Last Posted: May 11, 2021
- Aiding clinical assessment of neonatal sepsis using hematological analyzer data with machine learning techniques.
Huang Brian et al. International journal of laboratory hematology 2021
- Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery.
Han Xuan et al. Critical care medicine 2021
- Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.
Kamaleswaran Rishikesan et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2021 2020619-628
- A scalable approach for developing clinical risk prediction applications in different hospitals.
Sun Hong et al. Journal of biomedical informatics 2021 103783
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.
Yin Jiamin et al. Journal of medical Internet research 2021 23(4) e25759
- Development and Validation of a Machine Learning Model to Estimate Bacterial Sepsis Among Immunocompromised Recipients of Stem Cell Transplant.
Lind Margaret L et al. JAMA network open 2021 4(4) e214514
- Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review.
Kausch Sherry L et al. Intensive & critical care nursing 2021 103035
- Characterization of antimicrobial-resistant Gram-negative bacteria that cause neonatal sepsis in seven low- and middle-income countries.
Sands Kirsty et al. Nature microbiology 2021 3 (4) 512-523
- Prediction of Sepsis in COVID-19 Using Laboratory Indicators.
Tang Guoxing et al. Frontiers in cellular and infection microbiology 2021 10586054
- A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.
Shah Parth K et al. Critical care medicine 2021
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