Predictive Analytics in Information Systems Research

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research

SAS: Symmetric Analysis of Z-Spectra, a Method to Evaluate B0 Correction Techniques for CEST Data in Clinical Systems Using Non-Exchanging Phantoms

Presentation of a method for the comparison of B0 correction methods. This method is based on non-exchanging phantoms to remove CEST effects. SAS method proposed to inform studies

Eliminating Recursion from Monadic Datalog Programs on Trees

We study the problem of eliminating recursion from monadic datalog programs on trees with an infinite set of labels. We show that the boundedness problem, i.e., determining whether a datalog program is equivalent to some nonrecursive one is undecidable but the decidability is regained if the descendant relation is disallowed. Under similar restrictions we obtain decidability of the problem of equivalence to a given nonrecursive program. We investigate the connection between these two problems in more detail

Seeking a second medical opinion: composition, reasons and perceived outcomes in Israel.

Background: Seeking a second-opinion (SO) is a common clinical practice that can optimize treatment and reduce unnecessary procedures and risks. We aim to characterize the composition of the population of SO seekers, their reasons for seeking a SO and choosing a specific physician, and their perceived outcomes following the SO. Methods: A cross-sectional national telephone survey, using a representative sample of the general Israeli population (n = 848, response rate = 62%). SO utilization was defined as seeking an additional clinical opinion from a specialist within the same specialty, for the same medical concern. We describe the characteristics of respondents who obtained SOs, their reasons for doing so and their perceived outcomes: (1) Satisfaction with the SO; (2) Experiencing health improvement after receiving a SO; (3) A difference in the diagnosis or treatment suggested in the first opinions and the second opinions; (4) Preference of the SO over the first one. Results: Most of the respondents who sought a SO (n = 344) were above 60 years old, secular, living with a partner, perceived their income to be above average and their health status to be not so good. For the patients who utilized SOs, orthopedic surgeons were sought out more than any other medical professional.Reasons for seeking a SO included doubts about diagnosis or treatment (38%), search for a sub-specialty expert (19%) and dissatisfaction with communication (19%). SO seekers most frequently chose a specific specialist based on a recommendation from a friend or a relative (33%). About half of the SO seekers also searched for information on the internet. Most of the respondents who sought a SO mentioned that they were satisfied with it (84%), felt health improvement (77%), mentioned that there was a difference between the diagnosis or treatment between the first opinion and the SO (56%) and preferred the SO over the first one (91%). Conclusions: Clinical uncertainty or dissatisfaction with patient-physician communication were the main reasons for seeking a SO. Policy makers should be aware that many patients choose a physician for a SO based on recommendations made outside the medical system. We recommend creating mechanisms that help patients in the complicated process of seeking a SO, suggest specialists who are suitable for the specific medical problem of the patient, and provide tools to reconcile discrepant opinions

The Future Impact of AI on Academic Journals and the Editorial Process

This panel explores the emerging impact of Artificial Intelligence (AI) on academic journals and editorial processes. Scholarly publishing is on the cusp of transformation, as generative and other AI technologies challenge established norms and procedures. Advancements could either revolutionize or gradually evolve the intricate ecosystems of academic journals, responsible for validating and disseminating knowledge. How might this happen? Panelists will consider how AI could reshape editorial boards, redefine reviewer roles, and influence the academic value chain. The panel will address concerns about AI-driven review processes, biases, training data, and the integration of Human-in-the-loop AI. The panel will also examine the role of academic journals as drivers of research and innovation, with AI\u27s potential to alter communication, quality assessment, and the building blocks of archival knowledge. Highly experienced editors will share insights, concerns and predictions, engage with audience perspectives, and illuminate the challenges and opportunities that AI brings to scholarly pursuits

Patient-initiated second medical consultations: patient characteristics and motivating factors, impact on care and satisfaction: A systematic review

Objectives: To review the characteristics and motivations of patients seeking second opinions, and the impact of such opinions on patient management, satisfaction, and cost-effectiveness. Data sources: Embase, Medline, PsycINFO and HMIC databases. Study design: A systematic literature search was performed for terms related to second opinion and patient characteristics. Study quality was assessed using the National Institutes of Health (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Data collection / Extraction methods: We included articles focused on patient-initiated second opinions, which provided quantitative data on their impact on diagnosis, treatment, prognosis or patient satisfaction, described the characteristics or motivating factors of patients who initiated a second opinion, or the cost-effectiveness of patient-initiated second opinions. Principal findings: Thirty-one articles were included in the review. 27 studies considered patient characteristics, 18 patient motivating factors, 10 patient satisfaction, and 17 clinical agreement between the first and second opinion. Seeking a second opinion was more common in women, middle age patients, more educated patients; and in people having a chronic condition, with higher income or socioeconomic status or living in central urban areas. Patients seeking a second opinion sought to gain more information or reassurance about their diagnosis or treatment. While many second opinions confirm the original diagnosis or treatment, discrepancies in opinions had a potential major impact on patient outcomes in up to 58% of cases. No studies reporting on the cost-effectiveness of patient initiated second opinions. Conclusions: Seeking a second opinion was more common in women, middle-age patients, and more educated patients, and in people having a chronic condition, with higher income or socioeconomic status or living in central urban areas. Patients seeking a second opinion sought to gain more information or reassurance about their diagnosis or treatment

High Spatial Resolution Fast-Neutron Imaging Detectors for Pulsed Fast-Neutron Transmission Spectroscopy

Two generations of a novel detector for high-resolution transmission imaging and spectrometry of fast-neutrons are presented. These devices are based on a hydrogenous fiber scintillator screen and single- or multiple-gated intensified camera systems (ICCD). This detector is designed for energy-selective neutron radiography with nanosecond-pulsed broad-energy (1 - 10 MeV) neutron beams. Utilizing the Time-of-Flight (TOF) method, such a detector is capable of simultaneously capturing several images, each at a different neutron energy (TOF). In addition, a gamma-ray image can also be simultaneously registered, allowing combined neutron/gamma inspection of objects. This permits combining the sensitivity of the fast-neutron resonance method to low-Z elements with that of gamma radiography to high-Z materials.Comment: Also published in JINST: http://www.iop.org/EJ/abstract/1748-0221/4/05/P0501

The expected payoff to Internet auctions

In an Internet auction, the expected payoff acts as a benchmark of the reasonableness of the price that is paid for the purchased item. Since the number of potential bidders is not observable, the expected payoff is difficult to estimate accurately. We approach this problem by considering the bids as a record and 2-record sequence of the potential bidde

Efficient Bayesian inference for COM-Poisson regression models

COM-Poisson regression is an increasingly popular model for count data. Its main advantage is that it permits to model separately the mean and the variance of the counts, thus allowing the same covariate to affect in different ways the average level and the variability of the response variable. A key limiting factor to the use of the COM-Poisson distribution is the calculation of the normalisation constant: its accurate evaluation can be time-consuming and is not always feasible. We circumvent this problem, in the context of estimating a Bayesian COM-Poisson regression, by resorting to the exchange algorithm, an MCMC method applicable to situations where the sampling model (likelihood) can only be computed up to a normalisation constant. The algorithm requires to draw from the sampling model, which in the case of the COM-Poisson distribution can be done efficiently using rejection sampling. We illustrate the method and the benefits of using a Bayesian COM-Poisson regression model, through a simulation and two real-world data sets with different levels of dispersion