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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Updated: Jul 17, 2025

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Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic

Lucía A Carrasco-Ribelles1,2,3, José Llanes-Jurado4, Carlos Gallego-Moll1,3

  • 1Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol I Gurina (IDIAPJGol), Barcelona, 08007, Spain.

Journal of the American Medical Informatics Association : JAMIA
|September 2, 2023
PubMed
Summary

Artificial intelligence (AI) effectively uses longitudinal electronic health records (EHRs) for health outcome prediction. However, inconsistent reporting and limited data sharing hinder model replication and validation.

Keywords:
artificial intelligencedeep learningelectronic health recordslongitudinal datapredictionsystematic review

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Health Outcomes Research

Background:

  • Longitudinal data from electronic health records (EHRs) offer valuable insights for predicting health outcomes.
  • Artificial intelligence (AI) techniques are increasingly applied to analyze complex EHR data.

Approach:

  • Systematic review of studies utilizing AI with longitudinal EHR data for health outcome prediction.
  • Searched multiple databases (MEDLINE, Scopus, Web of Science, IEEE Xplorer) up to January 2022.
  • Assessed study methodology, AI techniques, prediction tasks, validation, and reporting quality.

Key Points:

  • Eighty-one studies were included, predominantly predicting disease development or events using diagnoses and drug treatments.
  • Recurrent Neural Networks and transformer architectures were common, with increasing use of combined layers and attention mechanisms.
  • Most studies had poor reporting quality, a high risk of bias, and inadequate performance assessment due to single train-test partitions.

Conclusions:

  • AI models show promise in leveraging longitudinal EHR data for health predictions.
  • Heterogeneity in reporting, lack of public EHR datasets, and limited code sharing impede reproducibility.
  • Standardized reporting and data sharing are crucial for advancing AI in healthcare.