Approaches to decision-making among late-stage melanoma patients: a multifactorial investigation.

PurposeThe treatment decisions of melanoma patients are poorly understood. Most research on cancer patient decision-making focuses on limited components of specific treatment decisions. This study aimed to holistically characterize late-stage melanoma patients' approaches to treatment decision-making in order to advance understanding of patient influences and supports.Methods(1) Exploratory analysis of longitudinal qualitative data to identify themes that characterize patient decision-making. (2) Pattern analysis of decision-making themes using an innovative method for visualizing qualitative data: a hierarchically-clustered heatmap. Participants were 13 advanced melanoma patients at a large academic medical center.ResultsExploratory analysis revealed eight themes. Heatmap analysis indicated two broad types of patient decision-makers. "Reliant outsiders" relied on providers for medical information, demonstrated low involvement in decision-making, showed a low or later-in-care interest in clinical trials, and expressed altruistic motives. "Active insiders" accessed substantial medical information and expertise in their networks, consulted with other doctors, showed early and substantial interest in trials, demonstrated high involvement in decision-making, and employed multiple decision-making strategies.ConclusionWe identified and characterized two distinct approaches to decision-making among patients with late-stage melanoma. These differences spanned a wide range of factors (e.g., behaviors, resources, motivations). Enhanced understanding of patients as decision-makers and the factors that shape their decision-making may help providers to better support patient understanding, improve patient-provider communication, and support shared decision-making

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Neural Attentive Session-based Recommendation

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939, arXiv:1606.08117 by other author

Beyond Exploratory: A Tailored Framework for Assessing Rigor in Qualitative Health Services Research

Objective: To propose a framework for assessing the rigor of qualitative research that identifies and distinguishes between the diverse objectives of qualitative studies currently used in patient-centered outcomes and health services research (PCOR and HSR). Study Design: Narrative review of published literature discussing qualitative guidelines and standards in peer-reviewed journals and national funding organizations that support PCOR and HSR. Principal Findings: We identify and distinguish three objectives of current qualitative studies in PCOR and HSR: exploratory, descriptive, and comparative. For each objective, we propose methodological standards that can be used to assess and improve rigor across all study phases—from design to reporting. Similar to quantitative studies, we argue that standards for qualitative rigor differ, appropriately, for studies with different objectives and should be evaluated as such. Conclusions: Distinguishing between different objectives of qualitative HSR improves the ability to appreciate variation in qualitative studies as well as appropriately evaluate the rigor and success of studies in meeting their own objectives. Researchers, funders, and journal editors should consider how adopting the criteria for assessing qualitative rigor outlined here may advance the rigor and potential impact of qualitative research in patient-centered outcomes and health services research

FliPer_Class:in search of solar-like pulsators among TESS targets

The NASA's Transiting Exoplanet Survey Satellite (TESS) is about to provide full-frame images of almost the entire sky. The amount of stellar data to be analysed represents hundreds of millions stars, which is several orders of magnitude above the amount of stars observed by CoRoT, Kepler, or K2 missions. We aim at automatically classifying the newly observed stars, with near real-time algorithms, to better guide their subsequent detailed studies. In this paper, we present a classification algorithm built to recognise solar-like pulsators among classical pulsators, which relies on the global amount of power contained in the PSD, also known as the FliPer (Flicker in spectral Power density). As each type of pulsating star has a characteristic background or pulsation pattern, the shape of the PSD at different frequencies can be used to characterise the type of pulsating star. The FliPer Classifier (FliPer$_{Class}$) uses different FliPer parameters along with the effective temperature as input parameters to feed a machine learning algorithm in order to automatically classify the pulsating stars observed by TESS. Using noisy TESS simulated data from the TESS Asteroseismic Science Consortium (TASC), we manage to classify pulsators with a 98% accuracy. Among them, solar-like pulsating stars are recognised with a 99% accuracy, which is of great interest for further seismic analysis of these stars like our Sun. Similar results are obtained when training our classifier and applying it to 27 days subsets of real Kepler data. FliPer$_{Class}$ is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (T'DA) classification working group.Comment: 8 pages, 6 figures, accepted to A&

Chronologically dating the early assembly of the Milky Way

The standard cosmological model predicts that galaxies are built through hierarchical assembly on cosmological timescales1,2. The Milky Way, like other disk galaxies, underwent violent mergers and accretion of small satellite galaxies in its early history. Owing to Gaia Data Release 23 and spectroscopic surveys4, the stellar remnants of such mergers have been identified5–7. The chronological dating of such events is crucial to uncover the formation and evolution of the Galaxy at high redshift, but it has so far been challenging due to difficulties in obtaining precise ages for these oldest stars. Here we combine asteroseismology—the study of stellar oscillations—with kinematics and chemical abundances to estimate precise stellar ages (~11%) for a sample of stars observed by the Kepler space mission8. Crucially, this sample includes not only some of the oldest stars that were formed inside the Galaxy but also stars formed externally and subsequently accreted onto the Milky Way. Leveraging this resolution in age, we provide compelling evidence in favour of models in which the Galaxy had already formed a substantial population of its stars (which now reside mainly in its thick disk) before the infall of the satellite galaxy Gaia-Enceladus/Sausage5,6 around 10 billion years ago

Population-Based Precision Cancer Screening: A Symposium on Evidence, Epidemiology, and Next Steps

Precision medicine, an emerging approach for disease treatment that takes into account individual variability in genes, environment, and lifestyle, is under consideration for preventive interventions, including cancer screening. On September 29, 2015, the National Cancer Institute sponsored a symposium entitled “Precision Cancer Screening in the General Population: Evidence, Epidemiology, and Next Steps”. The goal was two-fold: to share current information on the evidence, practices, and challenges surrounding precision screening for breast, cervical, colorectal, lung, and prostate cancers, and to allow for in-depth discussion among experts in relevant fields regarding how epidemiology and other population sciences can be used to generate evidence to inform precision screening strategies. Attendees concluded that the strength of evidence for efficacy and effectiveness of precision strategies varies by cancer site, that no one research strategy or methodology would be able or appropriate to address the many knowledge gaps in precision screening, and that issues surrounding implementation must be researched as well. Additional discussion needs to occur to identify the high priority research areas in precision cancer screening for pertinent organs and to gather the necessary evidence to determine whether further implementation of precision cancer screening strategies in the general population would be feasible and beneficial

A retrospective study of macropod progressive periodontal disease ("lumpy jaw") in captive macropods across Australia and Europe: using data from the past to inform future macropod management

Macropod Progressive Periodontal Disease (MPPD) is a well-recognised disease that causes high morbidity and mortality in captive macropods worldwide. Epidemiological data on MMPD are limited, although multiple risk factors associated with a captive environment appear to contribute to the development of clinical disease. The identification of risk factors associated with MPPD would assist with the development of preventive management strategies, potentially reducing mortality. Veterinary and husbandry records from eight institutions across Australia and Europe were analysed in a retrospective cohort study (1995 to 2016), examining risk factors for the development of MPPD. A review of records for 2759 macropods found incidence rates (IR) and risk of infection differed between geographic regions and individual institutions. The risk of developing MPPD increased with age, particularly for macropods >10 years (Australia Incidence Rate Ratio (IRR) 7.63, p < 0.001; Europe IRR 7.38, p < 0.001). Prognosis was typically poor, with 62.5% mortality reported for Australian and European regions combined. Practical recommendations to reduce disease risk have been developed, which will assist zoos in providing optimal long-term health management for captive macropods and, subsequently, have a positive impact on both the welfare and conservation of macropods housed in zoos globally