METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.
METHODS: This is a case series of 11 patients with history of previous IPS who underwent TC insertion under combined fluoroscopic and sonographic (CFS) guidance with preperitoneal tunneling at our center.
RESULTS: This is an interim result of our study. The mean age of the patients was 49.1 (±12.7). Seven were females, and four were males. Only two patients underwent more than one IPS prior to this procedure. The mean body mass index (BMI) of patients was 29.2 kg/m2 (±6.2). All patients underwent the procedure successfully. One patient developed post-procedure exit site bleeding which resolved spontaneously. One patient had urgent-start peritoneal dialysis (PD) (less than 72 h), and two patients had early-start PD (less than 2 weeks). Median catheter survival is 8 months at the time of writing.
CONCLUSION: CFS-assisted TC insertion with preperitoneal tunneling for patients with previous IPS is a safe and effective technique.
METHOD: We searched relevant electronic databases, including PUBMED, MEDLINE, and SCOPUS, and performed a systematic review. Keywords used were "necrotizing fasciitis" or "necrotising fasciitis" or "necrotizing soft tissue infections" and "point-of-care ultrasonography" "ultrasonography" or "ultrasound". No temporal limitation was set. An additional search was performed via google scholar, and the top 100 entry was screened.
RESULTS: Among 540 papers screened, only 21 were related to diagnosing necrotizing fasciitis using ultrasonography. The outcome includes three observational studies, 16 case reports, and two case series, covering the period from 1976 to 2022.
CONCLUSION: Although the use of ultrasonography in diagnosing NF was published in several papers with promising results, more studies are required to investigate its diagnostic accuracy and potential to reduce time delay before surgical intervention, morbidity, and mortality.
METHODS: Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.
RESULTS: Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).
CONCLUSIONS: Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.