scholarly journals Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning

2019 ◽  
Vol 65 (4) ◽  
pp. 1867-1890 ◽  
Author(s):  
Colin F. Camerer ◽  
Gideon Nave ◽  
Alec Smith
Keyword(s):  
Machine Learning ◽  
Information Theory ◽  
Private Information ◽  
Outcome Prediction

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 A quantitative approach for comparing statistical classifications founded in machine learning and information theory

2020 ◽  
Author(s):  
Hervé Guillon ◽  
Belize Lane ◽  
Colin Francis Byrne ◽  
Gregory Brian Pasternack ◽  
Samuel Sandoval Solis
Keyword(s):  
Machine Learning ◽  
Information Theory ◽  
Quantitative Approach

scholarly journals Preoperative Heart Rate Variability During Sleep Predicts Vagus Nerve Stimulation Outcome Better in Patients With Drug-Resistant Epilepsy

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Fang ◽  
Hong-Yun Liu ◽  
Zhi-Yan Wang ◽  
Zhao Yang ◽  
Tung-Yang Cheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Outcome Prediction ◽  
Healthy Controls ◽  
Drug Resistant ◽  
Sleep State ◽  
Drug Resistant Epilepsy

Objective: Vagus nerve stimulation (VNS) is an adjunctive and well-established treatment for patients with drug-resistant epilepsy (DRE). However, it is still difficult to identify patients who may benefit from VNS surgery. Our study aims to propose a VNS outcome prediction model based on machine learning with multidimensional preoperative heart rate variability (HRV) indices.Methods: The preoperative electrocardiography (ECG) of 59 patients with DRE and of 50 healthy controls were analyzed. Responders were defined as having at least 50% average monthly seizure frequency reduction at 1-year follow-up. Time domain, frequency domain, and non-linear indices of HRV were compared between 30 responders and 29 non-responders in awake and sleep states, respectively. For feature selection, univariate filter and recursive feature elimination (RFE) algorithms were performed to assess the importance of different HRV indices to VNS outcome prediction and improve the classification performance. Random forest (RF) was used to train the classifier, and leave-one-out (LOO) cross-validation was performed to evaluate the prediction model.Results: Among 52 HRV indices, 49 showed significant differences between DRE patients and healthy controls. In sleep state, 35 HRV indices of responders were significantly higher than those of non-responders, while 16 of them showed the same differences in awake state. Low-frequency power (LF) ranked first in the importance ranking results by univariate filter and RFE methods, respectively. With HRV indices in sleep state, our model achieved 74.6% accuracy, 80% precision, 70.6% recall, and 75% F1 for VNS outcome prediction, which was better than the optimal performance in awake state (65.3% accuracy, 66.4% precision, 70.5% recall, and 68.4% F1).Significance: With the ECG during sleep state and machine learning techniques, the statistical model based on preoperative HRV could achieve a better performance of VNS outcome prediction and, therefore, help patients who are not suitable for VNS to avoid the high cost of surgery and possible risks of long-term stimulation.

scholarly journals An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records (Preprint)

2020 ◽  
Author(s):  
Xiaolin Diao ◽  
Yanni Huo ◽  
Zhanzheng Yan ◽  
Haibin Wang ◽  
Jing Yuan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Stenosis ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Thyroid Dysfunction ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Secondary Hypertension ◽  
Validation Dataset ◽  
Composite Outcome

BACKGROUND Secondary hypertension is a kind of hypertension with definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from detection and treatment in time and, conversely, will have higher risk of morbidity and mortality than patients with primary hypertension. OBJECTIVE The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension. METHODS The analyzed dataset was retrospectively extracted from electronic medical records (EMRs) of patients discharged from Fuwai hospital between January 1, 2016 and June 30, 2019. A total of 7532 unique patients were included and divided into two datasets by time: 6302 patients in 2016-2018 as training dataset for model building and 1230 patients in 2019 as validation dataset for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop five prediction models of four etiologies of secondary hypertension and occurrence of any of them, including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction and aortic stenosis. Both univariate logistic analysis and Gini impure method were used for feature selection, while grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model. RESULTS Validation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation dataset, while the four prediction models of RVH, PA, thyroid dysfunction and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, 0.946, respectively, in the validation dataset. 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults. CONCLUSIONS The ML prediction models in this study showed good performance in detecting four etiologies of patients with suspected secondary hypertension, thus they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way. CLINICALTRIAL

scholarly journals Incorporating domain knowledge in machine learning for soccer outcome prediction

2018 ◽  
Vol 108 (1) ◽  
pp. 97-126 ◽  
Author(s):  
Daniel Berrar ◽  
Philippe Lopes ◽  
Werner Dubitzky
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Outcome Prediction
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