Abstract WP182: Machine Learning Models are More Accurate Than Regression-based Models for Predicting Functional Impairment Risk in Acute Ischemic Stroke

Stroke ◽  
2019 ◽  
Vol 50 (Suppl_1) ◽  
Author(s):  
Shakiru A Alaka ◽  
Anita Brobbey ◽  
Bijoy K Menon ◽  
Tyler Williamson ◽  
Mayank Goyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Impairment ◽  
Learning Models ◽  
Machine Learning Models

Abstract WP312: Identifying Acute Ischemic Stroke by Analyzing Icd-10 Claims Data Using Machine Learning Models

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Charles Esenwa ◽  
Jorge Luna ◽  
Benjamin Kummer ◽  
Hojjat Salmasian ◽  
David Vawdrey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Claims Data ◽  
Administrative Claims ◽  
Learning Models ◽  
Icd 10 ◽  
New Diagnosis ◽  
Machine Learning Models

Introduction: Retrospective identification of patients hospitalized with new diagnosis of acute ischemic stroke is important for administrative quality assurance, post-discharge clinical management, and stroke research. The benefit of using administrative claims data is its widespread availability, but the disadvantage is in the inability to accurately and consistently identify the clinical diagnosis of interest. Hypothesis: We hypothesized that decision tree and logistic regression models could be applied to administrative claims data coded using International Classification of Diseases, version 10 (ICD-10) to create algorithms that could accurately identify patients with acute ischemic stroke. Methods: We used hospital records from our institution to develop a gold standard list of 243 patients, continuously hospitalized with a new diagnosis of stroke from 10/1/2015 to 3/31/2016. We used 1,393 neurological patients without a diagnosis of stroke as negative controls. This list was used to train and test two machine learning methods of diagnosis and procedure codes analysis, for the purpose of ischemic stroke identification: one using classification and regression tree (CART) and another using regularized logistic regression. We trained the models using 75% of the data and performed the evaluation using the remaining 25%. Results: The CART model had a κ=0.78, sensitivity of 96%, specificity of 90%, and a positive predictive value of 99%. The regularized logistic regression model had a κ=0.73, sensitivity of 97%, specificity of 81%, and a positive predictive value of 98%. Conclusion: Both the decision tree and logistic regression machine based learning models showed very high accuracy in identifying patients with a new diagnosis of ischemic stroke, using ICD-10 code claims data, when compared to our gold standard. Applying these machine learning models to identify patients with ischemic stroke has widespread applications, especially in this period where national billing data has transitioned from ICD-9 to ICD-10 codes.

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.

Immune-inflammatory, coagulation, adhesion, and imaging biomarkers combined in machine learning models improve the prediction of death 1 year after ischemic stroke

2021 ◽  
Author(s):  
Ana Lucia Cruz Fürstenberger Lehmann ◽  
Daniela Frizon Alfieri ◽  
Maria Caroline Martins de Araújo ◽  
Emanuelle Roberto Trevisani ◽  
Maisa Rocha Nagao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Imaging Biomarkers ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 80
Author(s):  
I-Min Chiu ◽  
Wun-Huei Zeng ◽  
Chi-Yung Cheng ◽  
Shih-Hsuan Chen ◽  
Chun-Hung Richard Lin
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Reperfusion Therapy ◽  
Gradient Boosting ◽  
Stroke Patients ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5–6, should be considered as well before further invasive intervention. By using a machine learning algorithm, we aimed to develop a multiclass classification model for outcome prediction in acute ischemic stroke patients requiring reperfusion therapy. This was a retrospective study performed at a stroke medical center in Taiwan. Patients with acute ischemic stroke who visited between January 2016 and December 2019 and who were candidates for reperfusion therapy were included. Clinical outcomes were classified as favorable outcome, intermediate outcome, and miserable outcome. We developed four different multiclass machine learning models (Logistic Regression, Supportive Vector Machine, Random Forest, and Extreme Gradient Boosting) to predict clinical outcomes and compared their performance to the DRAGON score. A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression: 0.70, Supportive Vector Machine: 0.67, Random Forest: 0.69, and Extreme Gradient Boosting: 0.67, vs. DRAGON: 0.51, p < 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specificity. Compared with the DRAGON score, the multiclass machine learning approach showed better performance on the prediction of the 3-month functional outcome of acute ischemic stroke patients requiring reperfusion therapy.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Sign in / Sign up

Export Citation Format

Share Document