scholarly journals Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15‐year follow‐up study

2022 ◽  
Author(s):  
Yu‐Ning Chen ◽  
Yi‐Ning Wang ◽  
Meng‐Xi Chen ◽  
Kai Zhang ◽  
Rong‐Tian Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Uveal Melanoma ◽  
Outcome Prediction ◽  
Learning Models ◽  
Follow Up Study ◽  
Melanoma Patients ◽  
Machine Learning Models

scholarly journals Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models

2021 ◽  
pp. 109701
Author(s):  
Paula Bos ◽  
Michiel W.M. van den Brekel ◽  
Zeno A.R. Gouw ◽  
Abrahim Al-Mamgani ◽  
Marjaneh Taghavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oropharyngeal Cancer ◽  
Outcome Prediction ◽  
Learning Models ◽  
Mri Features ◽  
Improved Outcome ◽  
Machine Learning Models

scholarly journals Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models

2020 ◽  
Vol 7 (4) ◽  
pp. 212-219 ◽  
Author(s):  
Aixia Guo ◽  
Michael Pasque ◽  
Francis Loh ◽  
Douglas L. Mann ◽  
Philip R. O. Payne
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Outcome Prediction ◽  
Predictive Accuracy ◽  
Imbalanced Data ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Learning Models ◽  
Machine Learning Models

Abstract Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.

9316 Follow-up study in high-risk uveal melanoma patients

2009 ◽  
Vol 7 (2) ◽  
pp. 582
Author(s):  
P. Mariani ◽  
V. Servois ◽  
S. Piperno-Neumann ◽  
J. Couturier ◽  
C. Plancher ◽  
...  
Keyword(s):  
High Risk ◽  
Uveal Melanoma ◽  
Follow Up Study ◽  
Melanoma Patients

Machine learning models to predict the response to anti-cancer therapy in metastatic melanoma patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14071-e14071
Author(s):  
Romain Goussault ◽  
Cécile Frénard ◽  
Eve Maubec ◽  
Philippe Muller ◽  
Ludovic Martin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Targeted Therapy ◽  
Random Forest ◽  
Learning Models ◽  
Stage Iiic ◽  
Test Set ◽  
Metastatic Relapse ◽  
Class 1 ◽  
Melanoma Patients ◽  
Machine Learning Models

e14071 Background: Machine learning methods are new artificial intelligence tools with promising applications in healthcare. We developed and validated 4 machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. Methods: This work was conducted on data from 10 centers participating in the French network for Research and Clinical Investigation on Melanoma (RIC-Mel), launched in 2012. Thus, 935 patients, corresponding to 1978 systemic treatments have been extracted from RIC-Mel database. The following data were considered: age, sex, Breslow, melanoma type, ulceration, spontaneous regression, mitotic index, number of invaded lymph nodes, extracapsular extension, mutational status, melanoma stage, number of metastasis sites, lines of treatments, and time between first melanoma excision and metastatic relapse. Treatment response: complete response, partial response, stable disease, defined as class 1 and progressive disease as class 2. We split this cohort/database into a training set (80%) and test set (20%). The algorithm performances were evaluated on the test set by the percentage of treatments correctly classified in class 1 or 2. Four machine learning algorithms (linear model, random forest, XGBoost and LightGBM) were compared in terms of performance and interpretation for both types of treatments. Results: The accuracies of the best models for immunotherapy (LightGBM) and targeted therapy (random forest) were respectively 66% and 65%. The most significant variables for building the models were respectively: stage (IIIc or IV), response to previous treatments lines, age, number of metastasis sites and time between first melanoma excision and metastatic relapse. Conclusions: We present here the first machine learning models to predict the response to immunotherapy and targeted therapy in stage IIIc or IV melanoma patients. The most predictive variables are coherent with the literature. Future development will include data from 18FDG-PET/CT imaging and other predictive markers recently identified, as circulating DNA to improve the models performance.

MACHINE LEARNING MODELS SIGNIFICANT IMPROVE OUTCOME PREDICTION AFTER CARDIAC ARREST

2018 ◽  
Vol 71 (11) ◽  
pp. A775
Author(s):  
Shane Nanayakkara ◽  
Sam Fogarty ◽  
Kelvin Ross ◽  
Zoran Milosevic ◽  
Brent Richards ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Outcome Prediction ◽  
Learning Models ◽  
Improve Outcome ◽  
Machine Learning Models

scholarly journals Localized prediction of tissue outcome in acute ischemic stroke patients using diffusion- and perfusion-weighted MRI datasets

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241917
Author(s):  
Malte Grosser ◽  
Susanne Gellißen ◽  
Patrick Borchert ◽  
Jan Sedlacik ◽  
Jawed Nawabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Hybrid Approach ◽  
Learning Models ◽  
Stroke Patients ◽  
Local Approach ◽  
Prediction Approach ◽  
Machine Learning Models

Background An accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences. Material and methods Multi-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics. Results Statistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small. Conclusion The results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

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