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Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules.

ABSTRACT: As part of its role in memory consolidation, sleep has been repeatedly identified as critical for the extraction of regularities from wake experiences. However, many null results have been published as well, with no clear consensus emerging regarding the conditions that yield this sleep effect. Here, we systematically review the role of sleep in the extraction of hidden regularities, specifically those involving associative relations embedded in newly learned information. We found that the specific behavioral task used in a study had far more impact on whether a sleep effect was discovered than either the category of the cognitive processes targeted, or the particular experimental design employed. One emerging pattern, however, was that the explicit detection of hidden rules is more likely to happen when the rules are of a temporal nature (i.e., event A at time t predicts a later event B) than when they are non-temporal. We discuss this temporal rule sensitivity in reference to the compressed memory replay occurring in the hippocampus during slow-wave-sleep, and compare this effect to what happens when the extraction of regularities depends on prior knowledge and relies on structures other than the hippocampus.

SUBMITTER: Lerner I

PROVIDER: S-EPMC6779511 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules.

Lerner Itamar I Gluck Mark A MA

Sleep medicine reviews 20190605

As part of its role in memory consolidation, sleep has been repeatedly identified as critical for the extraction of regularities from wake experiences. However, many null results have been published as well, with no clear consensus emerging regarding the conditions that yield this sleep effect. Here, we systematically review the role of sleep in the extraction of hidden regularities, specifically those involving associative relations embedded in newly learned information. We found that the speci ...[more]

PMID: 31252335

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Project description:Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. Results: In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.

Project description:OBJECTIVE:To identify and assess the performance of clinical decision rules (CDR) for chest pain in general practice. DESIGN:Systematic review of diagnostic studies. DATA SOURCES:Medline/Pubmed, Embase/Ovid, CINAHL/EBSCO and Google Scholar up to October 2018. STUDY SELECTION:Studies that assessed CDRs for intermittent-type chest pain and for rule out of acute coronary syndrome (ACS) applicable in general practice, thus not relying on advanced laboratory, computer or diagnostic testing. REVIEW METHODS:Reviewers identified studies, extracted data and assessed the quality of the evidence (using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2)), independently and in duplicate. RESULTS:Eight studies comprising five CDRs met the inclusion criteria. Three CDRs are designed for rule out of coronary disease in intermittent-type chest pain (Gencer rule, Marburg Heart Score, INTERCHEST), and two for rule out of ACS (Grijseels rule, Bruins Slot rule). Studies that examined the Marburg Heart Score had the highest methodological quality with consistent sensitivity (86%-91%), specificity (61%-81%) and positive (23%-35%) and negative (97%-98%) predictive values (PPV and NPV). The diagnostic performance of Gencer (PPV: 20%-34%, NPV: 95%-99%) and INTERCHEST (PPV: 35%-43%, NPV: 96%-98%) appear comparable, but requires further validation. The Marburg Heart Score was more sensitive in detecting coronary disease than the clinical judgement of the general practitioner. The performance of CDRs that focused on rule out of ACS were: Grijseels rule (sensitivity: 91%, specificity: 37%, PPV: 57%, NPV: 82%) and Bruins Slot (sensitivity: 97%, specificity: 10%, PPV: 23%, NPV: 92%). Compared with clinical judgement, the Bruins Slot rule appeared to be safer than clinical judgement alone, but the study was limited in sample size. CONCLUSIONS:In general practice, there is currently no clinical decision aid that can safely rule out ACS. For intermittent chest pain, several rules exist, of which the Marburg Heart Score has been most extensively tested and appears to outperform clinical judgement alone.

Dataset Information

Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules.

Publications

Sleep and the extraction of hidden regularities: A systematic review and the importance of temporal rules.

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