ArticleHealth information science and systems2015
MLBCD: a machine learning tool for big clinical data.
Article in Health information science and systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
- Unveiling the Disparities in the Field of Precision Medicine: A Perspective.Health science reports · 2025Article
- An Alternative Application of Natural Language Processing to Express a Characteristic Feature of Diseases in Japanese Medical Records.Methods of information in medicine · 2023Article
- A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population.Frontiers in public health · 2022Article
- Classification and prediction of diabetes disease using machine learning paradigm.Health information science and systems · 2020Article
- Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.Journal of diabetes research · 2020Article
- Usability Testing of an Interactive Dashboard for Surgical Quality Improvement in a Large Congenital Heart Center.Applied clinical informatics · 2019Article
- Big data management challenges in health research-a literature review.Briefings in bioinformatics · 2019Review
- 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
- PredicT-ML: a tool for automating machine learning model building with big clinical data.Health information science and systems · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise.
methodsThis paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data.
resultsThe paper describes MLBCD's design in detail.
conclusionsBy making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.
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What OpenQuestion holds
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.