Search Results - Predicting the arrest using Random forest Algorithm

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest by Kwon, Joon‐myoung, Lee, Youngnam, Lee, Yeha, Lee, Seungwoo, Park, Jinsik

“… The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation…”
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Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing by Fernandes, Marta, Mendes, Rúben, Vieira, Susana M., Leite, Francisca, Palos, Carlos, Johnson, Alistair, Finkelstein, Stan, Horng, Steven, Celi, Leo Anthony

“…-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016…”
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Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study by Pirneskoski, Jussi, Tamminen, Joonas, Kallonen, Antti, Nurmi, Jouni, Kuisma, Markku, Olkkola, Klaus T., Hoppu, Sanna

“… Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs…”
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Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards by Churpek, Matthew M, Yuen, Trevor C, Winslow, Christopher, Meltzer, David O, Kattan, Michael W, Edelson, Dana P

“… arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines…”
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Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach by Chang, Sheng-Nan, Hu, Nian-Ze, Wu, Jo-Hsuan, Cheng, Hsun-Mao, Caffrey, James L., Yu, Hsi-Yu, Chen, Yih-Sharng, Hsu, Jiun, Lin, Jou-Wei

“… The discriminatory power (DP) for predicting in-hospital mortality was tested using both random forest (RF…”
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Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient by Niu, Yanxiang, Chen, Xin, Fan, Jianqi, Liu, Chunli, Fang, Menghao, Liu, Ziquan, Meng, Xiangyan, Liu, Yanqing, Lu, Lu, Fan, Haojun

“… Using the I-CARE database, we analyzed EEG, ECG, and clinical data from comatose cardiac arrest patients…”
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A Method for Predicting Cardiovascular Disorder using Machine Learning Techniques by Hegde, Ramakrishna, Pavithra, D R, Shivashankara, S, Prasanna Kumar, G, Soumyasri, S M, Nagashree, S

“… In most of these cases people experience cardiac arrest, some of which are normal. First, families of patients are vulnerable because it takes only a few minutes for a person to die of a heart attack, and it is difficult to get medical help in time…”
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Machine Learning Models for Predicting in-Hospital Cardiac Arrest: A Comparative Analysis with Logistic Regression by Chang, Wei-Shan, Hsiao, Kai-Yuan, Lin, Lian-Yu, Chen, MingChih, Shia, Ben-Chang, Lin, Chung-Yu

“…To develop and compare multiple machine learning (ML) algorithms with traditional logistic regression for predicting in-hospital cardiac arrest (IHCA…”
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Application of machine learning techniques to predict rupture propagation and arrest in 2-D dynamic earthquake simulations by Ahamed, Sabber, Daub, Eric G

“… Two models have been developed using neural networks and the random forest to predict if a rupture can break 2-D geometrically complex fault…”
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Development and validation of a machine learning model for predicting mortality risk in veno-arterial extracorporeal membrane oxygenation patients by Gao, Hanming, Huang, Xiaolin, Zhou, Kaihuan, Ling, Yicong, Chen, Yin, Mou, Chenglin, Li, Shuanglei, Lu, Junyu

“… mortality.Feature selection was conducted using Lasso regression…”
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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study by Kwun, Ju-Seung, Ahn, Houng-Beom, Kang, Si-Hyuck, Yoo, Sooyoung, Kim, Seok, Song, Wongeun, Hyun, Junho, Oh, Ji Seon, Baek, Gakyoung, Suh, Jung-Won

“… and manage medical costs more efficiently. This study aimed to use the Observational Medical Outcomes Partnership Common Data Model to develop a predictive model by applying machine learning algorithms that can effectively predict major…”
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Predicting Arrest Release Outcomes: A Comparative Analysis of Machine Learning Models by Adebayo, O. P., Ibrahim, Ahmed, Oyeleke, K.T.

“…This comparative study evaluates machine learning models for predicting arrest release outcomes using 5,226 marijuana possession cases from the Toronto Police Service (1997-2002…”
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Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol by Vatankhah Tarbebar, Majid, Mohammadi, Milad, Mehrnoush, Vahid, Darsareh, Fatemeh

“… Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that will assist surgeons in identifying the risk factors associated with the development of postoperative complications after general…”
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Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies by Müller, Michael, Rossetti, Andrea O., Zimmermann, Rebekka, Alvarez, Vincent, Rüegg, Stephan, Haenggi, Matthias, Z’Graggen, Werner J., Schindler, Kaspar, Zubler, Frédéric

“… Random forest classifiers were trained using 8 visual EEG features—first alone, then in combination with clinical features…”
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Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest by Mayampurath, Anoop, Hagopian, Raffi, Venable, Laura, Carey, Kyle, Edelson, Dana, Churpek, Matthew

“… Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques…”
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Computationally Efficient Early Prognosis of the Outcome of Comatose Cardiac Arrest Survivors Using Slow-Wave Activity Features in EEG by Salminen, Miikka, Partala, Juha, Vayrynen, Eero, Kortelainen, Jukka

“… Authors' team Cerenion developed a random forest based machine learning algorithm. A feature set of channel-by-channel root mean square power of a well-described neurophysiological EEG phenomenon called slow-wave activity (SWA…”
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Towards comparing and using Machine Learning techniques for detecting and predicting Heart Attack and Diseases by Obasi, Thankgod, Omair Shafiq, M.

“… This knowledge can be acquired using various data mining techniques to mine knowledge by designing models from the medical records dataset…”
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Machine learning models for predicting in-hospital mortality in patient with sepsis: Analysis of vital sign dynamics by Cheng, Chi-Yung, Kung, Chia-Te, Chen, Fu-Cheng, Chiu, I-Min, Lin, Chun-Hung Richard, Chu, Chun-Chieh, Kung, Chien Feng, Su, Chih-Min

“…PurposeTo build machine learning models for predicting the risk of in-hospital death in patients with sepsis within 48 h, using only dynamic changes in the patient's vital…”
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Predicting Myocardial Rupture after Acute Myocardial Infarction in Hospitalized Patients using Machine Learning by Azwari, Sana Al

“… That is also the reason why coronary artery diseases, including MI, cardiac arrest, and heart failure, have been labeled a disease of senior citizens…”
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A machine learning model for predicting short‐term outcomes after rapid response system activation by Naito, Takaki, Li, Micheal, Fujitani, Shigeki

“… To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic…”
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