Evidence-based prediction models, together with artificial intelligence have the potential to revolutionize the diagnosis and treatment of diseases or conditions of emergency medicine, including but not limited to trauma, stomach or abdominal pain, cardio-cerebrovascular emergent conditions, and infections that require urgent medical care or treatment. The prediction models developed for these conditions leverage extensive datasets and sophisticated statistical algorithms to predict the likelihood of adverse events and clinical outcomes for these emergency conditions, thereby informing clinical decision-making. The development and implementation of such tools could significantly enhance patient outcomes, reduce healthcare expenditures, and optimize the efficiency and efficacy of healthcare delivery.
Utilizing evidence-based methods such as systematic literature review, Delphi expert consultation, and consensus meetings to screen variables for prediction models or artificial intelligence is of paramount importance as it ensures that the model or algorithm is constructed using the most pertinent and informative variables. This practice ultimately enhances the model's clinical utility and increases its accuracy and reliability. Furthermore, involving domain experts in the screening process ensures that the resulting model or algorithm is both clinically relevant and applicable to real-world scenarios.
In emergency medicine, clinical prediction models and artificial intelligence have made significant strides. These models and algorithms can leverage a diverse range of factors, including radiomics and microbiomics, to predict the probability of various outcomes, such as morbidity, mortality, adverse events, length of hospitalization, and trauma recidivism, for individual patients having emergency medical care or treatment. Nevertheless, significant limitations to the applicability of these models in clinical practice persist. One such limitation is that they may not universally apply to all patient populations or healthcare settings. The development of these models frequently depends on data from specific populations, and their precision may be compromised when used in different settings or with different populations. Additionally, the development and validation of these models are subject to potential biases. The selection of predictor variables and statistical methods for model or algorithm construction may introduce biases, which can impact their accuracy and dependability. Despite these limitations, the application of prediction models and artificial intelligence has demonstrated significant potential in improving patient outcomes and optimizing clinical decision-making in the domain of emergency medicine.
For this Article Collection, the goal is to solicit submission of any articles to showcase the methodologies of developing prediction models and artificial intelligence in the domains of emergency medicine, or develop new clinical prediction models that integrate evidence-based predictor selection or incorporate omics predictors including but not limited to:
- The establishment of core risk factor sets (CRFS) and core outcome sets (COS) that are used to develop risk prediction models in emergency medicine.
- Omics variables, such as radiomics and microbiomics incorporated to develop new risk prediction models and artificial intelligence algorithms.
- Development of consensus guidelines for CRFS or COS in emergency medicine.
- Systematic reviews of current risk prediction models and artificial intelligence in emergency medicine.
We accept the following types of articles: Research Articles, Systematic Reviews and Meta-Analyses, Review Articles, Mini Reviews, and Registered Study Protocols.
Annals of Medicine is an online, open-access, international journal publishing across all areas of medicine and is part of our Elevate Series. This means that you will receive a concierge-level publishing experience, including dedicated support from our expert in-house Editorial team, with guaranteed response times of within 48 hours, an initial decision on whether your article will be peer-reviewed within 5 working days, and a first decision on your research within an average of 22 working days.
