Journal of Public Health - To investigate the attack rate of active tuberculosis (TB) cases and detection rate of latent tuberculosis infection (LTBI) cases, and to identify possible factors... 相似文献
ObjectivesTo evaluate a machine learning model designed to predict mortality for Medicare beneficiaries aged >65 years treated for hip fracture in Inpatient Rehabilitation Facilities (IRFs).DesignRetrospective design/cohort analysis of Centers for Medicare & Medicaid Services Inpatient Rehabilitation Facility–Patient Assessment Instrument data.Setting and ParticipantsA total of 17,140 persons admitted to Medicare-certified IRFs in 2015 following hospitalization for hip fracture.MeasuresPatient characteristics include sociodemographic (age, gender, race, and social support) and clinical factors (functional status at admission, chronic conditions) and IRF length of stay. Outcomes were 30-day and 1-year all-cause mortality. We trained and evaluated 2 classification models, logistic regression and a multilayer perceptron (MLP), to predict the probability of 30-day and 1-year mortality and evaluated the calibration, discrimination, and precision of the models.ResultsFor 30-day mortality, MLP performed well [acc = 0.74, area under the receiver operating characteristic curve (AUROC) = 0.76, avg prec = 0.10, slope = 1.14] as did logistic regression (acc = 0.78, AUROC = 0.76, avg prec = 0.09, slope = 1.20). For 1-year mortality, the performances were similar for both MLP (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.96) and logistic regression (acc = 0.68, AUROC = 0.75, avg prec = 0.32, slope = 0.95).Conclusion and ImplicationsA scoring system based on logistic regression may be more feasible to run in current electronic medical records. But MLP models may reduce cognitive burden and increase ability to calibrate to local data, yielding clinical specificity in mortality prediction so that palliative care resources may be allocated more effectively. 相似文献
Tea is a famous beverage that is produced from leaves of Camellia sinensis. Amongst the six major tea categories in China, dark tea is the only one that involves microbial fermentation in the manufacturing process, which contributes unique flavors and functions for the tea. In the recent decade, the reports about the biofunctions of dark teas have increased rapidly. Therefore it may be the proper time to consider dark tea as one potential homology of medicine and food. In this viewpoint, our current understanding of the chemical constituents, biological activities and possible health beneficial effects of dark teas were introduced. Some future directions and challenges to the development perspectives of dark teas were also discussed. 相似文献
Air samples were collected around industrial parks in Jiangsu, China, to allow the concentrations, profiles, and risk assessment of polybrominated dibenzo-p-dioxins and dibenzofurans (PBDD/Fs), polychlorinated naphthalenes (PCNs), and metals to be investigated. The concentrations of ΣPBDD/Fs and ΣPCNs were 1324.26–2080.98 fg/m3 (11.35–42.57 fg I-TEQ/m3) and 10,404.9–29,322.9 fg/m3 (1.32–7.19 fg I-TEQ/ m3), respectively. The highest concentration of ΣPBDD/Fs and ΣPCNs were observed at site C. PBDD/Fs were mainly dominated by PBDFs. The main contributor to the ΣPBDD/Fs in all samples was 1,2,3,4,6,7,8-HpBDF, which accounted for 25.75%–39.4%. For PCNs, the predominating homologues were tetra-, tri- and penta-CNs, which contributed 30.7%–43.3%, 24.7%–31.0%, and 10.6%–21.6%, respectively. As for metals, the pollution of As, Mn, Cr, and Ni in most samples exceeded National Ambient Air Quality Standards of China. Assessing the risk of inhalation exposure showed that there were potential carcinogenic risks to local residents.