Imaging Examination and Quantitative Detection and Analysis of Gastrointestinal Diseases Based on Data Mining Technology

• Published in: Journal of medical systems Vol. 44; no. 1; pp. 31 - 15
• Main Authors: Li, Tao, Long, Liling
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2020; Springer Nature B.V
• Subjects: Accuracy; Adolescent; Adult; Aged; Aged, 80 and over; Data mining; Data Mining - methods; Diagnosis, Computer-Assisted - methods; Diagnostic Errors - prevention & control; Digestive system diseases; Distributed Analytics and Deep Learning in Health Care; Female; Gastrointestinal diseases; Gastrointestinal Diseases - diagnosis; Gastrointestinal Diseases - diagnostic imaging; Gastrointestinal Diseases - pathology; Health Informatics; Health risks; Health Sciences; Humans; Image & Signal Processing; Image Processing, Computer-Assisted - methods; Interatrial Block; Male; Medical equipment; Medical research; Medicine; Medicine & Public Health; Middle Aged; Neural Networks, Computer; Statistics for Life Sciences; Time Factors; Young Adult; Data mining technology; Gastrointestinal diseases; Quantitative detection and analysis; Imaging examination
• ISSN: 0148-5598; 1573-689X; 1573-689X
• DOI: 10.1007/s10916-019-1482-3

The medical image storage and transmission system completes the collection, storage, management, diagnosis and information processing of digital medical image information generated from digital medical devices, accumulates a large amount of data resources, and uses these valuable data resources to extract corresponding diseases. Diagnostic rules that help improve the accuracy of clinical disease diagnosis have always been the subject of medical research and management. Gastrointestinal diseases are common high-risk digestive diseases. This paper studies the imaging detection and quantitative detection and analysis of gastrointestinal diseases based on data mining, aiming to improve the accuracy of doctors’ clinical diagnosis, reduce the misdiagnosis and misdiagnosis of patients’ diseases, and reduce the burden on patients. With the high computing speed and computational accuracy of the computer, combined with the flexible analysis and judgment ability of the human body, the doctor can help the semi-structured and unstructured diagnosis problems. Experiments demonstrate the effectiveness and robustness of the proposed method.