Publications by Antoine Honoré
Peer reviewed
Articles
[1]
A. Ghosh, A. Honore and S. Chatterjee, "DANSE : Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Learning Setup," IEEE Transactions on Signal Processing, vol. 72, pp. 1824-1838, 2024.
[2]
A. Honoré et al., "Vital sign-based detection of sepsis in neonates using machine learning," Acta Paediatrica, vol. 112, no. 4, pp. 686-696, 2023.
[3]
A. M. Stålhammar et al., "Weight a minute : The smaller and more immature, the more predictable the autonomic regulation?," Acta Paediatrica, vol. 112, no. 7, pp. 1443-1452, 2023.
[4]
E. Persad et al., "Neonatal sepsis prediction through clinical decision support algorithms : A systematic review," Acta Paediatrica, vol. 110, no. 12, pp. 3201-3226, 2021.
Conference papers
[5]
A. Honore, A. Ghosh and S. Chatterjee, "Compressed Sensing of Generative Sparse-Latent (GSL) Signals," in 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings, 2023, pp. 1918-1922.
[6]
A. Ghosh, A. Honore and S. Chatterjee, "DANSE : Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Bayesian Setup," in Proceedings 31st European Signal Processing Conference, EUSIPCO 2023, 2023, pp. 870-874.
[7]
A. Honore et al., "An LSTM-based Recurrent Neural Network for Neonatal Sepsis Detection in Preterm Infants," in 2022 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2022 : Proceedings, 2022.
[8]
A. Honore et al., "Hidden markov models for sepsis detection in preterm infants," in Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, 2020, pp. 1130-1134.
[9]
D. Liu et al., "Powering hidden markov model by neural network based generative models," in ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, pp. 1324-1331.
[10]
A. Ghosh et al., "Robust classification using hidden markov models and mixtures of normalizing flows," in 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020.
Non-peer reviewed
Articles
[11]
A. M. Stålhammar et al., "Body weight measurements support machine-learning algorithms in neonatal sepsis prediction," Pediatric Research, vol. 90, no. SUPPL 1, pp. 22-22, 2021.
[12]
A. Clareus et al., "Medications and clinical decision support systems; how caffeine influences heart rate characteristics in preterm infants," Pediatric Research, vol. 90, no. SUPPL 1, pp. 13-14, 2021.
[13]
K. Adolphson et al., "Predicting acute adverse events in neonates using automated vital sign pattern analysis," Pediatric Research, vol. 90, no. SUPPL 1, pp. 22-22, 2021.
Chapters in books
[14]
C. Danker et al., "AI and Dynamic Prediction of Deterioration in Covid-19," in Artificial Intelligence in Covid-19, : Springer Nature, 2022, pp. 257-277.
[15]
D. Forsberg et al., "AIM in Neonatal and Pediatric Intensive Care," in Artificial Intelligence in Medicine, 1st ed. : Springer Nature, 2022, pp. 1047-1056.
Theses
[16]
A. Honoré, "Perspectives of Deep Learning for Neonatal Sepsis Detection," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:51, 2023.
Latest sync with DiVA:
2024-07-17 09:47:40