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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Artificial Intelligence in Medicine

Background:

  • Automatic disease inference aids medical treatment efficiency.
  • Symptoms are crucial indicators for disease diagnosis and prediction.
  • Electronic Medical Records (EMR) contain valuable but often irregular symptom data.

Purpose of the Study:

  • To develop an improved disease inference method by integrating two distinct symptom representation techniques.
  • To address the challenges of uncertainty and irregularity in symptom descriptions within EMRs.
  • To enhance disease prediction accuracy by leveraging complex symptom-disease relationships.

Main Methods:

  • Symptom extraction from EMRs using the Metamap natural language processing tool.
  • Implementation of two symptom representation models: TF-IDF for symptom-disease relationships and Word2Vec for semantic symptom relationships.
  • Application of bidirectional Long Short-Term Memory (BiLSTM) networks to model symptom sequences.

Main Results:

  • The proposed model achieved an Area Under the Curve (AUC) of 0.895 and a F1 score of 0.572 for 50 diseases in the MIMIC-III dataset.
  • The combined approach of TF-IDF and Word2Vec representations outperformed models using only one representation.
  • Demonstrated significant improvement in disease inference accuracy.

Conclusions:

  • Integrating multiple symptom representation methods enhances disease inference performance.
  • The developed model offers a more accurate and robust approach to disease prediction from EMRs.
  • This method holds promise for improving automated diagnostic tools in healthcare.