Building artificial intelligence and machine learning models : a primer for emergency physicians

Abstract

There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emergency department (ED) implementations being developed. For EM researchers building models de novo, collaborative working with data scientists is invaluable throughout the process. Synergism and understanding between domain (EM) and data experts increases the likelihood of realising a successful real-world model. Our linked manuscript provided a conceptual framework (including a glossary of AI terms) to support clinicians in interpreting AI research. The aim of this paper is to supplement that framework by exploring the key issues for clinicians and researchers to consider in the process of developing an AI model.

An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies

“…In general, the dataset should be divided into training datasets (70% of samples), test datasets (20% of samples) and validation datasets (10% of samples) [71]. Furthermore, the samples should also include external datasets to better validate the results of the radiomics and artificial intelligence procedures [72][73][74][75].…”

Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models

“…Categorical variables were converted into dummy numerical values, and all variables were scaled in the 0–1 range. 7 …”

“…Categorical variables were converted into dummy numerical values, and all variables were scaled in the 0-1 range. 7 We implemented four supervised learning models to predict the positivity for SARS-CoV-2 on the rtPCR test (ie, the gold standard), the target variable for all models (figure 1). Results were evaluated with a 10-fold cross-validation protocol: the entire available dataset was divided into 10 subsets, and each of them was used once to validate a model trained on the other 9 subsets, with a final evaluation based on the distribution of the metrics across the different iterations.…”

“…Results were evaluated with a 10-fold cross-validation protocol: the entire available dataset was divided into 10 subsets, and each of them was used once to validate a model trained on the other 9 subsets, with a final evaluation based on the distribution of the metrics across the different iterations. 7 For each model, we tested four different algorithms (ie, logistic regression, random forest classifier, support vector machine and Gaussian Naïve Bayes). The different explanatory variables were included in the models as follows: ► Model 1: age, gender and variables retrieved by the operatorbased interview.…”

Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine

“…An understanding of model development is useful when interpreting an AI research paper. More detail on this process is discussed in a linked companion paper26.…”