In this paper, a concept of fuzzy semientropy is proposed to quantify the downside uncertainty. Several properties of fuzzy semientropy are identified and interpreted. By quantifying the downside risk with the use of semientropy, two mean-semi-entropy portfolio selection models are formulated, and a fuzzy simulation-based genetic algorithm is designed to solve the models to optimality. We carry out comparative analyses among the fuzzy mean-entropy models and the fuzzy mean-semi-entropy models and demonstrate that the mean-semi-entropy models can significantly improve the dispersion of investment. Several illustrative examples using stock dataset from the real-world financial market (China Shanghai Stock Exchange) also show the effectiveness of the models.

Background: Oral corticosteroids (OCS) are often prescribed for chronic obstructive pulmonary disease (COPD) exacerbations. Methods: This observational, individually matched historical cohort study used electronic medical records (1987– 2019) from the UK Clinical Practice Research Datalink linked to English Hospital Episode Statistics (HES) to evaluate adverse outcomes in patients with COPD who used OCS (OCS cohort) and those not exposed to OCS (non-OCS cohort). Risk of 17 adverse outcomes was estimated using proportional hazard regression. Results: Of 323,722 patients, 106,775 (33.0%) had COPD-related OCS prescriptions. Of the 106,775 patients in the overall cohort, 58,955 had HES linkage and were eligible for inclusion in the OCS cohort. The individual matching process identified 53,299 pairs of patients to form the OCS and non-OCS cohorts. Median follow-up post-index was 6.9 years (OCS cohort) and 5.4 years (non-OCS cohort). Adjusted risk of multiple adverse outcomes was higher for the OCS cohort versus the non-OCS cohort, including osteoporosis with/without fractures (adjusted hazard ratio [aHR] 1.80; 95% confidence interval [CI] 1.70– 1.92), type 2 diabetes mellitus (aHR 1.44; 95% CI 1.37– 1.51), cardiovascular/cerebrovascular disease (aHR 1.26; 95% CI 1.21– 1.30), and all-cause mortality (aHR 1.04; 95% CI 1.02– 1.07). In the OCS cohort, risk of most adverse outcomes increased with increasing categorized cumulative OCS dose. For example, risk of cardiovascular/cerebrovascular disease was 34% higher in the 1.0–< 2.5 g group versus the < 0.5 g group (HR 1.34; 95% CI 1.26– 1.42). Conclusion: Any OCS use was associated with higher risk of adverse outcomes in patients with COPD, with risk generally increasing with greater cumulative OCS dose. Plain Language Summary: Many patients with chronic obstructive pulmonary disease (COPD) have occasions when their symptoms suddenly worsen, called flare-ups or exacerbations. To treat flare-ups, doctors might prescribe a course of steroid tablets (oral corticosteroids or OCS for short). Doctors might also prescribe “rescue packs” containing OCS and antibiotics, to keep at home and start taking when needed. While OCS may speed up recovery from flare-ups, repeated use may have negative health effects. We studied effects of OCS use in patients with COPD, using anonymized electronic patient medical records in England. These databases are made available following a high-quality research proposal to research and ethics committees. Of 323,722 patients with COPD, around one-third received OCS for flare-ups. We studied 17 outcomes including important medical diagnoses and death. We grouped patients into 53,299 pairs so that every patient who used OCS matched a similar patient (eg, the same age and sex) who never used OCS. The patients were followed for an average of 6.9 years (used OCS) and 5.4 years (never used OCS). Most diagnoses, including diabetes, osteoporosis, cardiovascular/cerebrovascular disease, and death, were more likely in patients who used OCS than those who never used OCS. Patients using larger amounts of OCS over time were generally more likely to experience diagnoses or die. These results show risks of using OCS, even occasionally, in patients with COPD. Flare-up prevention is important, for example with appropriate daily “maintenance” medication, vaccinations for infections, and quitting smoking, thereby reducing health effects from OCS use for flare-ups.

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.