Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

OBJECTIVE: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them. MATERIALS AND METHODS: Using the MIMIC-III (Medical Information Mart for Intensive Care-III) dataset, we train deep neural network models to predict the onset of 6 endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single-task (ST) models with naive multitask and SeqSNR in terms of discriminative performance and label efficiency. RESULTS: SeqSNR showed a modest yet statistically significant performance boost across 4 of 6 tasks compared with ST and naive multitasking. When the size of the training dataset was reduced for a given task (label efficiency), SeqSNR outperformed ST for all cases showing an average area under the precision-recall curve boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels, respectively. CONCLUSIONS: The SeqSNR architecture shows superior label efficiency compared with ST and naive multitasking, suggesting utility in scenarios in which endpoint labels are difficult to ascertain

Mobilising Knowledge through Global Partnerships to Support Research-informed Teaching: Five Models for Translational Research

Education Futures Collaboration Charity The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the quality of teaching is of global concern: UNESCO’s Sustainable Development Goal (SDG) 4c in the Education 2030: Framework for Action calls for high quality teaching for all. The OECD challenges the education system to improve Knowledge Management. JET’s (2015) special issue: Translational Research (TR) and Knowledge Mobilisation in Teacher Education introduced the concept of ‘translational’ or ‘theory to practice’ research - well-established in medicine but not in education. Five TR models were subsequently developed by the MESH charity’s international network with organisations in South Africa, Bangladesh, Australia, Pakistan, UK. These distinct models engage 1) university staff and teachers 2) subject associations, 3) research units, 4) an international NGO working in crisis settings, 5) PhD tutors and students. Each model shares common features forming the MESH Translational Research methodology introduced in this article. A TR repository is part of the MESH knowledge mobilisation strategy giving teachers access to research summaries which, overtime, accumulate knowledge. TR publications called MESHGuides (www.meshguides.org) complement existing forms of publication. This article proposes the MESH TR methodology as one affordable and scalable solution to OECD and UNESCO’s challenges of keeping teachers up-to-date and making new knowledge accessible to teachers regardless of location

Overview of cattle diseases listed under category C, D or E in the animal health law for wich control programmes are in place within Europe

13 páginas, 5 figuras, 3 tablas.The COST action “Standardising output-based surveillance to control non-regulated diseases of cattle in the European Union (SOUND control),” aims to harmonise the results of surveillance and control programmes (CPs) for non-EU regulated cattle diseases to facilitate safe trade and improve overall control of cattle infectious diseases. In this paper we aimed to provide an overview on the diversity of control for these diseases in Europe. A non-EU regulated cattle disease was defined as an infectious disease of cattle with no or limited control at EU level, which is not included in the European Union Animal health law Categories A or B under Commission Implementing Regulation (EU) 2020/2002. A CP was defined as surveillance and/or intervention strategies designed to lower the incidence, prevalence, mortality or prove freedom from a specific disease in a region or country. Passive surveillance, and active surveillance of breeding bulls under Council Directive 88/407/EEC were not considered as CPs. A questionnaire was designed to obtain country-specific information about CPs for each disease. Animal health experts from 33 European countries completed the questionnaire. Overall, there are 23 diseases for which a CP exists in one or more of the countries studied. The diseases for which CPs exist in the highest number of countries are enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis, bovine viral diarrhoea and anthrax (CPs reported by between 16 and 31 countries). Every participating country has on average, 6 CPs (min–max: 1–13) in place. Most programmes are implemented at a national level (86%) and are applied to both dairy and non-dairy cattle (75%). Approximately one-third of the CPs are voluntary, and the funding structure is divided between government and private resources. Countries that have eradicated diseases like enzootic bovine leukosis, bluetongue, infectious bovine rhinotracheitis and bovine viral diarrhoea have implemented CPs for other diseases to further improve the health status of cattle in their country. The control of non-EU regulated cattle diseases is very heterogenous in Europe. Therefore, the standardising of the outputs of these programmes to enable comparison represents a challenge.Peer reviewe