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Related Experiment Video

Updated: May 24, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

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Published on: February 25, 2013

An Assessment of Trajectory Analysis for Disease Risk Prediction: A Scoping Review.

Freya Pollington1, Spiros C Denaxas2, Kezhi Li2

  • 1Epidemiology of Cancer Healthcare and Outcomes (ECHO) Research Group, Department of Behavioural Science and Health, Institute of Epidemiology & Health Care, University College London, London, UK, WC1E 7HB.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Structured electronic health records (EHRs) analyze patient trajectories to predict disease risk. This review examines studies using temporal EHR data for disease signatures and prediction, assessing methods and performance.

Keywords:
DLElectronic health recordsdiagnosis codesrisk prediction

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Last Updated: May 24, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

  • Health Informatics
  • Computational Medicine
  • Biostatistics

Background:

  • Electronic Health Records (EHRs) are increasingly utilized for clinical research.
  • Analyzing patient trajectories within EHRs offers potential for disease prediction.
  • Temporal sequences in EHR data hold valuable information for identifying disease signatures.

Purpose of the Study:

  • To conduct a scoping review of studies using temporal EHR sequences for disease prediction.
  • To assess the characteristics of studies employing patient trajectories for risk identification.
  • To identify common model types, evaluate performance metrics, and appraise reporting standards.

Main Methods:

  • Conducted a systematic literature search on PubMed and Web of Science.
  • Included studies that utilized temporal EHR sequences to identify disease signatures or predict disease.
  • Performed a scoping review to synthesize study characteristics, model types, performance, and reporting.

Main Results:

  • Identified a growing body of research on using temporal EHR data for disease prediction.
  • Cataloged various analytical models applied to patient trajectories.
  • Assessed the heterogeneity in performance reporting and methodological approaches across studies.

Conclusions:

  • Temporal EHR data analysis is a promising area for predictive modeling in healthcare.
  • Further standardization in methodology and reporting is needed to advance the field.
  • This review highlights key trends and gaps in the current literature on EHR-based disease prediction.