Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data

Development of a Computable Phenotype for Prehospital Pediatric Asthma Encounters

INTRODUCTION

Asthma exacerbations are a common cause of pediatric Emergency Medical Services (EMS) encounters. Accordingly, prehospital management of pediatric asthma exacerbations has been designated an EMS research priority. However, accurate identification of pediatric asthma exacerbations from the prehospital record is nuanced and difficult due to the heterogeneity of asthma symptoms, especially in children. Therefore, this study's objective was to develop a prehospital-specific pediatric asthma computable phenotype (CP) that could accurately identify prehospital encounters for pediatric asthma exacerbations.

METHODS

This is a retrospective observational study of patient encounters for ages 2-18 years from the ESO Data Collaborative between 2018 and 2021. We modified two existing rule-based pediatric asthma CPs and created three new CPs (one rule-based and two machine learning-based). Two pediatric emergency medicine physicians independently reviewed encounters to assign labels of asthma exacerbation or not. Taking that labeled encounter data, a 50/50 train/test split was used to create training and test sets from the labeled data. A 90/10 split was used to create a small validation set from the training set. We used specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and macro F1 to compare performance across all CP models.

RESULTS

After applying the inclusion and exclusion criteria, 24,283 patient encounters remained. The machine-learning models exhibited the best performance for the identification of pediatric asthma exacerbations. A multi-layer perceptron-based model had the best performance in all metrics, with an F1 score of 0.95, specificity of 1.00, sensitivity of 0.91, negative predictive value of 0.98, and positive predictive value of 1.00.

CONCLUSION

We modified existing and developed new pediatric asthma CPs to retrospectively identify prehospital pediatric asthma exacerbation encounters. We found that machine learning-based models greatly outperformed rule-based models. Given the high performance of the machine-learning models, the development and application of machine learning-based CPs for other conditions and diseases could help accelerate EMS research and ultimately enhance clinical care by accurately identifying patients with conditions of interest.

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

Our scoping review provides a comprehensive analysis of the landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It contributes to the field by highlighting the most studied AI applications and identifying the most common methodological approaches across 106 included studies. The findings indicate a promising future for AI in PEC, with many unique use cases, such as prognostication, demand prediction, resource optimization, and the Internet of Things continuous monitoring systems. Comparisons with other approaches showed AI outperforming clinicians and non-AI algorithms in most cases. However, most studies were internally validated and retrospective, highlighting the need for rigorous prospective validation of AI applications before implementation in clinical settings. We identified knowledge and methodological gaps using an evidence map, offering a roadmap for future investigators. We also discussed the significance of explainable AI for establishing trust in AI systems among clinicians and facilitating real-world validation of AI models.

Trends and opportunities in computable clinical phenotyping: A scoping review

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in biomedicine and particularly timely in precision medicine. Many research groups deliver pipelines that automatically retrieve and analyze data elements from one or more sources to automate this task and deliver high-performing computable phenotypes. We applied a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a thorough scoping review on computable clinical phenotyping. Five databases were searched using a query that combined the concepts of automation, clinical context, and phenotyping. Subsequently, four reviewers screened 7960 records (after removing over 4000 duplicates) and selected 139 that satisfied the inclusion criteria. This dataset was analyzed to extract information on target use cases, data-related topics, phenotyping methodologies, evaluation strategies, and portability of developed solutions. Most studies supported patient cohort selection without discussing the application to specific use cases, such as precision medicine. Electronic Health Records were the primary source in 87.1 % (N = 121) of all studies, and International Classification of Diseases codes were heavily used in 55.4 % (N = 77) of all studies, however, only 25.9 % (N = 36) of the records described compliance with a common data model. In terms of the presented methods, traditional Machine Learning (ML) was the dominant method, often combined with natural language processing and other approaches, while external validation and portability of computable phenotypes were pursued in many cases. These findings revealed that defining target use cases precisely, moving away from sole ML strategies, and evaluating the proposed solutions in the real setting are essential opportunities for future work. There is also momentum and an emerging need for computable phenotyping to support clinical and epidemiological research and precision medicine.

The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports

Purpose

Current injury surveillance efforts in agriculture are considerably hampered by the limited quantity of occupation or industry data in current health records. This has impeded efforts to develop more accurate injury burden estimates and has negatively impacted the prioritization of workplace health and safety in state and federal public health efforts. This paper describes the development of a Naïve Bayes machine learning algorithm to identify occupational injuries in agriculture using existing administrative data, specifically in pre-hospital care reports (PCR).

Methods

A Naïve Bayes machine learning algorithm was trained on PCR datasets from 2008–2010 from Maine and New Hampshire and tested on newer data from those states between 2011 and 2016. Further analyses were devoted to establishing the generalizability of the model across various states and various years. Dual visual inspection was used to verify the records subset by the algorithm.

Results

The Naïve Bayes machine learning algorithm reduced the volume of cases that required visual inspection by 69.5 percent over a keyword search strategy alone. Coders identified 341 true agricultural injury records (Case class = 1) (Maine 2011–2016, New Hampshire 2011–2015). In addition, there were 581 (Case class = 2 or 3) that were suspected to be agricultural acute/traumatic events, but lacked the necessary detail to make a certain distinction.

Conclusions

The application of the trained algorithm on newer data reduced the volume of records requiring visual inspection by two thirds over the previous keyword search strategy, making it a sustainable and cost-effective way to understand injury trends in agriculture.