Found: 9
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Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study.
- Published in:
- JMIR Formative Research, 2021, v. 5, n. 9, p. 1, doi. 10.2196/28028
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- Publication type:
- Article
Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.
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- Health Informatics Journal, 2020, v. 26, n. 3, p. 1912, doi. 10.1177/1460458219894494
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- Article
A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients.
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- Applied Sciences (2076-3417), 2021, v. 11, n. 12, p. 5576, doi. 10.3390/app11125576
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- Publication type:
- Article
A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.
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- Clinical & Applied Thrombosis/Hemostasis, 2021, v. 27, p. 1, doi. 10.1177/1076029621991185
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- Publication type:
- Article
Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification.
- Published in:
- Healthcare Technology Letters, 2021, v. 8, n. 6, p. 139, doi. 10.1049/htl2.12017
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- Publication type:
- Article
Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial.
- Published in:
- Journal of Clinical Medicine, 2020, v. 9, n. 12, p. 3834, doi. 10.3390/jcm9123834
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- Article
Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.
- Published in:
- 2020
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- Publication type:
- journal article
Case Report: The Coronavirus Disease 2019 (COVID-19) Pneumonia With Multiple Thromboembolism.
- Published in:
- Frontiers in Neurology, 2021, v. 11, p. N.PAG, doi. 10.3389/fneur.2020.625272
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- Publication type:
- Article
Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data.
- Published in:
- BioData Mining, 2021, v. 14, n. 1, p. 1, doi. 10.1186/s13040-021-00255-w
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- Publication type:
- Article