Mid-Frequency Bottom Scattering Model Development And Validation

2008 ◽  
Author(s):  
Lisa M. Zurk
Keyword(s):  
Model Development ◽  
Scattering Model ◽  
Development And Validation

Child neck FE model development and validation

2008 ◽  
Vol 1 (2) ◽  
pp. 244 ◽  
Author(s):  
F. Meyer ◽  
S. Roth ◽  
R. Willinger
Keyword(s):  
Model Development ◽  
Fe Model ◽  
Development And Validation

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 123-124
Keyword(s):  
Model Development ◽  
Lung Nodule ◽  
Dynamic Bayesian Network ◽  
Web Based ◽  
National Lung Screening Trial ◽  
Catastrophic Illness ◽  
Development And Validation ◽  
Low Dose Computed Tomography ◽  
Lung Screening ◽  
Heart Sound Segmentation

Arnold CW, Wallace WD, Chen S, Oh A, Abtin F, Genshaft S, Binder S, Aberle D, Enzmann D. RadPath: A web-based system for integrating and correlating radiology and pathology findings during cancer diagnosis. Acad Radiol 2016 Jan;23(1):90-100 http://escholarship.org/uc/item/22x4021q Hravnak M, Chen L, Dubrawski A, Bose E, Clermont G, Pinsky MR. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput 2016 Dec;30(6):875-88 https://link.springer.com/article/10.1007%2Fs10877-015-9788-2 Kalpathy-Cramer J, Zhao B, Goldgof D, Gu Y, Wang X, Yang H, Tan Y, Gillies R, Napel S. A comparison of lung nodule segmentation algorithms: methods and results from a multi-institutional study. J Digit Imaging 2016 Aug;29(4):476-87 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4942386/ Moss TJ, Lake DE, Calland JF, Enfield KB, Delos JB, Fairchild KD, Moorman JR. Signatures of subacute potentially catastrophic illness in the ICU: model development and validation. Crit Care Med 2016 Sep;44(9):1639-48 https://insights.ovid.com/pubmed?pmid=27452809 Petousis P, Han SX, Aberle D, Bui AA. Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network. Artif Intell Med 2016 Sep;72:42-55 https://linkinghub.elsevier.com/retrieve/pii/S0933-3657(16)30106-3 Springer DB, Tarassenko L, Clifford GD. Logistic regression-HSMM-based heart sound segmentation. IEEE Trans Biomed Eng 2016 Apr;63(4):822-32 http://ieeexplore.ieee.org/document/7234876/

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

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