Computing Bounds on Resource Levels for Flexible Plans

A new algorithm efficiently computes the tightest exact bound on the levels of resources induced by a flexible activity plan (see figure). Tightness of bounds is extremely important for computations involved in planning because tight bounds can save potentially exponential amounts of search (through early backtracking and detection of solutions), relative to looser bounds. The bound computed by the new algorithm, denoted the resource-level envelope, constitutes the measure of maximum and minimum consumption of resources at any time for all fixed-time schedules in the flexible plan. At each time, the envelope guarantees that there are two fixed-time instantiations one that produces the minimum level and one that produces the maximum level. Therefore, the resource-level envelope is the tightest possible resource-level bound for a flexible plan because any tighter bound would exclude the contribution of at least one fixed-time schedule. If the resource- level envelope can be computed efficiently, one could substitute looser bounds that are currently used in the inner cores of constraint-posting scheduling algorithms, with the potential for great improvements in performance. What is needed to reduce the cost of computation is an algorithm, the measure of complexity of which is no greater than a low-degree polynomial in N (where N is the number of activities). The new algorithm satisfies this need. In this algorithm, the computation of resource-level envelopes is based on a novel combination of (1) the theory of shortest paths in the temporal-constraint network for the flexible plan and (2) the theory of maximum flows for a flow network derived from the temporal and resource constraints. The measure of asymptotic complexity of the algorithm is O(N O(maxflow(N)), where O(x) denotes an amount of computing time or a number of arithmetic operations proportional to a number of the order of x and O(maxflow(N)) is the measure of complexity (and thus of cost) of a maximumflow algorithm applied to an auxiliary flow network of 2N nodes. The algorithm is believed to be efficient in practice; experimental analysis shows the practical cost of maxflow to be as low as O(N1.5). The algorithm could be enhanced following at least two approaches. In the first approach, incremental subalgorithms for the computation of the envelope could be developed. By use of temporal scanning of the events in the temporal network, it may be possible to significantly reduce the size of the networks on which it is necessary to run the maximum-flow subalgorithm, thereby significantly reducing the time required for envelope calculation. In the second approach, the practical effectiveness of resource envelopes in the inner loops of search algorithms could be tested for multi-capacity resource scheduling. This testing would include inner-loop backtracking and termination tests and variable and value-ordering heuristics that exploit the properties of resource envelopes more directly

Inter-database validation of a deep learning approach for automatic sleep scoring

[Abstract] Study objectives Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Methods A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. Results Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. Conclusions Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time

Third European Conference on Organizational Knowledge, Learning and Capabilities Academic Track

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Socioeconomic impact of restless legs syndrome and inadequate restless legs syndrome management across European settings

Restless legs syndrome (RLS) is one of the most common neurological disorders. It describes an irresistible urge to move the legs, mostly manifested in the evening and at night, which can lead to severe sleep disturbance. As part of the European Brain Council (EBC)-led Value-of-Treatment project, this study aimed at capturing the socioeconomic impact of RLS related to the inadequate diagnosis and treatment across different European healthcare settings. The economic burden of RLS was estimated using the published EBC framework of analysis in three separate European Union healthcare systems (France, Germany, and Italy). The RLS care pathway was mapped to identify the unmet needs of patients. Based on specific patient stories, the economic impact of correctly diagnosing RLS and changing between inadequate and target treatment was calculated using appropriate scenario analysis. RLS proved to be a significant personal and social burden, when epidemiological data, high prevalence of RLS, and its need for treatment are combined. By looking at the savings emerging from the provision of optimal care management (timely and correct diagnosis, evidence-based therapy, avoidance of therapy-related complications such as augmentation), the authors foresee substantial economic savings with the achievement of adequate diagnosis and treatment of RLS. Education about RLS is urgently needed for all subspecialties involved in RLS patient care as well as the general public. Equally important, the search for new causal treatment strategies should be intensified to reduce suffering and substantial societal cost

Cerebral Blood Flow Measured by Phase-Contrast Magnetic Resonance Angiography in Preterm and Term Neonates

Background: Preterm infants show a decreased tortuosity in all proximal segments of the cerebral vasculature at term-equivalent age (TEA). Recently MRI techniques were developed to measure cerebral blood flow (CBF) based on phase-contrast images. Objectives: We hypothesized that arterial CBF corrected for brain size differs between full-term and preterm infants at TEA. Methods: 344 infants without major brain abnormalities had a cranial MRI for clinical reasons including phase-contrast magnetic resonance angiography (PC-MRA) around TEA (mean 41.1 ± SD 1.2 weeks). This cohort consisted of 172 preterm infants (gestational age at birth 24.1-31.9 weeks) and 172 term-born infants (gestational age at birth 37.0-42.6 weeks). The total CBF in milliliters/minute was calculated by adding the blood flow of the carotid and basilar arteries, and compared to age at scan, body weight, and several parameters of estimated brain size. Results: After logarithmic transformation, total CBF was associated with body weight, estimated brain weight, head circumference, and 2D brain surface measurements at TEA. Total CBF was significantly (9-12%) higher in term compared to preterm infants after correction for 2D brain surface measurements, head circumference or postmenstrual age at MRI (p < 0.05). Conclusions: Total CBF as measured by PC-MRA was associated with body and (estimated) brain weight and 2D brain surface measurements and was higher in term compared to preterm born infants

Periodic limb movements in sleep are associated with stroke and cardiovascular risk factors in patients with renal failure

Periodic limb movements in sleep (PLMS) is prevalent among dialysed patients and is associated with increased risk of mortality. Our study aimed to determine the prevalence of this disease in a sample of transplanted and waiting-list haemodialysed patients. One hundred transplanted and 50 waiting- list patients underwent polysomnography. Moderate and severe diseases were defined as periodic limb movements in sleep index (PLMSI) higher than 15 and 25 events h(-1) , respectively. The 10-year coronary heart disease risk was estimated for all patients using the Framingham Score. Moreover, the 10-year estimated risk of stroke was calculated according to the modified version of the Framingham Stroke Risk Profile. PLMS was present in 27% of the transplanted and 42% of the waiting- list group (P = 0.094); the proportion of severe disease was twice as high in waiting-list versus transplanted patients (32 versus 16%, P = 0.024). Patients with severe disease had a higher 10-year estimated risk of stroke in the transplanted group [10 (7-17) versus 5 (4-10); P = 0.002] and a higher 10- year coronary heart disease risk in both the transplanted [18 (8-22) versus 7 (4-14); P = 0.002], and the waiting-list groups [11 (5-18) versus 4 (1-9); P = 0.032]. In multivariable linear regression models the PLMSI was associated independently with the Framingham cardiovascular and cerebrovascular scores after adjusting for important covariables. Higher PLMSI is an independent predictor of higher cardiovascular and cerebrovascular risk score in patients with chronic kidney disease. Severe PLMS is less frequent in kidney transplant recipients compared to waiting-list dialysis patients