MATERIALS AND METHODS: We investigated Google Trends® for popular search relating to medication errors, risk management and shift work. Relative search volumes (RSVs) were evaluated from 2008 to 2018. A comparison between RSV curves related to medication errors, risk management and shift work was carried out. Then, we compared the world to Italian search.
RESULTS: RSVs were persistently higher for risk management than for medication errors (mean RSVs 069 vs. 48%) and RSVs were stably higher for medication errors than shift work (mean RSVs 48 vs. 22%). In Italy, RSVs were much lower compared to the rest of the world, and RSVs for medication errors during the study period were negligible. Mean RSVs for risk management and shift work were 3 and 25%, respectively. RSVs related to medication errors and clinical risk management were correlated (r=0.520, p<0.0001).
CONCLUSIONS: Google Trends® search query volumes related to medication errors, risk management and shift work are different. RSVs for risk management are higher, and they are correlated with medication errors. Also, shift work search appears to be lower. These results should be interpreted in order to correctly evaluate how to decrease the number of medication errors in different health care related setting.
MATERIALS AND METHODS: Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers' scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.
RESULTS: The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).
CONCLUSION: The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.