The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.
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http://dx.doi.org/10.1155/2021/1285167 | DOI Listing |
Geroscience
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Dept. of Bioinformatics, Semmelweis University, 1094, Budapest, Hungary.
Age-related cognitive impairment and dementia pose a significant global health, social, and economic challenge. While Alzheimer's disease (AD) has historically been viewed as the leading cause of dementia, recent evidence reveals the considerable impact of vascular cognitive impairment and dementia (VCID), which now accounts for nearly half of all dementia cases. The Mediterranean diet-characterized by high consumption of fruits, vegetables, whole grains, fish, and olive oil-has been widely recognized for its cardiovascular benefits and may also reduce the risk of cognitive decline and dementia.
View Article and Find Full Text PDFMil Med
January 2025
Veterans Affairs Quality Scholars Fellowship, Ralph H. Johnson VA Medical Center, Charleston, SC 29412, USA.
Introduction: Cardiovascular disease (CVD) is the leading cause of death for women in the United States, and U.S. female Veterans have higher rates of CVD compared to civilian women.
View Article and Find Full Text PDFExpert Opin Drug Saf
January 2025
Department of Endocrinology, Guang'anmen Hospital of China Academy of Chinese Medical Sciences, Beijing, China.
Background: Fulminant type 1 diabetes mellitus (FT1DM) is a severe subtype of type 1 diabetes characterized by rapid onset, metabolic disturbances, and irreversible insulin secretion failure. Recent studies have suggested associations between FT1DM and certain medications, warranting further investigation.
Objectives: This study aims to analyze drugs associated with an increased risk of FT1DM using the Food and Drug Administration Adverse Event Reporting System (FAERS) database.
Cancers (Basel)
January 2025
Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA 02115, USA.
Background: The digital phenotyping tool has great potential for the deep characterization of neurological and quality-of-life assessments in brain tumor patients. Phone communication activities (details on call and text use) can provide insight into the patients' sociability.
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Cancers (Basel)
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Hybrid Technology Hub, Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway.
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