Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

www.mobilehealthmap.org 617‐442‐3200 New research shows that mobile health clinics improve health outcomes for hard to reach populations in cost‐effective and culturally competent ways . A Harvard Medical School study determined that for every dollar invested in a mobile health clinic, the US healthcare system saves $30 on average. Mobile health clinics, which offer a range of services from preventive screenings to asthma treatment, leverage their mobility to treat people in the convenience of their own communities. For example, a mobile health clinic in Baltimore, MD, has documented savings of $3,500 per child seen due to reduced asthma‐related hospitalizations. The estimated 2,000 mobile health clinics across the country are providing similarly cost‐effective access to healthcare for a wide range of populations. Many successful mobile health clinics cite their ability to foster trusting relationships. Qualitative research in such mobile health clinics has found that patients value the informal, familiar environment in a convenient location, with staff who “are easy to talk to,” and that the staff’s “marriage of professional and personal discourses” provides patients the space to disclose information themselves. A communications academic argued that mobile health clinics’ unique use of space is important in facilitating these relationships. Mobile health clinics park in the heart of the community in familiar spaces, like shopping centers or bus stations, which lend themselves to the local community atmosphere.

Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care

Statistical Analysis With Missing Data (2nd ed.) (Book)

State-Trait Anxiety Inventoryによるペインクリニック外来患者の不安の評価

Analysis of Contingency Tables

This research is motivated by one of our survey studies to assess the potential influence of introducing zebra mussels to the Lake Mead National Recreation Area, Nevada. One research question in this study is to investigate the association between the boating activity type and the awareness of zebra mussels. A chi-squared test is often used for testing independence between two factors with nominal levels. When the null hypothesis of independence between two factors is rejected, we are often left wondering where does the significance come from. Cell residuals, including standardized residuals and adjusted residuals, are traditionally used in testing for cell significance, which is often known as a post hoc test after a statistically significant chi-squared test. In practice, the limiting distributions of these residuals are utilized for statistical inference. However, they may lead to different conclusions based on the calculated p-values, and their p-values could be over- o6r under-estimated due to the unsatisfactory performance of asymptotic approaches with regards to type I error control. In this article, we propose new exact p-values by using Fisher's approach based on three commonly used test statistics to order the sample space. We theoretically prove that the proposed new exact p-values based on these test statistics are the same. Based on our extensive simulation studies, we show that the existing asymptotic approach based on adjusted residual is often more likely to reject the null hypothesis as compared to the exact approach due to the inflated family-wise error rates as observed. We would recommend the proposed exact p-value for use in practice as a valuable post hoc analysis technique for chi-squared analysis.

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Fisher's exact approach for post hoc analysis of a chi-squared test

Simon's optimal two-stage design has been widely used in early phase clinical trials for Oncology and AIDS studies with binary endpoints. With this approach, the second-stage sample size is fixed when the trial passes the first stage with sufficient activity. Adaptive designs, such as those due to Banerjee and Tsiatis (2006) and Englert and Kieser (2013), are flexible in the sense that the second-stage sample size depends on the response from the first stage, and these designs are often seen to reduce the expected sample size under the null hypothesis as compared with Simon's approach. An unappealing trait of the existing designs is that they are not associated with a second-stage sample size, which is a non-increasing function of the first-stage response rate. In this paper, an efficient intelligent process, the branch-and-bound algorithm, is used in extensively searching for the optimal adaptive design with the smallest expected sample size under the null, while the type I and II error rates are maintained and the aforementioned monotonicity characteristic is respected. The proposed optimal design is observed to have smaller expected sample sizes compared to Simon's optimal design, and the maximum total sample size of the proposed adaptive design is very close to that from Simon's method. The proposed optimal adaptive two-stage design is recommended for use in practice to improve the flexibility and efficiency of early phase therapeutic development.

Optimal adaptive two-stage designs for early phase II clinical trials

Objective: To examine and compare factors associated with making the decision to vacate a job (organizational turnover) versus leaving the profession (professional turnover) among registered nurses (RN) in the United States (U.S.). Methods: Nationally representative data from the 2008 National Sample Survey of Registered Nurses was used. The sample consisted of 8,796 RNs who held an active RN license as of March 10, 2008, but changed a place of work or left the profession entirely. The analysis has been performed using SAS, version 9.3. Results: The results of binary logistic regression revealed that RNs who reported work-related disability (OR = 14.51; p -value: < .001), illness (OR = 3.32; p -value: < .001), experienced high physical demands (OR = 1.57; p -value: < .001) or burnout (OR = 1.39; p -value: < .001), were unsatisfied with their schedule (OR = 2.16; p -value: < .001), or staffing arrangements (OR = 1.41; p -value: < .001) were more likely to leave the profession. Whereas RNs who reported high levels of stress (OR = 0.59; p -value: < .001) were unsatisfied with the organization’s leadership (OR = 0.22; p -value: < .001), unsatisfied with their opportunity to advance their career (OR = 0.56; p -value: < .001), or were not adequately compensated (OR = 0.63; p -value: < .001), were more likely to leave the organization. Conclusions: Policy makers and health care managers should be aware of the different factors that are associated with RNs’ decision to leave the profession or an organization. Health care managers involved in the development of nurse retention strategies should address organizational leadership and consider development of comprehensive career development programs. Policy makers should consider allocating additional resources to ensure that RN workforce is of adequate size, is qualified, and is able to provide high quality care in the U.S..

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Analyzing U.S. nurse turnover: Are nurses leaving their jobs or the profession itself?

The Youden Index is a summary measurement of the receiver operating characteristic (ROC) curve for the accuracy of a diagnostic test with ordinal or continuous endpoints. The bootstrap confidence interval based on the adjusted proportion estimate was shown to have satisfactory performance among the existing confidence intervals, including the parametric interval via the delta method. In this article, we propose two confidence intervals using the square-and-add limits based on the Wilson score method. We compare the two proposed intervals with the existing interval with extensive simulation studies. The new interval based on the empirical proportion estimate generally has better performance than that based on the adjusted proportion estimate. A real example from a clinical trial of prostate cancer is illustrated for the application of the new intervals.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0127272&type=printable

Improved Confidence Intervals for the Youden Index.

Comparing two proportions through the difference is a basic problem in statistics and has applications in many fields. More than twenty confidence intervals (Newcombe, 1998a,b) have been proposed. Most of them are approximate intervals with an asymptotic infimum coverage probability much less than the nominal level. In addition, large sample may be costly in practice. So exact optimal confidence intervals become critical for drawing valid statistical inference with accuracy and precision. Recently, Wang (2010, 2012) derived the exact smallest (optimal) one-sided 1 - alpha confidence intervals for the difference of two paired or independent proportions. His intervals, however, are computer-intensive by nature. In this article, we provide an R package ExactCIdiff to implement the intervals when the sample size is not large. This would be the first available package in R to calculate the exact confidence intervals for the difference of proportions. Exact two-sided 1 - alpha interval can be easily obtained by taking the intersection of the lower and upper one-sided 1 - alpha/2 intervals. Readers may jump to Examples 1 and 2 to obtain these intervals.

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Guogen Shan

Papers

Fisher's exact approach for post hoc analysis of a chi-squared test

Optimal adaptive two-stage designs for early phase II clinical trials

Analyzing U.S. nurse turnover: Are nurses leaving their jobs or the profession itself?

Improved Confidence Intervals for the Youden Index.

ExactCIdiff: An R Package for Computing Exact Confidence Intervals for the Difference of Two Proportions