Adaptive mobile sensor networks for structural health monitoring

Issued as final reportNational Science Foundation (U.S.

Planar Orthogonal Polynomials As Type II Multiple Orthogonal Polynomials

We show that the planar orthogonal polynomials with $l$ logarithmic singularities in the potential are the multiple orthogonal polynomials (Hermite-Pad\'e polynomials) of Type II with $l$ measures. We also find the ratio between the determinant of the moment matrix corresponding to the multiple orthogonal polynomials and the determinant of the moment matrix from the original planar measure

Lemniscate ensembles with spectral singularity

We consider a family of random normal matrix models whose eigenvalues tend to occupy lemniscate type droplets as the size of the matrix increases. Under the insertion of a point charge, we derive the scaling limit at the singular boundary point, which is expressed in terms of the solution to the model Painlev\'{e} IV Riemann-Hilbert problem. For this, we apply a version of the Christoffel-Darboux identity and the strong asymptotics of the associated orthogonal polynomials, where the latter was obtained by Bertola, Elias Rebelo, and Grava.Comment: 29 pages, 5 figure

The cost-effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England

Background: Nivolumab was the first programmed death receptor 1 (PD-1) immune checkpoint inhibitor to demonstrate long-term survival benefit in a clinical trial setting for advanced melanoma patients. Objective: To evaluate the cost effectiveness of nivolumab monotherapy for the treatment of advanced melanoma patients in England. Methods: A Markov state-transition model was developed to estimate the lifetime costs and benefits of nivolumab versus ipilimumab and dacarbazine for BRAF mutation-negative patients and versus ipilimumab, dabrafenib, and vemurafenib for BRAF mutation-positive patients. Covariate-adjusted parametric curves for time to progression, pre-progression survival, and post-progression survival were fitted based on patient-level data from two trials and long-term ipilimumab survival data. Indirect treatment comparisons between nivolumab, ipilimumab, and dacarbazine were informed by these covariate-adjusted parametric curves, controlling for differences in patient characteristics. Kaplan–Meier data from the literature were digitised and used to fit progression-free and overall survival curves for dabrafenib and vemurafenib. Patient utilities and resource use data were based on trial data or the literature. Patients are assumed to receive nivolumab until there is no further clinical benefit, assumed to be the first of progressive disease, unacceptable toxicity, or 2 years of treatment. Results: Nivolumab is the most cost-effective treatment option in BRAF mutation-negative and mutation-positive patients, with incremental cost-effectiveness ratios of £24,483 and £17,362 per quality-adjusted life year, respectively. The model results are most sensitive to assumptions regarding treatment duration for nivolumab and the parameters of the fitted parametric survival curves. Conclusions: Nivolumab is a cost-effective treatment for advanced melanoma patients in England

Safety and efficacy of etomidate and propofol anesthesia in elderly patients undergoing gastroscopy: A double-blind randomized clinical study

The aim of the present study is to compare the safety, efficacy and cost effectiveness of anesthetic regimens by compound, using etomidate and propofol in elderly patients undergoing gastroscopy. A total of 200 volunteers (65–79 years of age) scheduled for gastroscopy under anesthesia were randomly divided into the following groups: P, propofol (1.5–2.0 mg/kg); E, etomidate (0.15-0.2 mg/kg); P+E, propofol (0.75–1 mg/kg) followed by etomidate (0.075-0.1 mg/kg); and E+P, etomidate (0.075-0.01 mg/kg) followed by propofol (0.75–1 mg/kg). Vital signs and bispectral index were monitored at different time points. Complications, induction and examination time, anesthesia duration, and recovery and discharge time were recorded. At the end of the procedure, the satisfaction of patients, endoscopists and the anesthetist were evaluated. The recovery (6.1±1.2 h) and discharge times (24.8±2.8 h) in group E were significantly longer compared with groups P, P+E and E+P (P<0.05). The occurrence of injection pain in group P+E was significantly higher compared with the other three groups (P<0.05). In addition, the incidence of myoclonus and post-operative nausea and vomiting were significantly higher in group P+E compared with the other three groups (P<0.05). There was no statistical difference among the four groups with regards to the patients' immediate, post-procedure satisfaction (P>0.05). Furthermore, there was no difference in the satisfaction of anesthesia, as evaluated by the anesthetist and endoscopist, among the four groups (P>0.05). The present study demonstrates that anesthesia for gastroscopy in elderly patients can be safely and effectively accomplished using a drug regimen that combines propofol with etomidate. The combined use of propofol and etomidate has unique characteristics which improve hemodynamic stability, cause minimal respiratory depression and less side effects, provide rapid return to full activity and result in high levels of satisfaction

Explainability of Traditional and Deep Learning Models on Longitudinal Healthcare Records

Recent advances in deep learning have led to interest in training deep learning models on longitudinal healthcare records to predict a range of medical events, with models demonstrating high predictive performance. Predictive performance is necessary but insufficient, however, with explanations and reasoning from models required to convince clinicians for sustained use. Rigorous evaluation of explainability is often missing, as comparisons between models (traditional versus deep) and various explainability methods have not been well-studied. Furthermore, ground truths needed to evaluate explainability can be highly subjective depending on the clinician's perspective. Our work is one of the first to evaluate explainability performance between and within traditional (XGBoost) and deep learning (LSTM with Attention) models on both a global and individual per-prediction level on longitudinal healthcare data. We compared explainability using three popular methods: 1) SHapley Additive exPlanations (SHAP), 2) Layer-Wise Relevance Propagation (LRP), and 3) Attention. These implementations were applied on synthetically generated datasets with designed ground-truths and a real-world medicare claims dataset. We showed that overall, LSTMs with SHAP or LRP provides superior explainability compared to XGBoost on both the global and local level, while LSTM with dot-product attention failed to produce reasonable ones. With the explosion of the volume of healthcare data and deep learning progress, the need to evaluate explainability will be pivotal towards successful adoption of deep learning models in healthcare settings.Comment: 21 pages, 10 figure