Based on Machine Learning Algorithm: Construction of an Early Prediction Model of Integrated Traditional Chinese and Western Medicine for Cognitive Impairment after Ischemic Stroke

2020 ◽  
Author(s):  
Xinhao Chen ◽  
Chengxia Wei ◽  
Wuhui Wu ◽  
Lizhen Guo ◽  
Chengyuan Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Cognitive Impairment ◽  
Prediction Model ◽  
Western Medicine ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Algorithm Construction

scholarly journals Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052093688
Author(s):  
Daehyuk Yim ◽  
Tae Young Yeo ◽  
Moon Ho Park
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Dysfunction ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Kappa Statistics ◽  
Machine Learning Algorithm ◽  
Neuropsychological Screening ◽  
Dementia Screening

Objective To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. Methods This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant’s caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen’s kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. Results The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. Conclusion This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

scholarly journals Silent Hypoxia in Covid-19: A Machine Learning Algorithm for Early Prediction

2021 ◽  
Vol 233 (5) ◽  
pp. e191
Author(s):  
Zain I. Khalpey ◽  
Amina Khalpey ◽  
Bhavisha Modi ◽  
Jessa L. Deckwa
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Early Prediction

scholarly journals Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†

2021 ◽  
Vol 8 (3) ◽  
pp. 209-221
Author(s):  
Li-Li Wei ◽  
Yue-Shuai Pan ◽  
Yan Zhang ◽  
Kai Chen ◽  
Hao-Yu Wang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Random Forest Algorithm ◽  
Random Forest Regression ◽  
Data Set

Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction model was analyzed. Results We obtained 4806 analyzable data from 1625 pregnant women. Among these, 3265 samples with all 67 indicators were used to establish data set F1; 4806 samples with 38 identical indicators were used to establish data set F2. Each of F1 and F2 was used for training the random forest algorithm. The overall predictive accuracy of the F1 model was 93.10%, area under the receiver operating characteristic curve (AUC) was 0.66, and the predictive accuracy of GDM-positive cases was 37.10%. The corresponding values for the F2 model were 88.70%, 0.87, and 79.44%. The results thus showed that the F2 prediction model performed better than the F1 model. To explore the impact of sacrificial indicators on GDM prediction, the F3 data set was established using 3265 samples (F1) with 38 indicators (F2). After training, the overall predictive accuracy of the F3 model was 91.60%, AUC was 0.58, and the predictive accuracy of positive cases was 15.85%. Conclusions In this study, a model for predicting GDM with several input variables (e.g., physical examination, past history, personal history, family history, and laboratory indicators) was established using a random forest regression algorithm. The trained prediction model exhibited a good performance and is valuable as a reference for predicting GDM in women at an early stage of pregnancy. In addition, there are certain requirements for the proportions of negative and positive cases in sample data sets when the random forest algorithm is applied to the early prediction of GDM.

scholarly journals Machine Learning Approaches for Early Prediction of Gestational Diabetes Mellitus Based on Prospective Cohort Study

2021 ◽  
Author(s):  
Jingyuan Wang ◽  
Xiujuan Chen ◽  
Yueshuai Pan ◽  
Kai Chen ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Cohort Study ◽  
Gestational Diabetes ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Machine Learning Algorithm ◽  
Early Prediction ◽  
Test Dataset ◽  
Svm Model

Abstract Purpose: To develop and verify an early prediction model of gestational diabetes mellitus (GDM) using machine learning algorithm.Methods: The dataset collected from a pregnant cohort study in eastern China, from 2017 to 2019. It was randomly divided into 75% as the training dataset and 25% as the test dataset using the train_test_split function. Based on Python, four classic machine learning algorithm and a New-Stacking algorithm were first trained by the training dataset, and then verified by the test dataset. The four models were Logical Regression (LR), Random Forest (RT), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The sensitivity, specificity, accuracy, and area under the Receiver Operating Characteristic Curve (AUC) were used to analyse the performance of models.Results: Valid information from a total of 2811 pregnant women were obtained. The accuracies of the models ranged from 80.09% to 86.91% (RF), sensitivities ranged from 63.30% to 81.65% (SVM), specificities ranged from 79.38% to 97.53% (RF), and AUCs ranged from 0.80 to 0.82 (New-Stacking).Conclusion: This paper successfully constructed a New-Stacking model theoretically, for its better performance in specificity, accuracy and AUC. But the SVM model got the highest sensitivity, the SVM model was recommends as the prediction model for clinical.

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World (Preprint)

2021 ◽  
Author(s):  
Jincheng Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

BACKGROUND Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. Support Vector Machine (SVM) is a type of supervised learning method which analyzes data and recognizes patterns, mainly used for statistical classification and regression. OBJECTIVE From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. METHODS Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of SVM was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. RESULTS Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. CONCLUSIONS The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making. CLINICALTRIAL N/A

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World

2020 ◽  
Author(s):  
Jincheng Yang ◽  
Weilong Lin ◽  
Liming Shi ◽  
Ming Deng ◽  
Wenjing Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

Abstract Background: Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. Methods: Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of Support Vector Machine (SVM) was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. Results: Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. Conclusions: The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making.

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