scholarly journals Automated Machine Learning Model Development for Intracranial Aneurysm Treatment Outcome Prediction: A Feasibility Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Chubin Ou ◽  
Jiahui Liu ◽  
Yi Qian ◽  
Winston Chong ◽  
Dangqi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Statistical Model ◽  
Outcome Prediction ◽  
Characteristic Curve ◽  
The Other ◽  
Statistical Prediction ◽  
Aneurysm Treatment ◽  
Automated Machine Learning ◽  
Treatment Outcome Prediction

Background: The prediction of aneurysm treatment outcomes can help to optimize the treatment strategies. Machine learning (ML) has shown positive results in many clinical areas. However, the development of such models requires expertise in ML, which is not an easy task for surgeons.Objectives: The recently emerged automated machine learning (AutoML) has shown promise in making ML more accessible to non-computer experts. We aimed to evaluate the feasibility of applying AutoML to develop the ML models for treatment outcome prediction.Methods: The patients with aneurysms treated by endovascular treatment were prospectively recruited from 2016 to 2020. Treatment was considered successful if angiographic complete occlusion was achieved at follow-up. A statistical prediction model was developed using multivariate logistic regression. In addition, two ML models were developed. One was developed manually and the other was developed by AutoML. Three models were compared based on their area under the precision-recall curve (AUPRC) and area under the receiver operating characteristic curve (AUROC).Results: The aneurysm size, stent-assisted coiling (SAC), and posterior circulation were the three significant and independent variables associated with treatment outcome. The statistical model showed an AUPRC of 0.432 and AUROC of 0.745. The conventional manually trained ML model showed an improved AUPRC of 0.545 and AUROC of 0.781. The AutoML derived ML model showed the best performance with AUPRC of 0.632 and AUROC of 0.832, significantly better than the other two models.Conclusions: This study demonstrated the feasibility of using AutoML to develop a high-quality ML model, which may outperform the statistical model and manually derived ML models. AutoML could be a useful tool that makes ML more accessible to the clinical researchers.

Machine learning and treatment outcome prediction for oral cancer

2020 ◽  
Vol 49 (10) ◽  
pp. 977-985 ◽  
Author(s):  
Chui S. Chu ◽  
Nikki P. Lee ◽  
John Adeoye ◽  
Peter Thomson ◽  
Siu‐Wai Choi
Keyword(s):  
Machine Learning ◽  
Treatment Outcome ◽  
Oral Cancer ◽  
Outcome Prediction ◽  
Treatment Outcome Prediction

scholarly journals Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning (Preprint)

2019 ◽  
Author(s):  
Karen-Inge Karstoft ◽  
Ioannis Tsamardinos ◽  
Kasper Eskelund ◽  
Søren Bo Andersen ◽  
Lars Ravnborg Nissen
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Linear Models ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Ptsd Symptoms ◽  
Forest Models ◽  
Random Forest Models ◽  
Automated Machine Learning ◽  
Military Rank

BACKGROUND Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. OBJECTIVE This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. METHODS Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). RESULTS Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. CONCLUSIONS Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

scholarly journals Erratum: Single-Subject Anxiety Treatment Outcome Prediction using Functional Neuroimaging

2018 ◽  
Vol 43 (4) ◽  
pp. 926-926 ◽  
Author(s):  
Tali M Ball ◽  
Murray B Stein ◽  
Holly J Ramsawh ◽  
Laura Campbell-Sills ◽  
Martin P Paulus
Keyword(s):  
Treatment Outcome ◽  
Outcome Prediction ◽  
Functional Neuroimaging ◽  
Single Subject ◽  
Anxiety Treatment ◽  
Treatment Outcome Prediction

scholarly journals Relevance of Baseline Viral Genetic Heterogeneity and Host Factors for Treatment Outcome Prediction in Hepatitis C Virus 1b-Infected Patients

PLoS ONE ◽  
2013 ◽  
Vol 8 (8) ◽  
pp. e72600 ◽  
Author(s):  
Verónica Saludes ◽  
Elisabet Bascuñana ◽  
Elena Jordana-Lluch ◽  
Sònia Casanovas ◽  
Mercè Ardèvol ◽  
...  
Keyword(s):  
Treatment Outcome ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Genetic Heterogeneity ◽  
Outcome Prediction ◽  
Host Factors ◽  
Treatment Outcome Prediction

scholarly journals Machine Learning Model for Predicting Outcomes of Biologic Therapy in Psoriasis

2021 ◽  
Author(s):  
Amy X. Du ◽  
Zarqa Ali ◽  
Kawa K. Ajgeiy ◽  
Maiken G. Dalager ◽  
Tomas N. Dam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Model ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Biologic Therapy ◽  
Characteristic Curve ◽  
Drug Discontinuation ◽  
Biologic Treatment ◽  
Cox Regression Analysis ◽  
Biologic Drug

AbstractBackgroundBiological agents used for the therapy of psoriasis lose efficacy over time, which leads to discontinuation of the drug. Optimization of long-term biologic treatment is an area of medical need but there are currently no prediction tools for biologic drug discontinuation.ObjectiveTo compare the accuracy of the risk factor-based frequentist statistical model to machine learning to predict the 5-year probability of biologic drug discontinuation.MethodsThe national Danish psoriasis biologic therapy registry, Dermbio, comprising 6,172 treatment series with anti-TNF (Etanercept, Infliximab, Adalimumab), Ustekinumab, Guselkumab and anti-IL17 (Secukinumab and Ixekizumab) in 3,388 unique patients was used as data source. Hazard ratios (HR) were computed for all available predictive factors using Cox regression analysis. Different machine learning (ML) models for the prediction of 5-year risk of drug discontinuation were trained using the 5-fold cross validation technique and using 10 clinical features routinely assessed in psoriasis patients as input variables. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe lowest 5-year risk of discontinuation was associated with therapy with ustekinumab or ixekizumab, male sex and no previous exposure to biologic therapy. The predictive model based on those risk factors had an AUC of 0.61. The best ML model (gradient boosted tree) had an AUC of 0.85.ConclusionsA machine learning-based approach, more than a statistical model, accurately predicts the risk of discontinuation of biologic therapy based on simple patient variables available in clinical practice. ML might be incorporated into clinical decision making.

Sign in / Sign up

Export Citation Format

Share Document