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Classification of Illness

Classification of Illness

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.
Acute illness is severe...

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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An early illness recognition framework using a temporal Smith Waterman algorithm and NLP.

Zahra Hajihashemi¹, Mihail Popescu¹

¹University of Missouri, Columbia, MO.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 20, 2014

Summary

This summary is machine-generated.

This study introduces a novel framework for early illness detection in aging populations using sensor data and natural language processing (NLP). The system analyzes sensor sequences and electronic health record (EHR) notes to identify health patterns, aiding timely medical intervention.

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Area of Science:

Gerontology
Biomedical Informatics
Data Science

Background:

Aging in place facilities require advanced methods for early illness detection.
Integrating non-wearable sensor data with electronic health records (EHR) offers potential for proactive healthcare.
Existing methods may not fully leverage the temporal and semantic information in health data.

Purpose of the Study:

To develop and evaluate a framework for detecting health patterns using sensor sequence similarity and natural language processing (NLP).
To enable early illness recognition in an aging in place setting through automated health problem detection.
To adapt genomic sequence annotation algorithms for time-series sensor data and clinical notes.

Main Methods:

Deployment of 47 sensor networks and an EHR system at TigerPlace, an aging in place facility.
Utilizing sensor sequence similarity algorithms, inspired by Smith-Waterman (SW), with a proposed temporal variant (TSW).
Employing NLP tools like Metamap to extract health concepts from EHR nursing comments and associate them with sensor sequences.

Main Results:

A pilot study on three residents demonstrated the framework's capability to detect health patterns.
Achieved an average precision of 0.64 and a recall of 0.37 on a dataset of 1685 sensor days and 626 nursing records.
Highlighted challenges in determining optimal time sequence similarity and aggregating medical concepts.

Conclusions:

The proposed framework shows promise for early illness recognition in aging populations by combining sensor data and NLP.
Further research is needed to refine temporal similarity measures and concept aggregation for improved performance.
This approach has the potential to enhance proactive healthcare monitoring in residential aging facilities.