Machine learning models for predicting survival in patients with ampullary adenocarcinoma

Huang, Liying; Yang, Rui; Li, Shuna; He, Ningxia; Feng, Aozi; Li, Li; Lyu, Jun

doi:10.1016/j.apjon.2022.100141

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…Specially, we found that RSF showed better predictive efficacy for short-term outcomes. Huang et al also obtained similar findings when using RSF and Cox regression to predict mortality in patients with pot-belly adenocarcinoma over 1–10 years [ 44 ], which may be due to the fact that short-term mortality risk is easier to predict because it only needs to consider serious events that occur in a shorter period of time, whereas long-term mortality risk is subject to more unknown and confounding factors. In addition, compared to traditional scores, we found that the RSF model had the best AUC performance (AUC = 0.875), followed by SAPSII, OASIS, SIRS and SOFA.…”

Section: Discussionmentioning

confidence: 71%

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

Wang,

Deng,

Tan

et al. 2023

BMC Med Inform Decis Mak

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Objective To evaluate RSF and Cox models for mortality prediction of hemorrhagic stroke (HS) patients in intensive care unit (ICU). Methods In the training set, the optimal models were selected using five-fold cross-validation and grid search method. In the test set, the bootstrap method was used to validate. The area under the curve(AUC) was used for discrimination, Brier Score (BS) was used for calibration, positive predictive value(PPV), negative predictive value(NPV), and F1 score were combined to compare. Results A total of 2,990 HS patients were included. For predicting the 7-day mortality, the mean AUCs for RSF and Cox regression were 0.875 and 0.761, while the mean BS were 0.083 and 0.108. For predicting the 28-day mortality, the mean AUCs for RSF and Cox regression were 0.794 and 0.649, while the mean BS were 0.129 and 0.174. The mean AUCs of RSF and Cox versus conventional scores for predicting patients’ 7-day mortality were 0.875 (RSF), 0.761 (COX), 0.736 (SAPS II), 0.723 (OASIS), 0.632 (SIRS), and 0.596 (SOFA), respectively. Conclusions RSF provided a better clinical reference than Cox. Creatine, temperature, anion gap and sodium were important variables in both models.

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Section: Discussionmentioning

confidence: 71%

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

Wang,

Deng,

Tan

et al. 2023

BMC Med Inform Decis Mak

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Section: Methodsmentioning

confidence: 99%

“…An ensemble of survival gradient-boosted trees for predicting individualized risk of MACCE at 2 years will be developed using the extreme gradient boosting (XGBoost) with survival embeddings framework. 38,39 Within this framework, the XGBoost algorithm will be used to build a binary classifier that identifies subjects (possibly right-censored) that experienced MACCE during follow-up by outputting a continuous score (from 0 to 1) which is interpreted as a risk score. During training the negative loglikelihood for the accelerated failure time model is minimized by sequentially fitting decision trees with different weights distributions over the data set subjects according to the performance of the previous estimators.…”

Section: Model Developmentmentioning

confidence: 99%

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis

Cau¹,

Muscogiuri

Pisu³

et al. 2023

Journal of Thoracic Imaging

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Purpose: Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification. Methods: EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack. Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis. Conclusions: The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

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“…In recent years, deep learning (DL) and machine learning (ML) have been widely applied in mHealth [ 22 , 23 , 24 , 25 ]. In these studies, DL and ML models are not only used for diagnosing, estimating, mining, and delivering physiological signals, but also for preventing chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

Tan

Shih

Liu

et al. 2023

Sensors

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Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people’s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.

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Machine learning models for predicting survival in patients with ampullary adenocarcinoma

Cited by 6 publications

References 51 publications

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

A comparison of random survival forest and Cox regression for prediction of mortality in patients with hemorrhagic stroke

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

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