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21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 ; 2021-December:925-934, 2021.
Article in English | Scopus | ID: covidwho-1730939


Finding significant events, which follow a specific pattern, is an essential task in sequential rule mining. While the significance of a rule often is based on conditions like a maximum amount of time [1], or a minimum distance between patterns [2], the area between these two extremes is rarely analyzed. This paper aims at the discovery of partially-ordered sequential rules which satisfy a given correlation gap constraint. Applying this constraint to the support threshold determines a more relevant rule, among other parameters. We also require it in sparse datasets, where long sequences with many distinct events exist. This setting can be found in online product configurators, where the basis is an unstructured process that combines both high-level and fine-grained configuration steps. In general, our novel approach SCORER-Gap can be applied to procedures with a high variability of events.By focusing on the gap size between antecedent and consequent of a rule, we show that usually, the resulting vast number of rules gets highly reduced while keeping the flexibility between a minimum and a maximum distance in between. To implement our novel approach, we use an in-mining setup, namely RuleGrowth [1] to which we attach the correlation gap constraint as mentioned above. The code is available on [3]. For an extensive analysis of application areas, we use three real-world datasets consisting of different characteristics. We start with a Covid19 genome sequence representing a highly dense dataset. Additionally, an industrial database and the clickstream of a Hungarian news website (Kosarak) are used as representatives for increasingly sparse datasets.SCORER-Gap shows a high percentual reduction in the number of rules in the resulting ruleset while slightly increasing accuracy in a train and test setting. Furthermore, a high proportion of recommendation rules differs between RuleGrowth and SCORER-Gap. © 2021 IEEE.

CIGRE Science and Engineering ; 19(October):5-19, 2020.
Article in English | Scopus | ID: covidwho-1445126
Public Health Action ; 11(3): 112-113, 2021 Sep 21.
Article in English | MEDLINE | ID: covidwho-1441347


Baylor Clinic in Mbabane, Eswatini, convened a crisis meeting to tackle critical shortages of long-sleeved disposable gowns that resulted from COVID-19 pandemic constraints on available personal protective equipment (PPE). A strategy deemed safe, affordable and sustainable was adopted to autoclave and re-use gowns based on a risk-stratified approach. Key objectives were to ensure essential infection control and prevention (ICP) for medical doctors, nurses, and laboratory teams. Administrative, environmental and personal protective measures for ICP were enhanced through regular staff training. This strategy for gown re-use has been invaluable in motivating responsible stewardship and maximization of available gowns during the COVID-19 pandemic.

La Baylor Clinic de Mbabane, Eswatini, a convoqué une réunion de crise pour remédier à la grave pénurie de blouses jetables à manches longues due au manque d'équipements de protection individuelle (PPE) lié à la pandémie de COVID-19. Une stratégie jugée sûre, abordable et durable a été adoptée pour stériliser par autoclave et réutiliser les blouses en prenant appui sur une approche stratifiée des risques. Les objectifs clés étaient de garantir la prévention et le contrôle des infections (ICP) pour les médecins, les infirmiers et les équipes de laboratoire. Les mesures ICP d'ordre administratif, environnemental et de protection individuelle ont été renforcées par le biais de formations régulières du personnel. Cette stratégie de réutilisation des blouses a permis de promouvoir une gestion responsable et de tirer au maximum profit des blouses disponibles pendant la pandémie de COVID-19.

Journal of Pharmaceutical Research International ; 32(47):49-61, 2020.
Article in English | Web of Science | ID: covidwho-1168143


Background: An unprecedented global effort in identifying potentially viable and emerging drugs for effective treatment of the novel coronavirus disease (2019) is being made. Of the most promising candidate therapies, convalescent plasma (CP), albeit controversial, is approved for emergency use authorization (EUA) by the U.S. Food and Drug Administration (FDA). The concept rests on passive immunity, achieved by administering plasma with high titers of neutralizing antibodies to reduce severity of SARS-CoV-2 infection and mortality. The aim of this paper is to assess the clinical improvement, patients' discharge status and all-cause mortality in convalescent plasma versus standard of care COVID-19 patient groups. Methods: Using PRISMA guidelines, a review was conducted from January, 2020, until October, 2020 employing keywords including "convalescent plasma", "clinical improvement, "mortality", "adverse events", "viral load", "dosing", and survival." Dichotomous data for all-cause mortality, patients' discharge status, and clinical improvement at day 14 of treatment were meta-analyzed applying the Mantel-Haenszel (M-H) random effects model using Review Manager 5.4. Results: A total of 627 (23.9%) patients in the CP group and 1997 (76.1%) patients in the control group were pooled. The studies were conducted in the United States, China, Netherlands, and Iran. The CP group had a lower association to all-cause mortality as compared to the control group [OR: 0.69;CI: 0.50 to 0.96;P=0.03]. Patients who received CP had higher probability of discharge during the study course [OR: 1.87;CI: 1.1 to 3.18;P=0.02]. Bias was expected in the analysis due to the stratified of study designs included. Conclusion: Convalescent plasma therapy may be an effective and vital tool with promising historical, current, and expected clinical trial evidence of metrics such as increased safety and reduction of all-cause mortality.