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In the classification calculation, the data are sometimes not unique and there are different values and probabilities. Then, it is meaningful to develop the appropriate methods to make classification decision. To solve this issue, this paper proposes the machine learning methods based on a probabilistic decision tree (DT) under the multi-valued preference environment and the probabilistic multi-valued preference environment respectively for the different classification aims. First, this paper develops a data pre-processing method to deal with the weight and quantity matching under the multi-valued preference environment. In this method, we use the least common multiple and weight assignments to balance the probability of each preference. Then, based on the training data, this paper introduces the entropy method to further optimize the DT model under the multi-valued preference environment. After that, the corresponding calculation rules and probability classifications are given. In addition, considering the different numbers and probabilities of the preferences, this paper also uses the entropy method to develop the DT model under the probabilistic multi-valued preference environment. Furthermore, the calculation rules and probability classifications are similarly derived. At last, we demonstrate the feasibility of the machine learning methods and the DT models under the above two preference environments based on the illustrated examples. [ABSTRACT FROM AUTHOR]

Demands for out-of-hours primary care (OOH-PC) services are increasing. Many citizens call because of non-urgent health problems. Nevertheless, the patients' motives for requesting medical help outside office hours remains an understudied area. This study aimed to examine motives for calling OOH-PC services in various age groups. Cross-sectional paper based questionnaire study conducted during two weeks in 2015. The OOH-PC services in two Danish regions. Randomly selected patients calling the two healthcare services and accepting to participate in the study received a questionnaire on patient characteristics, health problems, and 26 pre-defined motives based on the Andersen Behavioural Model. Multivariate regression analyses were conducted for various age groups to calculate the probability of each motive to be a significant factor for the decision to call. A total of 1,871 patients were included in the study; half were parents of children aged 0-12 years. Young adults (18 to 39 years) differed significantly from other age groups as they more often stated perceived barriers and benefits such as "Own GP no time available soon enough" and "Need for quick help because of work". Young adults more often perceive barriers and benefits, which may suggest af difference in expectations regarding the purpose of out-of-hours services and accessibility. Further research is needed to address this issue and further explore the potential gap between the citizens' expectations to the OOH-PC services and the prevailing health policies. The out-of-hours primary healthcare services are increasingly contacted for non-urgent problems, but little is known about the citizens' motives for calling. Age is associated with differences in the perceived importance of various motives for calling out-of-hours care. Young adults are more often than other age groups motivated to call due to logistical issues, such as their job. [ABSTRACT FROM AUTHOR]

The unmet need for contraception is documented as a significant determinant of unintended pregnancies and high number of induced abortions. The period immediately after an abortion is recognised as a unique opportunity to offer contraceptive services. This paper explores the association between place of abortion and women's post-abortion contraceptive behaviour. The reproductive calendar data from the National Family Health Survey (NFHS-4) (2015–16) was used for this study. Multinomial logistic regression models were used to understand factors associated with post-abortion method choices. Single decrement life-tables were built to examine rates of contraceptive discontinuation and proportional hazard models were employed to examine probability and correlates of method discontinuation. About 20% of women who underwent an abortion adopted a contraceptive method by the end of one month following an abortion. The decision to choose methods like sterilisation or intrauterine contraceptive devices (IUCDs) was associated with the place of abortion, past contraceptive behaviour, number and sex of surviving children at the time of abortion, mass media exposure, and time of the abortion. Compared to women who underwent an abortion at private health facilities, women who sought abortion at public health facilities were more likely to choose permanent methods or IUCDs. Furthermore, women who opted for an IUCD were less likely to discontinue the method compared to those using short-acting modern methods. The lack of post-abortion contraceptive choices for women is evident in the low uptake of post-abortion contraceptives in private facilities and the predominant promotion of permanent methods and IUCDs in public health facilities. [ABSTRACT FROM AUTHOR]

Reliability demonstration tests are usually performed in product design or validation processes to demonstrate whether a product meets specified requirements on reliability. For binomial demonstration tests, the zero-failure test has been most commonly used due to its simplicity and use of minimum sample size to achieve an acceptable consumer's risk level. However, this test can often result in unacceptably high risk for producers as well as a low probability of passing the test even when the product has good reliability. This paper explicitly explores the interrelationship between multiple objectives that are commonly of interest when planning a demonstration test and proposes structured decision-making procedures using a Pareto front approach for selecting an optimal test plan based on simultaneously balancing multiple criteria. Different strategies are suggested for scenarios with different user priorities and graphical tools are developed to help quantify the trade-offs between choices and to facilitate informed decision making. Potential impacts of some subjective user inputs on the final decision are studied to offer insights and useful guidance for general applications. [ABSTRACT FROM AUTHOR]

Background: Cardiopulmonary resuscitation (CPR) is occurring more frequently at community hospitals but most patients undergoing CPR do not survive to discharge. Tools to predict CPR survival can be improved by the identification of high-yield clinical indicators.Objective: To identify variables associated with survival to discharge following in-hospital cardiac arrest.Methods: Retrospective cohort study of 463,530 hospital admissions from the Nationwide Inpatient Sample (2012–2016). The analysis includes adults (age ≥50) who underwent in-hospital CPR at US community hospitals.Results: Overall survival to discharge was 29.8% (95% CI: 29.5–30.1%). Age was the strongest predictor of survival and had greater prognostic value than the Charlson comorbidity index. Obesity was associated with improved survival (35.9%, 95% CI: 35.1–36.7%), whereas underweight patients had decreased survival (24.0%, 95% CI: 22.2–25.7%). Acute indicators of poor survival included hyperkalemia, hypercalcemia, and sepsis. We generated an ABCD index based upon four high-yield variables (age, body habitus, comorbidity, day of hospital admission). An ABCD score of 2 or less was a sensitive but non-specific predictor of post-CPR survival (96.8% sensitivity, 95% CI: 96.6–97.0), and those with extreme scores differed 3.8-fold with respect to post-CPR survival probability (46.0% versus 12.1%).Conclusion: Age is the strongest predictor of post-CPR survival, but body habitus is also an important indicator that may currently be underutilized. Our results support improved post-CPR survival of obese patients, consistent with an 'obesity paradox'. The ABCD score provides an efficient means of risk-stratifying patients and can be calculated in less than 1 minute. [ABSTRACT FROM AUTHOR]

Availability of information is one of the most important factors for financial decision-makers. Having complete information about the probability of losing money should always leave decision-makers better off. However, in some situations financial decision-makers prefer to know less than more. In this study we investigated the impact of selected characteristics of financial threats on individuals' decisions to avoid risk information in an incentivised online experiment. We found that threat severity, relative risk, and effectiveness of threat prevention alone do not influence decisions to avoid risk information. However, we did find an interaction effect between the first two treatments. Furthermore, our data suggest that coping style, locus of control, and anticipated emotional response are statistically significant predictors of financial risk information avoidance. [ABSTRACT FROM AUTHOR]

Students of statistics should be taught the ideas and methods that are widely used in practice and that will help them understand the world of statistics. Today, this means teaching them about Bayesian methods. In this article, I present ideas on teaching an undergraduate Bayesian course that uses Markov chain Monte Carlo and that can be a second course or, for strong students, a first course in statistics. [ABSTRACT FROM PUBLISHER]