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Beacon in the Dark
(2018)
The large amount of heterogeneous data in these email corpora renders experts' investigations by hand infeasible. Auditors or journalists, e.g., who are looking for irregular or inappropriate content or suspicious patterns, are in desperate need for computer-aided exploration tools to support their investigations.
We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations based on rearranged and aggregated extracted information to reveal salient patterns.
Introduction: Chronic low back pain (LBP) is a major cause of disability; early diagnosis and stratification of care remain challenges.
Objectives: This article describes the development of a screening tool for the 1-year prognosis of patients with high chronic LBP risk (risk stratification index) and for treatment allocation according to treatment-modifiable yellow flag indicators (risk prevention indices, RPI-S).
Methods: Screening tools were derived from a multicentre longitudinal study (n = 1071, age >18, intermittent LBP). The greatest prognostic predictors of 4 flag domains ("pain," "distress," "social-environment," "medical care-environment") were determined using least absolute shrinkage and selection operator regression analysis. Internal validity and prognosis error were evaluated after 1-year follow-up. Receiver operating characteristic curves for discrimination (area under the curve) and cutoff values were determined.
Results: The risk stratification index identified persons with increased risk of chronic LBP and accurately estimated expected pain intensity and disability on the Pain Grade Questionnaire (0-100 points) up to 1 year later with an average prognosis error of 15 points. In addition, 3-risk classes were discerned with an accuracy of area under the curve = 0.74 (95% confidence interval 0.63-0.85). The RPI-S also distinguished persons with potentially modifiable prognostic indicators from 4 flag domains and stratified allocation to biopsychosocial treatments accordingly.
Conclusion: The screening tools, developed in compliance with the PROGRESS and TRIPOD statements, revealed good validation and prognostic strength. These tools improve on existing screening tools because of their utility for secondary preventions, incorporation of exercise effect modifiers, exact pain estimations, and personalized allocation to multimodal treatments.
Introduction: Chronic low back pain (LBP) is a major cause of disability; early diagnosis and stratification of care remain challenges.
Objectives: This article describes the development of a screening tool for the 1-year prognosis of patients with high chronic LBP risk (risk stratification index) and for treatment allocation according to treatment-modifiable yellow flag indicators (risk prevention indices, RPI-S).
Methods: Screening tools were derived from a multicentre longitudinal study (n = 1071, age >18, intermittent LBP). The greatest prognostic predictors of 4 flag domains ("pain," "distress," "social-environment," "medical care-environment") were determined using least absolute shrinkage and selection operator regression analysis. Internal validity and prognosis error were evaluated after 1-year follow-up. Receiver operating characteristic curves for discrimination (area under the curve) and cutoff values were determined.
Results: The risk stratification index identified persons with increased risk of chronic LBP and accurately estimated expected pain intensity and disability on the Pain Grade Questionnaire (0-100 points) up to 1 year later with an average prognosis error of 15 points. In addition, 3-risk classes were discerned with an accuracy of area under the curve = 0.74 (95% confidence interval 0.63-0.85). The RPI-S also distinguished persons with potentially modifiable prognostic indicators from 4 flag domains and stratified allocation to biopsychosocial treatments accordingly.
Conclusion: The screening tools, developed in compliance with the PROGRESS and TRIPOD statements, revealed good validation and prognostic strength. These tools improve on existing screening tools because of their utility for secondary preventions, incorporation of exercise effect modifiers, exact pain estimations, and personalized allocation to multimodal treatments.