Towards the Application of Machine Learning in Emergency Informatics.

Abstract

Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these requirements through automated emergency technology. Machine learning (ML) is one of the main parts of some of these proposed technologies. There are various ML algorithms and techniques which are potentially applicable for different purposes of emergency care. AI-based approaches using classification and clustering algorithms, natural language processing, and text mining are some of the possible techniques that could prove useful for investigating models of emergency prevention and management and proposing improved procedures for handling such critical situations. ML is known as a field of AI which attempts to automatically learn from data and applies that learning to make better decisions. Decision-support tools can apply the results of either supervised or various semi-supervised or unsupervised learning methods to tackle the how decisions about emergency situations are typically handled by the best professionals at the scene of an emergency, in the pre-hospital, and in healthcare facility settings. Enhanced and rapid communication at the moment of emergency, with the most effective decision making for triaging to estimate the acute nature of injuries and possible complications, how to keep a patient stable on the way to the care facility, and also avoiding adverse drug reactions, are some of the possible directions for exploring how ML can help to gather the data and to make emergency management more efficient and effective. The wide range of scenarios present in emergency situations and the complexity of different legal and ethical constraints on what responding personnel are allowed to perform on an injured subject before reaching a hospital makes for a most challenging set of problems for investigating the components of "intelligent" decision support that could help in these highly interactive and humanly tragic situations.