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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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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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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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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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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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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Value of laboratory results in addition to vital signs in a machine learning algorithm to predict in-hospital cardiac arrest: A single-center retrospective cohort study by Ueno, Ryo, Xu, Liyuan, Uegami, Wataru, Matsui, Hiroki, Okui, Jun, Hayashi, Hiroshi, Miyajima, Toru, Hayashi, Yoshiro, Pilcher, David, Jones, Daryl

“… Random forest models with/without laboratory results (Vitals+Labs model and Vitals-Only model, respectively…”
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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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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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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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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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An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients by Wang, Meng, Yao, Xinghua, Chen, Yixiang

“… How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random…”
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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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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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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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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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Predicting Mortality in Subarachnoid Hemorrhage Patients Using Big Data and Machine Learning: A Nationwide Study in Türkiye by Khaniyev, Taghi, Cekic, Efecan, Gecici, Neslihan Nisa, Can, Sinem, Ata, Naim, Ulgu, Mustafa Mahir, Birinci, Suayip, Isikay, Ahmet Ilkay, Bakir, Abdurrahman, Arat, Anil, Hanalioglu, Sahin

“… Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods…”
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An intelligent warning model for early prediction of cardiac arrest in sepsis patients by Layeghian Javan, Samaneh, Sepehri, Mohammad Mehdi, Layeghian Javan, Malihe, Khatibi, Toktam

“… Several studies have been conducted to predict cardiac arrest using machine learning. However, no previous research has used machine learning for predicting cardiac arrest in adult…”
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