https://findresearcher.sdu.dk:8443/ws/files/157406200/1_s2.0_S0300957218309754_main.pdf

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

The aim of these guidelines is to provide evidence‑based guidance for temperature control in adults who are comatose after resuscitation from either in-hospital or out-of-hospital cardiac arrest, regardless of the underlying cardiac rhythm. These guidelines replace the recommendations on temperature management after cardiac arrest included in the 2021 post-resuscitation care guidelines co-issued by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). The guideline panel included thirteen international clinical experts who authored the 2021 ERC-ESICM guidelines and two methodologists who participated in the evidence review completed on behalf of the International Liaison Committee on Resuscitation (ILCOR) of whom ERC is a member society. We followed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and grade recommendations. The panel provided suggestions on guideline implementation and identified priorities for future research. The certainty of evidence ranged from moderate to low. In patients who remain comatose after cardiac arrest, we recommend continuous monitoring of core temperature and actively preventing fever (defined as a temperature > 37.7 °C) for at least 72 h. There was insufficient evidence to recommend for or against temperature control at 32-36 °C or early cooling after cardiac arrest. We recommend not actively rewarming comatose patients with mild hypothermia after return of spontaneous circulation (ROSC) to achieve normothermia. We recommend not using prehospital cooling with rapid infusion of large volumes of cold intravenous fluids immediately after ROSC. 

/pdf/erc-esicm-guidelines-on-temperature-control-after-cardiac-1ys8ye7u.pdf

ERC-ESICM guidelines on temperature control after cardiac arrest in adults

Out of hospital cardiac arrest: Past, present, and future.

Machine learning can support dispatchers to better and faster recognize out-of-hospital cardiac arrest during emergency calls: A retrospective study.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI (AI HLEG 2019) set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners towards more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.

/pdf/on-assessing-trustworthy-ai-in-healthcare-machine-learning-3djv5ztvcm.pdf

On Assessing Trustworthy AI in Healthcare: Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls

pdf/diagnosis-of-out-of-hospital-cardiac-arrest-by-emergency-si6lr6ppil.pdf

Diagnosis of out-of-hospital cardiac arrest by emergency medical dispatch: A diagnostic systematic review

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Diagnosis of out-of-hospital cardiac arrest by emergency medical dispatch: A diagnostic systematic review