Filters








1,359 Hits in 9.2 sec

Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

Mohammed Sayed, David Riaño, Jesús Villar
2021 Journal of Clinical Medicine  
Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV).  ...  We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches.  ...  Conflicts of Interest: The authors declare no conflict of interest in relation to this manuscript.  ... 
doi:10.3390/jcm10173824 pmid:34501270 fatcat:pw3ebaf6wfat3bd3nopglkkhey

Can Big Data and Machine Learning Improve Our Understanding of Acute Respiratory Distress Syndrome?

Sanket Bhattarai, Ashish Gupta, Eiman Ali, Moeez Ali, Mohamed Riad, Prakash Adhikari, Jihan A Mostafa
2021 Cureus  
Acute respiratory distress syndrome (ARDS) accounts for 10% of all diagnoses in the Intensive Care Unit, and about 40% of the patients succumb to the disease.  ...  The purpose of this study is to evaluate the role that big data and machine learning (ML) have played in understanding the heterogeneity of the disease and the development of various prediction algorithms  ...  Introduction And Background Acute respiratory distress syndrome (ARDS) occurs in 10% of all Intensive Care Unit (ICU) patients and, unfortunately, 40% of the patients with ARDS die [1] .  ... 
doi:10.7759/cureus.13529 pmid:33786236 pmcid:PMC7996475 fatcat:zvxaqf4c2rhm3eid3xtcfcjyee

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

An-Kwok Ian Wong, Patricia C. Cheung, Rishikesan Kamaleswaran, Greg S. Martin, Andre L. Holder
2020 Frontiers in Big Data  
, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.  ...  These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process.  ...  The authors used a random forest approach to predict development of ARDS using baseline Predictive model for acute respiratory distress syndrome events in ICU patients in china using machine learning algorithms  ... 
doi:10.3389/fdata.2020.579774 pmid:33693419 pmcid:PMC7931901 fatcat:5umladtmb5b23kncs5ea3vtiqi

Supervised Machine Learning for the Early Prediction of Acute Respiratory Distress Syndrome (ARDS) [article]

Sidney Le, Emily Pellegrini, Abigail Green-Saxena, Charlotte Summers, Jana Hoffman, Jacob Calvert, Ritankar Das
2020 medRxiv   pre-print
Purpose: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity.  ...  Conclusion: Supervised machine learning predictions may help predict patients with ARDS up to 48 hours prior to onset.  ...  INTRODUCTION Acute respiratory distress syndrome (ARDS) is a clinical syndrome characterized by hypoxemia in the presence of non-cardiogenic pulmonary edema, and is associated with severe inflammation.  ... 
doi:10.1101/2020.03.19.20038364 fatcat:vd232odanbasnn25w7ibnakc44

Diagnosis and Management of Acute Respiratory Distress Syndrome in a Time of COVID-19

Shayan Kassirian, Ravi Taneja, Sanjay Mehta
2020 Diagnostics  
Acute respiratory distress syndrome (ARDS) remains a serious illness with significant morbidity and mortality, characterized by hypoxemic respiratory failure most commonly due to pneumonia, sepsis, and  ...  Future improved clinical outcomes in ARDS of all causes depends upon individual patient physiological and biological endotyping in order to improve accuracy and timeliness of diagnosis as well as optimal  ...  A significant care gap remains around targeted medical therapies, but precision medicine approaches to RCTs and clinical management hold promise for improved clinical management and patient-relevant outcomes  ... 
doi:10.3390/diagnostics10121053 pmid:33291238 fatcat:7tbu3hydkfcebcjuhny2jbiqna

Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data

Gregory B Rehm, Irene Cortés-Puch, Brooks T Kuhn, Jimmy Nguyen, Sarina A Fazio, Michael A Johnson, Nicholas R Anderson, Chen-Nee Chuah, Jason Y Adams
2021 Critical Care Explorations  
Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation.  ...  This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings  ...  Our Two-Step Acute Respiratory Distress Syndrome Classification Methodology % Acute Respiratory Distress Syndrome Votes in First 24 hr Sensitivity Specificity Positive Predictive Value Negative  ... 
doi:10.1097/cce.0000000000000313 pmid:33458681 pmcid:PMC7803688 fatcat:3mlxx6wqjvdtfostnutuin35qe

A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning

Majid Afshar, Cara Joyce, Anthony Oakey, Perry Formanek, Philip Yang, Matthew M Churpek, Richard S Cooper, Susan Zelisko, Ron Price, Dmitriy Dligach
2018 AMIA Annual Symposium Proceedings  
Acute Respiratory Distress Syndrome (ARDS) is a syndrome of respiratory failure that may be identified using text from radiology reports.  ...  Linguistic pre-processing of reports was performed and text features were inputs to machine learning classifiers tuned using 10-fold cross-validation on 80% of the sample size and tested in the remaining  ...  Introduction Acute respiratory distress syndrome (ARDS) is a common manifestation of pulmonary organ failure and a syndrome with profound hypoxemia with a period prevalence of 10% in all intensive care  ... 
pmid:30815053 pmcid:PMC6371271 fatcat:zjdldyfhxnbnvgc57sasqe7j74

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

Zhongheng Zhang
2019 PeerJ  
an acute respiratory distress syndrome Clinical Trials Network.  ...  Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment.  ...  Further prospective studies are needed to evaluate the effectiveness of the prediction model in improving clinical outcomes.  ... 
doi:10.7717/peerj.7719 pmid:31576250 pmcid:PMC6752189 fatcat:wapnw3tb3rfejl6pko4hoevbpe

Machine learning for patient risk stratification for acute respiratory distress syndrome

Daniel Zeiberg, Tejas Prahlad, Brahmajee K. Nallamothu, Theodore J. Iwashyna, Jenna Wiens, Michael W. Sjoding, Bobak Mortazavi
2019 PLoS ONE  
Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions.  ...  We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant  ...  a1111111111 Introduction Acute Respiratory Distress Syndrome (ARDS) is a common and devastating critical illness, developing in 23% of patients receiving invasive mechanical ventilation, and with a  ... 
doi:10.1371/journal.pone.0214465 pmid:30921400 pmcid:PMC6438573 fatcat:ujymr6pf7bfgpmmd3uawvdrwem

A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data

Meagan A. Bechel, Adam R. Pah, Hanyu Shi, Sanjay Mehrotra, Stephen D. Persell, Shayna Weiner, Richard G. Wunderink, Luís A. Nunes Amaral, Curtis H. Weiss, Lars-Peter Kamolz
2019 PLoS ONE  
Despite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS).  ...  We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians.  ...  [1, 2] The use of low tidal volume ventilation (LTVV) for the treatment of acute respiratory distress syndrome (ARDS) is a prime example.  ... 
doi:10.1371/journal.pone.0222826 pmid:31539417 pmcid:PMC6754155 fatcat:jstxzw4o35aenaf5tg7ljmhujq

Impact of Using Simulation on Critical Care Nursing Students' Knowledge and Skills of Acute Respiratory Distress Syndrome

Fayza Ahmed Abdou, Rawhia Salah Dogham
2016 IOSR Journal of Nursing and Health Science  
Acute respiratory distress syndrome is common in critically ill patients admitted to intensive care units.  ...  Conclusion: The level of critical care nursing students' knowledge and clinical performance of Acute Respiratory Distress Syndrome were general improved after application of teaching program with simulation  ...  Critical care nursing students' skills regarding ARDS: The use of simulation as a teaching strategy in assessment and nursing care of acute respiratory distress syndrome can contribute to patient safety  ... 
doi:10.9790/1959-0505042842 fatcat:y6lmxedefrhrvjn7omxojddbrm

Metabolic and Nutrition Support in the Chronic Critical Illness Syndrome

Rifka C Schulman, Jeffrey I Mechanick
2012 Respiratory care  
Ideally, IMS should be under the supervision of a metabolic support consultative team. Further research specifically focused on the CCI population is needed to validate this current approach.  ...  Metabolic interventions are extrapolated from clinical critical care research, scientific theory, and years of CCI patient care experience.  ...  eicosapentaenoic acid (EPA), ␥-linolenic acid (GLA), and antioxidants in patients with acute respiratory distress syndrome (ARDS) or acute lung injury (ALI). 102, 103 In contrast, the recently published  ... 
doi:10.4187/respcare.01620 pmid:22663970 fatcat:gbqec6jisrd4no373e45wlb7om

Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients

Fu-Yuan Cheng, Himanshu Joshi, Pranai Tandon, Robert Freeman, David L Reich, Madhu Mazumdar, Roopa Kohli-Seth, Matthew Levin, Prem Timsina, Arash Kia
2020 Journal of Clinical Medicine  
Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h.  ...  Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU).  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jcm9061668 pmid:32492874 pmcid:PMC7356638 fatcat:7fcvebe2ufg3jou3ddnpb76gse

Guillan–Barré syndrome affects the quality of life after discharge from the ICU

SE Mataloun, B Lazzaretto, K Batista, C Campos, S Orra, M Annes, M Moock
2009 Critical Care  
with acute lung injury and acute respiratory distress syndrome.  ...  for the acute respiratory distress syndrome have been proposed.  ...  acute respiratory failure.  ... 
doi:10.1186/cc7859 pmcid:PMC4085457 fatcat:ig2pqppnxzadnapoo3c5mq3sda

A comparison of three alveolar recruitment maneuver approaches in acute lung injury and acute respiratory distress syndrome

SN Nemer, C Barbas, J Caldeira, C Coimbra, L Azeredo, V Silva, R Santos, T Carias, P Souza
2009 Critical Care  
with acute lung injury and acute respiratory distress syndrome.  ...  for the acute respiratory distress syndrome have been proposed.  ...  acute respiratory failure.  ... 
doi:10.1186/cc7846 pmcid:PMC4085444 fatcat:q3brhbpdunap7lrs6risifr6ca
« Previous Showing results 1 — 15 out of 1,359 results