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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
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Last data update: Jun 21, 2024
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The Hypotension Prediction Index is equally effective in predicting intraoperative hypotension during non-cardiac surgery compared to a mean arterial pressure threshold: a prospective observational study.
Marijn P Mulder et al. Anesthesiology 2024
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Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.
Daqing Hong et al. Kidney Dis (Basel) 2023 9(5) 433-442
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Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study.
Jingjing Dong et al. Comput Methods Programs Biomed 2023 240107698
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Effect of the machine learning-derived Hypotension Prediction Index (HPI) combined with diagnostic guidance versus standard care on depth and duration of intraoperative and postoperative hypotension in elective cardiac surgery patients: HYPE-2 - study protocol of a randomised clinical trial.
Santino R Rellum et al. BMJ Open 2023 13(5) e061832
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Predicting patient decompensation from continuous physiologic monitoring in the emergency department.
Sameer Sundrani et al. NPJ digital medicine 2023 6(1) 60
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Bayesian network modeling of risk and prodromal markers of Parkinson's disease.
Meemansa Sood et al. PloS one 2023 18(2) e0280609
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Prediction of early-wheelchair dependence in multiple system atrophy based on machine learning algorithm: A prospective cohort study.
Lingyu Zhang et al. Clinical parkinsonism & related disorders 2023 8100183
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Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.
Fuadah Yunendah Nur et al. Diagnostics (Basel, Switzerland) 2022 12(11)
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Machine learning for predicting acute hypotension: A systematic review.
Zhao Anxing et al. Frontiers in cardiovascular medicine 2022 9937637
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Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System.
Mehrpour Omid et al. BMC pharmacology & toxicology 2022 23(1) 49
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Machine learning model to predict hypotension after starting continuous renal replacement therapy.
Kang Min Woo et al. Scientific reports 2021 11(1) 17169
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Pediatric sepsis screening in US hospitals.
Eisenberg Matthew A et al. Pediatric research 2021
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Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit.
Yoon Joo Heung et al. Critical care (London, England) 2020 Nov 24(1) 661
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Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery.
Fernandes Marta Priscila Bento et al. Journal of cardiothoracic and vascular anesthesia 2020 Jul
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Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.
Burlacu Alexandru et al. BioMed research international 2020 20209867872
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The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S).
Gratz Irwin et al. BMC anesthesiology 2020 May 20(1) 98
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Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.
Cherifa Ményssa et al. Anesthesia and analgesia 2020 May 130(5) 1157-1166
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A machine-learning approach to predicting hypotensive events in ICU settings.
Moghadam Mina Chookhachizadeh et al. Computers in biology and medicine 2020 Mar 118103626
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Development and Validation of a Predictive Model of the Risk of Pediatric Septic Shock Using Data Known at the Time of Hospital Arrival.
Scott Halden F et al. The Journal of pediatrics 2019 Nov
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The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial.
Wijnberge M et al. Trials 2019 Oct 20(1) 582
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Measurement of selected preventable adverse drug events in electronic health records: Toward developing a complexity score.
Jeon Nakyung et al. American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists 2017 Nov 74(22) 1865-1877
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Page last reviewed:
Feb 1, 2024
Page last updated:
Jun 21, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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