Filters








575 Hits in 8.0 sec

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers

Md. Maniruzzaman, Md. Jahanur Rahman, Md. Al-MehediHasan, Harman S. Suri, Md. Menhazul Abedin, Ayman El-Baz, Jasjit S. Suri
2018 Journal of medical systems  
missing values and outliers when replaced by computed medians will improve the risk stratification accuracy.  ...  Thus, our objective is to develop an optimized and robust machine learning (ML) system under the assumption that missing values or outliers if replaced by a median configuration will yield higher risk  ...  Further, the data has missing values or has outliers, which further affects the performance of machine learning systems for risk stratification.  ... 
doi:10.1007/s10916-018-0940-7 pmid:29637403 pmcid:PMC5893681 fatcat:a46myqf5yfh4lphcxn4ufpvxpa

Machine Learning-Based Risk Stratification for Gestational Diabetes Management [article]

Jenny Yang, David Clifton, Jane Hirst, Foteini Kavvoura, George Farah, Lucy Mackillop, Huiqi Lu
2022 medRxiv   pre-print
This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health  ...  AbstractGestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention.  ...  This paper presents one of the largest clinical machine learning studies on GDM patient stratification and provides a proof-of-concept demonstration of how personalized patient care can be implemented  ... 
doi:10.1101/2022.06.11.22276278 fatcat:rdhqxkcv7bardadhfob3eho3be

Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

Paul Thottakkara, Tezcan Ozrazgat-Baslanti, Bradley B. Hupf, Parisa Rashidi, Panos Pardalos, Petar Momcilovic, Azra Bihorac, Zhongcong Xie
2016 PLoS ONE  
Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value.  ...  Conclusions Generalized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI.  ...  Acknowledgments Paul Thottakkara and Tezcan Ozrazgat-Baslanti contributed equally to the manuscript.  ... 
doi:10.1371/journal.pone.0155705 pmid:27232332 pmcid:PMC4883761 fatcat:xgk2riaekzbptbnwbyxvq5wsz4

Machine Learning-Based Risk Stratification for Gestational Diabetes Management

Jenny Yang, David Clifton, Jane E. Hirst, Foteini K. Kavvoura, George Farah, Lucy Mackillop, Huiqi Lu
2022 Sensors  
This study introduces a machine learning-based stratification system for identifying patients at risk of exhibiting high blood glucose levels, based on daily blood glucose measurements and electronic health  ...  Gestational diabetes mellitus (GDM) is often diagnosed during the last trimester of pregnancy, leaving only a short timeframe for intervention.  ...  The funders and supports of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.  ... 
doi:10.3390/s22134805 pmid:35808300 pmcid:PMC9268930 fatcat:qvzd35xoong3vkktw63dajwvdq

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Tim Smole, Bojan Žunkovič, Matej Pičulin, Enja Kokalj, Marko Robnik-Šikonja, Matjaž Kukar, Dimitrios I. Fotiadis, Vasileios C. Pezoulas, Nikolaos S. Tachos, Fausto Barlocco, Francesco Mazzarotto, Dejana Popović (+7 others)
2021 Computers in Biology and Medicine  
Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification.  ...  The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM.  ...  of several supervised machine learning algorithms; and the performance of the system is compared to existing risk-stratification models for SCD, cardiac death and allcause death risk-stratification, •  ... 
doi:10.1016/j.compbiomed.2021.104648 pmid:34280775 fatcat:nusmmlhegbewfdt6yusk4u3aam

Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

Alan Brnabic, Lisa M Hess
2021 BMC Medical Informatics and Decision Making  
Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data.  ...  There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions  ...  of missing values Hische et al  ... 
doi:10.1186/s12911-021-01403-2 pmid:33588830 fatcat:3bn6kc47gzcbjaknsx55m4kyge

Machine Learning Coronary Artery Disease Prediction Based on Imaging and Non-Imaging Data

Vassiliki I. Kigka, Eleni Georga, Vassilis Tsakanikas, Savvas Kyriakidis, Panagiota Tsompou, Panagiotis Siogkas, Lampros K. Michalis, Katerina K. Naka, Danilo Neglia, Silvia Rocchiccioli, Gualtiero Pelosi, Dimitrios I. Fotiadis (+1 others)
2022 Diagnostics  
In this study, a machine learning predictive model based on gradient boosting classifier is presented, aiming to identify the patients of high CAD risk and those of low CAD risk.  ...  The overall predictive accuracy of the model was 0.81, using both imaging and non-imaging data.  ...  In addition to this, a curation procedure was implemented to curate our dataset both for outliers and missing values.  ... 
doi:10.3390/diagnostics12061466 pmid:35741275 pmcid:PMC9221964 fatcat:ikvtgzprh5achati5dez7nd7iq

Predicting Risk of Developing Diabetic Retinopathy using Deep Learning [article]

Ashish Bora, Siva Balasubramanian, Boris Babenko, Sunny Virmani, Subhashini Venugopalan, Akinori Mitani, Guilherme de Oliveira Marinho, Jorge Cuadros, Paisan Ruamviboonsuk, Greg S Corrado, Lily Peng, Dale R Webster (+4 others)
2020 arXiv   pre-print
We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening.  ...  The training set was derived from 575,431 eyes, of which 28,899 had known 2-year outcome, and the remaining were used to augment the training process via multi-task learning.  ...  Acknowledgements We thank Jacqueline Shreibati, Ellery Wulczyn, and Michael Howell for review and suggestions for the manuscript, and Roy Lee, Noemi Figueroa, and the labeling software team in Google Health  ... 
arXiv:2008.04370v1 fatcat:ht2qazembvhk7gvj7yhdtwplea

A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia

Guan Wang, Yanbo Zhang, Sijin Li, Jun Zhang, Dongkui Jiang, Xiuzhen Li, Yulin Li, Jie Du
2021 Frontiers in Cardiovascular Medicine  
and clinical variables, a new machine learning algorithm was developed and verified for the individualized prediction of cardiovascular risk in post-preeclamptic women.  ...  This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women.Methods: Collecting demographic characteristics and clinical serum markers  ...  JZ, DJ, and XL provided clinical input at all stages of the project. DJ and XL collected the trial data. All authors contributed to manuscript revision, read and approved the submitted version.  ... 
doi:10.3389/fcvm.2021.736491 pmid:34778400 pmcid:PMC8578855 fatcat:c6jkv7xnhvdfpbumy2k5a5ytaa

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

Feng Xie, Marcus Eng Hock Ong, Johannes Nathaniel Min Hui Liew, Kenneth Boon Kiat Tan, Andrew Fu Wah Ho, Gayathri Devi Nadarajan, Lian Leng Low, Yu Heng Kwan, Benjamin Alan Goldstein, David Bruce Matchar, Bibhas Chakraborty, Nan Liu
2021 JAMA Network Open  
To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate  ...  The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework.  ...  Subsequently, all missing values were imputed using the median value of the training cohort.  ... 
doi:10.1001/jamanetworkopen.2021.18467 pmid:34448870 pmcid:PMC8397930 fatcat:r5xx3r36yjerpb34lstrv7lbyq

A remote healthcare monitoring framework for diabetes prediction using machine learning

Jayroop Ramesh, Raafat Aburukba, Assim Sagahyroon
2021 Healthcare technology letters  
A support vector machine was developed for diabetes risk prediction using the Pima Indian Diabetes Database, after feature scaling, imputation, selection and augmentation.  ...  This work proposes an end-to-end remote monitoring framework for automated diabetes risk prediction and management, using personal health devices, smart wearables and smartphones.  ...  ACKNOWLEDGMENT This research was funded by the American University of Sharjah FRG grant FRG17-R-20.  ... 
doi:10.1049/htl2.12010 pmid:34035925 pmcid:PMC8136765 fatcat:rspczw6a5fhqpi2durt6a26yjy

Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran

Golnar Sabetian, Aram Azimi, Azar Kazemi, Benyamin Hoseini, Naeimehossadat Asmarian, Vahid Khaloo, Farid Zand, Mansoor Masjedi, Reza Shahriarirad, Sepehr Shahriarirad
2022 Indian Journal of Critical Care Medicine  
The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively.  ...  To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care.  ...  The predictors found in the current study are mostly coherent with previously reported factors associated with the severity of disease. 1, 2, 4, 27 However, we aimed to use machine learning for risk stratification  ... 
doi:10.5005/jp-journals-10071-24226 pmid:35836646 pmcid:PMC9237161 fatcat:whcbwhxbkjdvzh5v5a43cvovn4

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis [article]

William P.T.M. van Doorn, Patricia M Stassen, Hella F. Borggreve, Maaike J. Schalkwijk, Judith Stoffers, Otto Bekers, Steven J.R. Meex
2020 medRxiv   pre-print
Development of a risk stratification tool for these patients is important for appropriate triage and early treatment.  ...  The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk  ...  Missing values did not require any processing as our machine learning model is capable of dealing with missing data.  ... 
doi:10.1101/2020.11.24.20237636 fatcat:guitkrmfvnfwdlfdxzgogqkxbq

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

William P. T. M. van Doorn, Patricia M. Stassen, Hella F. Borggreve, Maaike J. Schalkwijk, Judith Stoffers, Otto Bekers, Steven J. R. Meex, Ivan Olier
2021 PLoS ONE  
Development of a risk stratification tool for these patients is important for appropriate triage and early treatment.  ...  The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk  ...  In addition, clinical judgment of the attending physician in the ED plays an important role in risk stratification.  ... 
doi:10.1371/journal.pone.0245157 pmid:33465096 fatcat:5ziasvrd5nezfp3jwton5zgmqa

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications

Allison Zimmerman, Dinesh Kalra
2020 Reviews in cardiovascular medicine  
identification and risk stratification.  ...  of data and making associations that may have been missed.  ...  Acknowledgments We would like to express my gratitude to all those who helped me during the writing of this manuscript. Conflict of interest The authors declare no conflicts of interest statement.  ... 
doi:10.31083/j.rcm.2020.03.120 pmid:33070540 fatcat:uiwanbws2nc6tg4teqkuccj7vy
« Previous Showing results 1 — 15 out of 575 results