Joshuaa D Allison-Burbank, Annahbah Conn, Daniel Vandever
Interpreting Diné Epistemologies and Decolonization to Improve Language and Literacy Instruction for Diné Children
Language, speech, and hearing services in schools
early assessment accurate staging liver fibrosis may great help clinical diagnosis treatment patients chronic hepatitis b chb aimed identify serum markers construct machine learning ml model reliably predict stage fibrosis chb patients clinical data 618 chb patients february 2017 september 2021 zhejiang provincial peoples hospital retrospectively analyzed data training cohort build model six ml models constructed based logistic regression support vector machine bayes knearest neighbor decision tree dt random forest using maximum relevance minimum redundancy mrmr gradient boosting decision tree gbdt dimensionality reduction selected features training cohort resampling method used select optimal ml model addition total 571 patients another hospital used external validation cohort verify performance model dt model constructed based five serological biomarkers included hbvdna platelet thrombin time international normalized ratio albumin area curve auc values dt model assessment liver fibrosis stages f01 f2 f3 f4 training cohort 0898 0891 0907 0944 respectively auc values dt model assessment liver fibrosis stages f01 f2 f3 f4 external validation cohort 0906 0876 0931 0933 respectively simulated risk classification based cutoff value showed classification performance dt model distinguishing hepatic fibrosis stages accurately matched pathological diagnosis results ml model five serum markers allows accurate diagnosis hepatic fibrosis stages beneficial clinical monitoring treatment chb patients