Intensive Care Units - Development of a Machine Learning Prediction Model
    Christian F. Luz, Dimitrios Soudis, Maurits H. Renes, Leslie R. Zwerwer, Nicoletta Giudice, Corinna Glasner, Maarten W. Nijsten, Bhanu N. M. Sinha · 2021 · medRxiv
91. Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review
    Dennis Peace Ezeobi, William Wasswa, Angella Musiimenta, Stella Kyoyagala · 2022 · Research Square
92. The data driven intensive care unit
    Lucas M. Fleuren · 2023
93. Sepsis Prediction Method Based on Neural Network Combined with Multiple Life Characteristics
    亚君 何 · 2023 · Modeling and Simulation
94. Comparison of Machine Learning Models for Predictive Analytics of Sepsis in the Emergency Medical Admissions
    Vandana, Parli B. Hari · 2023 · International Conference on Inventive Research in Computing Applications (ICIRCA)
95. An novel cutting edge ANN machine learning algorithm for sepsis early prediction and diagnosis
    R. Rajesh Kanna, T. Mohana Priya, V. Ashok Immanuel, V. B. Kirubanand, T. Senthilnathan, V. Rohini · 2023 · AIP conference proceedings
96. NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients
    Tucker Stewart, Katherine E. Stern, Grant Edward O’Keefe, Ankur M. Teredesai, Juhua Hu · 2023 · IEEE International Conference on Big Data (BigData)
97. Early Prediction of Sepsis Using Machine Learning Algorithms: A Review
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98. Application of Machine Learning and its use in determining Sepsis Prediction
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99. Multi-Horizon Event Detection for In-Hospital Clinical Deterioration Using Dual-Channel Graph Attention Network
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100. Research Progress of Deep Learning in Diagnosis of Sepsis
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