ArticleHealth information science and systems2017
Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.
Article in Health information science and systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review.Current topics in medicinal chemistry · 2025Pooled it
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- Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.Network neuroscience (Cambridge, Mass.) · 2023Article
- Multi-feature computational framework for combined signatures of dementia in underrepresented settings.Journal of neural engineering · 2022Article
- Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening.Healthcare (Basel, Switzerland) · 2022Article
- A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.JMIR medical informatics · 2022Article
- Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.Journal of medical Internet research · 2022Article
- Structural and functional motor-network disruptions predict selective action-concept deficits: Evidence from frontal lobe epilepsy.Cortex; a journal devoted to the study of the nervous system and behavior · 2021Article
- Predicting cognitive impairment in outpatients with epilepsy using machine learning techniques.Scientific reports · 2021Article
- Multimodal neurocognitive markers of frontal lobe epilepsy: Insights from ecological text processing.NeuroImage · 2021Article
- Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.JMIR research protocols · 2021Article
- Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.Journal of medical Internet research · 2021Article
- Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection.Heterogenous data management, polystores, and analytics for healthcare : VLDB Workshops, Poly 2020 and DMAH 2020 virtual event, August 31 and September 4, 2020 : revised selected papers · 2021Article
- Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis.JMIR medical informatics · 2020Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
- Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis.JMIR medical informatics · 2020Article
- Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis.JMIR research protocols · 2019Article
- Predicting Appropriate Hospital Admission of Emergency Department Patients with Bronchiolitis: Secondary Analysis.JMIR medical informatics · 2019Article
- A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling.Global transitions · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMachine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era.
methodsTo address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values.
resultsWe report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization.
conclusionsThis is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.