Using EEG measures to quantify reduced daytime vigilance in patients diagnosed with obstructive sleep apnoea using a novel electroencephalogram analysis method

Introduction Vigilance in obstructive sleep apnoea (OSA) does not correlate well with disease severity/ symptoms: Hence the need for a simple objective test. One such method could be quantitative analysis of the awake electroencephalogram (qEEG). qEEG is conventionally analysed using Power Spectral Analysis (PSA) looking at different EEG frequencies of delta, theta, alpha and beta. A novel method of analyzing the qEEG: De-trended fluctuation analysis (DFA) provides a single value: the scaling exponent (SE), which measures the fluctuations in the EEG signal. Artefact removal from qEEG is mandatory with the gold standard being manual scoring. Another method of automated artefact removal is independent component analysis (ICA). Objective Investigate the role of PSA and DFA (SE) as an objective measure of testing vigilance and validate the use of ICA in patients diagnosed with OSA. Methodology Retrospective cross-sectional study of untreated OSA patients. Results ICA and manual artefact removal gave well-correlated results in the DFA (SE), but not PSA. EEG slowing measured by PSA and DFA did not correlate to impaired performance during a battery of 14 separate performance tests. Conclusion ICA and manual artefact removal can be interchangeably used in extracting DFA measurements with confidence. In PSA metrics the use of ICA may not be reliable

Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation

With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.Comment: To appear in UMAP 202

Development and internal validation of a machine learning prediction model for low back pain non-recovery in patients with an acute episode consulting a physiotherapist in primary care

BACKGROUND: While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and 'traditional' prediction modeling. METHODS: Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (≤ one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists' expectation) and 'traditional' logistic regression analysis. RESULTS: Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a'traditional' logistic regression model, it outperformed current practice. CONCLUSIONS: We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first

Recommending personalized touristic sights using Google Places

Contains fulltext : 116220.pdf (publisher's version ) (Open Access)The 36th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR

Term extraction for user profiling: evaluation by the user

Contains fulltext : 116259.pdf (author's version ) (Open Access)The 21st Conference on User Modeling, Adaptation and Personalization (UMAP

Using file system content to organize e-mail

Contains fulltext : 103871.pdf (publisher's version ) (Open Access)IIIX '12 Proceedings of the 4th Information Interaction in Context Symposiu

TNO and RUN at the TREC2012 Contextual Suggestion Track

Contains fulltext : 111264.pdf (author's version ) (Open Access)TREC 201

Combining textual and non-textual features for e-mail importance estimation

Contains fulltext : 122950.pdf (preprint version ) (Open Access)BNAIC 2013 : 25th Benelux Conference on Artificial Intelligence, Delft, 7-8 November 201