AI Medical Compendium Topic:
Data Mining

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Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Journal of neuroscience methods
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...

Using OWL reasoning to support the generation of novel gene sets for enrichment analysis.

Journal of biomedical semantics
BACKGROUND: The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular...

Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism.

Journal of the American College of Radiology : JACR
OBJECTIVE: The aim of this study was to quantify the variability of language in free text reports of pulmonary embolus (PE) studies and to gauge the informativeness of free text to predict PE diagnosis using machine learning as proxy for human unders...

Automated chart review utilizing natural language processing algorithm for asthma predictive index.

BMC pulmonary medicine
BACKGROUND: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Electronic health records (EHR) yet. Our objective is to develop and validate a natural language proces...

Machine Learning in Medical Imaging.

Journal of the American College of Radiology : JACR
Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as ...

Machine learning to parse breast pathology reports in Chinese.

Breast cancer research and treatment
INTRODUCTION: Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics communi...

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes.

Iranian biomedical journal
BACKGROUND: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and health administrators. The current study aimed at developing and comparing some statistical models to i...

Drug drug interaction extraction from the literature using a recursive neural network.

PloS one
Detecting drug-drug interactions (DDI) is important because information on DDIs can help prevent adverse effects from drug combinations. Since there are many new DDI-related papers published in the biomedical domain, manually extracting DDI informati...

Supervised Machine Learning for Population Genetics: A New Paradigm.

Trends in genetics : TIG
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly be...

BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics.

Database : the journal of biological databases and curation
Precision medicine aims to provide personalized treatments based on individual patient profiles. One critical step towards precision medicine is leveraging knowledge derived from biomedical publications-a tremendous literature resource presenting the...