Economic evaluation of the introduction of the Prostate Health Index as a rule-out test to avoid unnecessary biopsies in men with prostate specific antigen levels of 4-10 in Hong Kong

A recent study showed that the Prostate Health Index may avoid unnecessary biopsies in men with prostate specific antigen 4-10ng/ml and normal digital rectal examination in the diagnosis of prostate cancer in Hong Kong. This study aimed to conduct an economic evaluation of the impact of adopting this commercially-available test in the Hong Kong public health service to determine whether further research is justified. A cost-consequence analysis was undertaken comparing the current diagnostic pathway with a proposed diagnostic pathway using the Prostate Health Index. Data for the model was taken from a prospective cohort study recruited at a single-institution and micro-costing studies. Using a cut off PHI score of 35 to avoid biopsy would cost HK$3,000 and save HK$7,988 per patient in biopsy costs and HK$511 from a reduction in biopsy-related adverse events. The net cost impact of the change was estimated to be HK$5,500 under base case assumptions. At the base case sensitivity and specificity for all grades of cancer (61.3% and 77.5% respectively) all grade cancer could be missed in 4.22% of the population and high grade cancer in 0.53%. The introduction of the prostate health index into the diagnostic pathway for prostate cancer in Hong Kong has the potential to reduce biopsies, biopsy costs and biopsy-related adverse events. Policy makers should consider the clinical and economic impact of this proposal

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec

Energy Cost of Slow and Normal Gait Speeds in Low and Normally Functioning Adults

Objective Slow walking speed paired with increased energy cost is a strong predictor for mortality and disability in older adults but has yet to be examined in a heterogeneous sample (ie, age, sex, disease status). The aim of this study was to examine energy cost of slow and normal walking speeds among low- and normal-functioning adults. Design Adults aged 20–90 yrs were recruited for this study. Participants completed a 10-m functional walk test at a self-selected normal walking speed and were categorized as low functioning or normal functioning based on expected age- and sex-adjusted average gait speed. Participants completed two successive 3-min walking stages, at slower than normal and normal walking speeds, respectively. Gas exchange was measured and energy cost per meter (milliliter per kilogram per meter) was calculated for both walking speeds. Results Energy cost per meter was higher (P \u3c 0.0001) in the low-functioning group (n = 76; female = 59.21%; mean ± SD age = 61.13 ± 14.68 yrs) during the slower than normal and normal (P \u3c 0.0001) walking speed bouts compared with the normal-functioning group (n = 42; female = 54.76%; mean ± SD age = 51.55 ± 19.51 yrs). Conclusions Low-functioning adults rely on greater energy cost per meter of walking at slower and normal speeds. This has implications for total daily energy expenditure in low-functioning, adult populations

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.Comment: 13 pages, 6 figures, Accepted at Medical Image Analysis Journal (MedIA

A Regularized Graph Layout Framework for Dynamic Network Visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material (animations and MATLAB toolbox) available at http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201

Comparative Survival of Asian and White Metastatic Castration-Resistant Prostate Cancer Men Treated With Docetaxel

There are few data regarding disparities in overall survival (OS) between Asian and white men with metastatic castration-resistant prostate cancer (mCRPC). We compared OS of Asian and white mCRPC men treated in phase III clinical trials with docetaxel and prednisone (DP) or a DP-containing regimen. Individual participant data from 8820 men with mCRPC randomly assigned on nine phase III trials to receive DP or a DP-containing regimen were combined. Men enrolled in these trials had a diagnosis of prostate adenocarcinoma. The median overall survival was 18.8 months (95% confidence interval [CI] = 17.4 to 22.1 months) and 21.2 months (95% CI = 20.8 to 21.7 months) for Asian and white men, respectively. The pooled hazard ratio for death for Asian men compared with white men, adjusted for baseline prognostic factors, was 0.95 (95% CI = 0.84 to 1.09), indicating that Asian men were not at increased risk of death. This large analysis showed that Asian men did not have shorter OS duration than white men treated with docetaxel

Tropical Cyclones and Climate Change

Trabajo presentado en: 10th International Worskshop Cyclones Tropicales, celebrado del 5 al 9 de diciembre de 2022 en Bali, Indonesia.A substantial number of studies have been published since the IWTC-9 in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. They reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scale. However, there is still substantial uncertainty owing to model uncertainty in simulating historical TC decadal variability in the Atlantic and owing to limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and Medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453