ArticleHealth information science and systems2016
PredicT-ML: a tool for automating machine learning model building with big clinical data.
Article in Health information science and systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Article
- Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.Frontiers in public health · 2024Article
- Improving the Accuracy of Progress Indication for Constructing Deep Learning Models.IEEE access : practical innovations, open solutions · 2022Article
- Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.Computational intelligence and neuroscience · 2022Article
- Classification and prediction of diabetes disease using machine learning paradigm.Health information science and systems · 2020Article
- Progress Indication for Deep Learning Model Training: A Feasibility Demonstration.IEEE access : practical innovations, open solutions · 2020Article
- Big data management challenges in health research-a literature review.Briefings in bioinformatics · 2019Review
- Predicting patient-reported outcomes following hip and knee replacement surgery using supervised machine learning.BMC medical informatics and decision making · 2019Article
- A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling.Global transitions · 2019Article
- Progress Indication for Machine Learning Model Building: A Feasibility Demonstration.SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining · 2018Article
- Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining · 2017Article
- Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection.Health information science and systems · 2017Article
- Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.JMIR research protocols · 2017Article
- Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.Ethnicity & disease · 2017Review
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
backgroundPredictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs.
methodsThis paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data.
resultsThe paper presents the detailed design of PredicT-ML.
conclusionsPredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.
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Registered trials
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.