| 1. |
- Coste, Antoine, et al.
(författare)
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Finite Size Effects and Twisted Boundary Conditions
- 1987
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Ingår i: Nuclear Physics B. - : Elsevier BV. - 0550-3213. ; 287, s. 569-588
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Tidskriftsartikel (refereegranskat)abstract
- We explore the possibility of reducing finite size effects in the weak coupling region of glueball correlations and Wilson loops. Our analysis indicates that twisted boundary conditions do diminish finite size effects and numerical evidence for this is given for the glueball correlation.
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| 2. |
- Forsberg, David, et al.
(författare)
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AIM in Neonatal and Pediatric Intensive Care
- 2022. - 1st
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Ingår i: Artificial Intelligence in Medicine. - Cham : Springer Nature. ; , s. 1047-1056
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Infections are one of the leading causes of death in infants and detecting life-threatening events in infants is challenging. Thus, providing effective life-saving interventions in time is essential. As infants’ immune and autonomic control system are under development, signs preceding potentially life-threatening events are subtle. Clinical detection is aided by analysis of biomarkers, which unfortunately requires invasive sampling and is time consuming. Infection and inflammation interfere with the autonomic control systems and consequently affect vital signs. Constantly monitoring vital signs at a high frequency enables the immediate detection of discrepancies and is thus a key, noninvasive instrument in modern intensive care units. For pediatric intensive care, several predictive monitoring systems have been developed over the last decade that aim to utilize vital sign monitoring to mitigate the risk of developing lifethreatening events, such as sepsis. Recent advances in the field of machine learning have provided novel techniques for big data analysis. This enables an individualized risk assessment via continuous multimodal inputs and development of better clinical decision support systems. These more advanced systems are able to detect sepsis 24 hours earlier than clinical practice and enable an overall risk assessment for future sepsis, life-threatening events, and death at the time of hospitalization or during the first week of life. This chapter summarizes the current evidence on machine learning-based monitoring systems and provides an overview on the strengths, limitations, and potential future roles of novel machine learning-based methods for the early detection of pedatric sepsis and potentially life-threatening events.
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| 3. |
- Honore, Antoine, et al.
(författare)
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Hidden markov models for sepsis detection in preterm infants
- 2020
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Ingår i: Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1130-1134
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Konferensbidrag (refereegranskat)abstract
- We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model basedHMM, and a recently proposed neural network based HMM. To improve the neural network based HMM, we propose a discriminative training approach. Experimental results show the potential of HMMs over logistic regression, support vector machine and extreme learning machine.
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| 4. |
- Persad, E., et al.
(författare)
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Neonatal sepsis prediction through clinical decision support algorithms : A systematic review
- 2021
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Ingår i: Acta Paediatrica. - : Wiley. - 0803-5253 .- 1651-2227. ; 110:12, s. 3201-3226
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates. Methods: A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE. PROSPERO ID: CRD42020205143. Results: After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results. Conclusion: Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.
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