Publications

Conventional advice to reduce the risk of breast cancer-related lymphedema (BCLE) suggests avoidance of daily-living risks, and limited research has investigated these risks.

Exponentiated models have been widely used in modeling various types of data such as survival data and insurance claims data. However, the exponentiated composite distribution models have not been explored yet. In this paper, we introduce an improvement of the one-parameter Inverse Gamma-Pareto composite model by exponentiating the random variable associated with the one-parameter Inverse Gamma-Pareto composite distribution function. The goodness-of-fit of the exponentiated Inverse Gamma-Pareto was assessed using three different insurance data sets. The two-parameter exponentiated Inverse Gamma-Pareto model outperforms the one-parameter Inverse Gamma-Pareto model in terms of goodness-of-fit measures for all datasets. In addition, the proposed exponentiated composite Inverse Gamma-Pareto model provides a very good fit with some well-known insurance datasets.

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87–96, p\textless 0.01) and specificity (90%; 95% CI: 85–93, p\textless 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90–94, p\textless 0.01; and 91%; 95% CI: 88–93, p \textless 0.01) were higher than those using tabular data (81%; 95% CI: 77–85, p\textless 0.01; and 83%; 95% CI: 76–88, p \textless 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90–96, p \textless 0.01) and specificity (92%; 95% CI: 89–94, p\textless 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

In this paper, we propose a new family of distributions, by exponentiating the random variables associated with the probability density functions of composite distributions. We also derive some mathematical properties of this new family of distributions, including the moments and the limited moments. Specifically, two special models in this family are discussed. Three real datasets were chosen, to assess the performance of these two special exponentiated-composite models. When fitting to these three datasets, these three special exponentiated-composite distributions demonstrate significantly better performance, compared to the original composite distributions.

This systematic review and meta-analysis summarized the results from nine eligible observational studies. Lithium use was significantly associated with a decrease risk of fractures. INTRODUCTION: The association between lithium use and risk of fracture is uncertain. To date, there have been no meta-analyses that have studied the association between the two. We conducted a systematic review and meta-analysis to examine the effect of lithium medication on the risk of fracture. METHODS: A comprehensive literature search was performed in PubMed, Embase, and MEDLINE to include eligible observational studies. Three reviewers conducted the literature search, study selection, study appraisal, and data abstraction independently. Random effects models were used to obtain the overall estimate for meta-analysis. Cochran s Q and Higgins I2 were used to assess heterogeneity. A funnel plot and Egger s regression test were employed to assess publication bias.