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The prediction of posttraumatic stress disorder (PTSD) has gained a lot of interest in clinical studies. Identifying patients with a high risk of PTSD can guide mental healthcare workers when making treatment decisions. The main goal of this paper is to propose several Bayesian network (BN) models to assess the probability that a veteran has PTSD when first visiting a U.S. Department of Veteran Affairs (VA) facility seeking medical care. The current practice is to use a five-question test called PC-PTSD-5. We aim to use the PC-PTSD-5 test, which is currently administered to most incoming new patients, and demographic information, military service history, and medical history. We construct a Bayes information criterion score-based BN, a group L2-regularized BN (GL2-regularized BN), and a naïve Bayes BN to assess the probability that a patient has PTSD. The GL2-regularized BN is a new method for constructing a BN motivated by some of the challenges of analyzing this data set. A secondary goal is to identify which features are important in predicting PTSD. We discover that the following features help compute the probability of PTSD: PC-PTSD-5, service-connected flag, combat flag, agent orange flag, military sexual trauma flag, traumatic brain injury, and age. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0174) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0174). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A study was conducted to examine the relation between daily order imbalance and return in the stock markets of Shanghai and Shenzhen in China. Previous studies have indicated that daily order imbalance predicts subsequent returns. Findings show that a contemporaneous relation exists between daily order imbalances and returns. There was no evidence found regarding the prediction of subsequent returns by order imbalances.

This paper proposes a linear belief function (LBF) approach to evaluate portfolio performance. By drawing on the notion of LBFs, an elementary approach to knowledge representation in expert systems is proposed. It is shown how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset-pricing models. The authors then appeal to Dempster's rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance and updating the belief by incorporating additional evidence. An example of three gold stocks is used to illustrate the approach. Index Terms--Dempster-Shafer belief functions, knowledge-based systems, linear belief functions, multivariate normal distribution, portfolio evaluation.

Objective: This study is an interventional evaluation of a post-offer employment testing. The study is designed to determine if shoulder injury rates are lowered when employees are placed at jobs they demonstrate the physical ability to perform. Methods: A physical capacity evaluation based testing protocol was utilized to determine if each new employee had the physical work capacity to perform the job for which they were hired. Injuries to the shoulder were specifically scrutinized. The interventional group was compared to a historical control. Results: The incidence of shoulder injuries was 0% in the tested group and 3.8% in the untested historical control. Over a 6 year study timeframe the utilization of physical capacity testing for work placement appeared to be the major factor in decreasing work related shoulder injuries. The annual cost of administering the tests for three years was $9,543, while the net annual cost savings was $124,451. This represented a 37% decrease in medical costs for shoulder and other work-related injuries. For every dollar spent on testing there was a $14 savings in medical costs secondary to injury prevention. Conclusion: The use of post-offer physical capacity testing resulted in a substantial and noticeable decrease in shoulder related non-accidental injuries. Furthermore, it is evident that a properly conceived and implemented post-offer testing program may help in the reduction of work-related injuries. [ABSTRACT FROM AUTHOR]

This paper proposes a linear belief function (LBF) approach to evaluate portfolio performance. By drawing on the notion of LBFs, an elementary approach to knowledge representation in expert systems is proposed. It is shown how to use basic matrices to represent market information and financial knowledge, including complete ignorance, statistical observations, subjective speculations, distributional assumptions, linear relations, and empirical asset-pricing models. The authors then appeal to Dempster's rule of combination to integrate the knowledge for assessing the overall belief of portfolio performance and updating the belief by incorporating additional evidence. An example of three gold stocks is used to illustrate the approach. [ABSTRACT FROM AUTHOR]