The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations
Study Type
OBSERVATIONAL
Enrollment
2,000
According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations.
Qilu Hospital, Shandong University
Jinan, Shandong, China
The diagnostic accuracy of gastric diseases with deep learning algorithm
The diagnostic accuracy of gastric diseases with deep learning algorithm
Time frame: 12 month
The accuracy of recommentions for different disease with deep learning algorithm
The accuracy of recommentions for different disease with deep learning algorithm
Time frame: 12 month
The diagnostic sensitivity of gastric diseases with deep learning algorithm
The diagnostic sensitivity of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic specificity of gastric diseases with deep learning algorithm
The diagnostic specificity of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic positive predictive value of gastric diseases with deep learning algorithm
The diagnostic positive predictive valu of gastric diseases with deep learning algorithm
Time frame: 12 month
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
The diagnostic negative predictive value of gastric diseases with deep learning algorithm
Time frame: 12 month
The F-score of gastric diseases with deep learning algorithm
The F-score of gastric diseases with deep learning algorithm
Time frame: 12 month
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