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2020 – today
- 2024
[j3]Zhijiang Yu, Negin Ashrafi
, Hexin Li, Kamiar Alaei, Maryam Pishgar:
Prediction of 30-day mortality for ICU patients with Sepsis-3. BMC Medical Informatics Decis. Mak. 24(1): 223 (2024)- [j2]Jiayi Gao, Yuying Lu, Negin Ashrafi, Ian Domingo, Kamiar Alaei, Maryam Pishgar:
Prediction of sepsis mortality in ICU patients using machine learning methods. BMC Medical Informatics Decis. Mak. 24(1): 228 (2024) - [i4]Negin Ashrafi, Armin Abdollahi, Greg Placencia, Maryam Pishgar:
Effect of a Process Mining based Pre-processing Step in Prediction of the Critical Health Outcomes. CoRR abs/2407.02821 (2024) - [i3]Negin Ashrafi, Armin Abdollahi, Maryam Pishgar:
Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques. CoRR abs/2408.01144 (2024) - [i2]Arseniy Shumilov, Yueting Zhu, Negin Ashrafi, Gaojie Lian, Shilong Ren, Maryam Pishgar:
Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality. CoRR abs/2408.01612 (2024) - [i1]Negin Ashrafi, Armin Abdollahi, Jiahong Zhang, Maryam Pishgar:
Optimizing Mortality Prediction for ICU Heart Failure Patients: Leveraging XGBoost and Advanced Machine Learning with the MIMIC-III Database. CoRR abs/2409.01685 (2024) - 2023
- [j1]Sheela Ramanna, Negin Ashrafi, Evan Loster, Karen Debroni, Shelley Turner:
Rough-set based learning: Assessing patterns and predictability of anxiety, depression, and sleep scores associated with the use of cannabinoid-based medicine during COVID-19. Frontiers Artif. Intell. 6 (2023) - 2021
- [c1]Negin Ashrafi, Sheela Ramanna:
Application of Granular Computing-Based Pre-processing in the Labelling of Phonemes. KES-IDT 2021: 141-150
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