Clinical Medication Assisted Decision-making Model Considering the Genetic Detection Errors for Cancer Patient
In recent years,breakthroughs have been made in tumor genetic detection and precision medicine,and a large variety of targeted and immunological agents have been launched in the market,resulting in significantly prolonged patient survival.However,despite the large variety of cancer agents,each drug is only indicated for a limited patient population due to the tremendous individual heterogeneity of tumors.As a result,supporting physicians in predicting the acceptability of medications for patients based on the results of genetic detection is crucial for clinical practice.Meanwhile,due to biological characteristics and bioinformatics limitations,the accuracy of cancer genetic detection is low.Moreover,it is difficult to judge the data quality due to the several phases in the detection procedure,the easily magnified faults after transmission,and the unintelligible data between the steps.Therefore,we present a global risk prediction for the production process of genetic detection from the engineering management perspective and propose a clinical decision model for supporting immunotherapy doses based on tumor mutation burden biomarkers considering the genetic detection errors.The model presented in this paper can effectively reduce the interference of error rate on decision judgment,greatly enhancing the accuracy of decisions.
NETL
NSTL
万方数据
