Method for constructing lung cancer risk prediction model based on peripheral blood markers

Method for constructing lung cancer risk prediction model based on peripheral blood markers

The invention discloses a method for constructing a lung cancer risk prediction model based on peripheral blood markers. The method comprises the following steps: 1) collecting leukocyte, neutrophil absolute value, hemoglobin, lymphocyte absolute value and platelet count in peripheral blood routine...

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Hauptverfasser: LU SONGMEI, XU LINQUAN
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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XU LINQUAN
description The invention discloses a method for constructing a lung cancer risk prediction model based on peripheral blood markers. The method comprises the following steps: 1) collecting leukocyte, neutrophil absolute value, hemoglobin, lymphocyte absolute value and platelet count in peripheral blood routine examination, albumin, prealbumin, globulin and alkaline phosphatase in liver function, sodium, chlorine and iron in electrolyte, fibrinogen and D-dimer in coagulogram, CA125, CEA and cyfra21-1 in tumor markers, and CA125, CEA and cyfra21-1 in tumor markers; 27 indexes, namely, SCC, NSE, ProGRP, CD3 +, CD4 +, CD8 +, B and NK lymphocytes in the immune function, total lymphocyte count and age, are included; and 2) modeling peripheral blood test indexes of the lung cancer patient and the lung benign disease patient (G1) based on machine learning. The model has very high sensitivity and specificity; the model integrates an inflammation index, a nutrition index, a blood coagulation index, a tumor marker and an immune fun
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The method comprises the following steps: 1) collecting leukocyte, neutrophil absolute value, hemoglobin, lymphocyte absolute value and platelet count in peripheral blood routine examination, albumin, prealbumin, globulin and alkaline phosphatase in liver function, sodium, chlorine and iron in electrolyte, fibrinogen and D-dimer in coagulogram, CA125, CEA and cyfra21-1 in tumor markers, and CA125, CEA and cyfra21-1 in tumor markers; 27 indexes, namely, SCC, NSE, ProGRP, CD3 +, CD4 +, CD8 +, B and NK lymphocytes in the immune function, total lymphocyte count and age, are included; and 2) modeling peripheral blood test indexes of the lung cancer patient and the lung benign disease patient (G1) based on machine learning. 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subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
title Method for constructing lung cancer risk prediction model based on peripheral blood markers
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