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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
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The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Ensemble neural models for ICD code prediction using unstructured and structured healthcare data.

Alimurtaza Mustafa Merchant1, Naveen Shenoy1, Sidharth Lanka1

  • 1Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Srinivas Nagar P.O., Mangalore, 575025, Karnataka, India.

Heliyon
|September 16, 2024
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Summary

This study introduces an AI model for automated disease coding from clinical notes, improving accuracy and efficiency in healthcare. Ensemble models combining structured and unstructured data show superior performance for real-world deployment.

Keywords:
Artificial intelligenceAutomated medical codingHealthcare informaticsLabel attentionUnstructured text modeling

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Clinical Natural Language Processing

Background:

  • Disease coding is crucial for patient tracking but is manual, costly, and prone to errors.
  • Automating disease coding with Artificial Intelligence (AI) is vital for efficient Hospital Information Management Systems.
  • Convolutional Neural Network (CNN)-based approaches currently represent the state-of-the-art in automated coding.

Purpose of the Study:

  • To propose a novel neural model for automated diagnostic coding using unstructured clinical text.
  • To enhance the model's ability to learn label-specific features and relevant clinical text snippets.
  • To improve diagnostic code prediction by integrating code descriptions and considering structured clinical data through ensembling.

Main Methods:

  • Developed a neural model utilizing unstructured clinical text (discharge summaries).
  • Incorporated a structured self-attention mechanism to identify label-specific vectors and key text snippets.
  • Integrated a code description pipeline and explored model ensembling with supervised machine learning (Random Forest, Boosting) on structured data.

Main Results:

  • The proposed model achieved state-of-the-art performance on the MIMIC-III dataset, outperforming Longformer and Knowledge Graph models.
  • Ensemble models combining unstructured and structured data demonstrated superior performance over models using only one data type.
  • The findings highlight the potential of ensemble approaches for enhancing diagnostic code prediction accuracy.

Conclusions:

  • The developed AI model effectively automates diagnostic coding from clinical notes.
  • Ensemble models integrating both unstructured and structured clinical data offer significant advantages for real-world healthcare applications.
  • This approach promises to enhance the accuracy and efficiency of disease coding in hospital information systems.