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An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit [article]

Jonathan Rubin, Cristhian Potes, Minnan Xu-Wilson, Junzi Dong, Asif Rahman, Hiep Nguyen, David Moromisato
2017 arXiv   pre-print
Our work focuses on the problem of predicting the transfer of pediatric patients from the general ward of a hospital to the pediatric intensive care unit.  ...  We further combine these learned classifiers into an ensemble model and compare its performance to a modified pediatric early warning score (PEWS) baseline that relies on expert defined guidelines.  ...  Introduction Approximately 1% -3% of pediatric patients admitted to the general ward of a hospital will be transferred to the pediatric intensive care unit (PICU) due to a deterioration in health (Tucker  ... 
arXiv:1707.04958v1 fatcat:oec3bed3nrexdckzitudva23w4

Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques

Rosanna I. Comoretto, Danila Azzolina, Angela Amigoni, Giorgia Stoppa, Federica Todino, Andrea Wolfler, Dario Gregori, on behalf of the TIPNet Study Group
2021 Diagnostics  
The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry.  ...  The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their  ...  For example, several studies used MLTs for early mortality prediction, the development of sepsis, or the need for pediatric intensive care unit transfer for newly hospitalized children [33] [34] [35]  ... 
doi:10.3390/diagnostics11071299 doaj:943cb8fedd7d4482b4b8ea04d27dde30 fatcat:g3wftf4m7jav5csq4fiq755xei

Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis [article]

George A. Pappy, Melissa D. Aczon, Randall C. Wetzel, David R. Ledbetter
2021 arXiv   pre-print
The LSTM models trained with transfer learning generally performed better than the LR models, with the best LSTM model achieving an AUROC of 0.78, vs 0.66 for the LR, two hours after initiation.  ...  Timely prediction of HFNC failure can provide an indication for increasing respiratory support. This work developed and compared machine learning models to predict HFNC failure.  ...  Methods Data Sources Data for this study came from de-identified clinical observations collected in Electronic Medical Records (EMR, Cerner) of children admitted to the Pediatric Intensive Care Unit  ... 
arXiv:2111.11846v1 fatcat:gubun4zrone4nkm3kiv6ejysee

Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

Tadahiro Goto, Carlos A. Camargo, Mohammad Kamal Faridi, Robert J. Freishtat, Kohei Hasegawa
2019 JAMA Network Open  
While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.  ...  a Direct admission to intensive care unit or in- hospital death. b Admission to an inpatient care site or direct transfer to an acute care hospital.  ...  and unplanned transfers to the intensive care unit [ICU] ). 9- 15 The advantages of machine learning approaches include their ability to process complex nonlinear relationships between predictors and  ... 
doi:10.1001/jamanetworkopen.2018.6937 pmid:30646206 pmcid:PMC6484561 fatcat:hj33lhbo4ba3xke25gnsnadouu

Interpretable Deep Models for ICU Outcome Prediction

Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu
2017 AMIA Annual Symposium Proceedings  
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful  ...  Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks  ...  The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agency, or the U.S. Government.  ... 
pmid:28269832 pmcid:PMC5333206 fatcat:xppc23d4nbgofgr5b7v22vtqwm

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study

Yoshihiko Raita, Carlos A. Camargo, Charles G. Macias, Jonathan M. Mansbach, Pedro A. Piedra, Stephen C. Porter, Stephen J. Teach, Kohei Hasegawa
2020 Scientific Reports  
The outcomes were positive pressure ventilation use and intensive treatment (admission to intensive care unit and/or positive pressure ventilation use).  ...  We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventional scoring (reference) model.  ...  The funding organizations were not involved in the collection, management, or analysis of the data; preparation or approval of the manuscript; or decision to submit the manuscript for publication.  ... 
doi:10.1038/s41598-020-67629-8 pmid:32620819 fatcat:imhcdvd65jfujeld5etrxqgcoi

Applications of Machine Learning Approaches in Emergency Medicine; a Review Article

Negin Shafaf, Hamed Malek
2019 Archives of Academic Emergency Medicine  
These studies belonged to three categories: prediction and detection of disease; prediction of need for admission, discharge and also mortality; and machine learning based triage systems.  ...  In each of these categories, the most important studies have been chosen and accuracy and results of the algorithms have been briefly evaluated by mentioning machine learning techniques and used datasets  ...  Another study assessed clinical notes in the first 24 hours after admission in intensive care unit (ICU), which is extracted from MIMIC-III dataset (9) .  ... 
pmid:31555764 pmcid:PMC6732202 fatcat:zjd3l57eufbrnotveb2adnincu

Evaluating Patient Readmission Risk: A Predictive Analytics Approach [article]

Avishek Choudhury, Dr. Christopher M Greene
2018 arXiv   pre-print
There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting.  ...  Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.  ...  Kroeger et al. (2018) determines whether Pediatric Early Warning Score before transfer may serve as a predictor of unplanned readmission to the cardiac intensive care unit.  ... 
arXiv:1812.11028v1 fatcat:2mcqlrx4fndltpz45girschtdi

Comparison of Multivariable Logistic Regression and Machine Learning Models for Predicting Bronchopulmonary Dysplasia or Death in Very Preterm Infants

Faiza Khurshid, Helen Coo, Amal Khalil, Jonathan Messiha, Joseph Y. Ting, Jonathan Wong, Prakesh S. Shah
2021 Frontiers in Pediatrics  
Models were developed to predict, on days 1, 7, and 14 of admission to neonatal intensive care, the composite outcome of BPD/death prior to discharge.  ...  The top-performing algorithms will be used to develop multinomial models and an online risk estimator for predicting BPD severity and death that does not require information on ethnicity.  ...  A list of the CNN investigators and their affiliations is provided in the Supplementary Material.  ... 
doi:10.3389/fped.2021.759776 pmid:34950616 pmcid:PMC8688959 fatcat:a2flwgn3jbdibhjywoh6n676ti

Evaluating Patient Readmission Risk: A Predictive Analytics Approach

Avishek Choudhury, Dr. Christopher M. Greene
2018 American Journal of Engineering and Applied Sciences  
There are tangible works in the literature emphasizing on developing readmission risk prediction models; However, the models are not accurate enough to be deployed in an actual clinical setting.  ...  Furthermore, Genetic Algorithm and Greedy Ensemble is used to optimize the developed model constraints.  ...  Kroeger et al. (2018) determines whether Pediatric Early Warning Score before transfer may serve as a predictor of unplanned readmission to the cardiac intensive care unit.  ... 
doi:10.3844/ajeassp.2018.1320.1331 fatcat:m4cmf4q3ofbohhpllq7iu7dl4e

Realizing a Stacking Generalization Model for Improving the Prediction Accuracy of Major Depressive Disorder in Adults

Nivedhitha Mahendran, Durai Raj Vincent P M, Kathiravan Srinivasan, Vishal Sharma, Dushantha Nalin K. Jayakody
2020 IEEE Access  
Therefore, to make their work more comfortable, and to predict MDD at the early stages, we have developed an ensemble-based machine learning model.  ...  The results show that the prediction accuracy of the stacking generalization model is superior to the individual classifiers.  ...  The authors [32] have proposed an ensemble machine learning model for the clinicians to use in predicting the transfer of patients from regular ward to pediatric ICU care when it is likely.  ... 
doi:10.1109/access.2020.2977887 fatcat:53xb3eghmjhmjcuidpttd4xjtq

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units [article]

Shenda Hong, Yanbo Xu, Alind Khare, Satria Priambada, Kevin Maher, Alaa Aljiffry, Jimeng Sun, Alexey Tumanov
2020 arXiv   pre-print
To address these challenges, we propose HOLMES-an online model ensemble serving framework for healthcare applications.  ...  HOLMES dynamically identifies the best performing set of models to ensemble for highest accuracy, while also satisfying sub-second latency constraints on end-to-end prediction.  ...  ACKNOWLEDGEMENTS This work was in part supported by the National Science Foundation award IIS-1418511, CCF-1533768 and IIS-1838042, the National Institute of Health award NIH R01 1R01NS107291-01 and R56HL138415  ... 
arXiv:2008.04063v1 fatcat:lb33es3ogjgrhcbewvscgxvyla

Real-Time Bedside Root Cause Analysis (RCA) as a Catalyst for Clostridioides difficile Reduction

Hannah Newman, Linda Kirschenbaum, Irene Macyk, Daniel Baker, Janet C Haas
2020 Infection control and hospital epidemiology  
Methods: Starting in July 2015, real-time bedside RCAs were performed weekly for any HO-CDI on the unit to which the infection was attributed and on any unit from which the patient had been recently transferred  ...  The findings were documented, and changes to care were made based on the findings.  ...  Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay.  ... 
doi:10.1017/ice.2020.992 fatcat:ateoqjyjx5hyvgq6pzsifxaq7e

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach

David Castiñeira, Katherine R Schlosser, Alon Geva, Amir R Rahmani, Gaston Fiore, Brian K Walsh, Craig D Smallwood, John H Arnold, Mauricio Santillana
2020 Respiratory care  
Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay.  ...  We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects.  ...  The authors have disclosed no conflicts of interest. The study was performed at Boston Children's Hospital, Boston, Massachusetts. Dr Geva was funded by NICHD T32 HD040128 and NICHD K12 HD047349.  ... 
doi:10.4187/respcare.07561 pmid:32879034 pmcid:PMC7906608 fatcat:rzk25oz3yjen5ejjq44jjpb4tq

Distilling Knowledge from Deep Networks with Applications to Healthcare Domain [article]

Zhengping Che, Sanjay Purushotham, Robinder Khemani, Yan Liu
2015 arXiv   pre-print
Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved  ...  In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance  ...  diagnosis in Intensive Care Units (ICU).  ... 
arXiv:1512.03542v1 fatcat:j4mq2mhhl5bnjmlwihsjptp6qq
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