Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and...

Area of Science:

• Computational Linguistics
• Developmental Psychology
• Artificial Intelligence in Healthcare

Background:

• Traditional language sample analysis faces clinical barriers, hindering efficient assessment.
• Narrative language sampling provides rich data but requires extensive manual processing.
• Automated methods are emerging to overcome these limitations in child language analysis.

Purpose of the Study:

• To review computerized approaches for analyzing narrative language in children.
• To explore the identification of language disorders using AI and machine learning.
• To highlight the potential of automated tools for improving language assessment efficiency and accessibility.

Main Methods:

• Review of recent studies on natural language processing (NLP) and machine learning (ML) for linguistic feature extraction.
• Explanation of how AI tools operate in analyzing child language samples.
• Discussion of the impact of design decisions on the performance of these automated tools.

Main Results:

• Automated extraction of linguistic features is demonstrated through NLP and ML.
• Identification of developmental language disorder is achievable using these advanced computational techniques.
• AI tools show promise in analyzing complex features of child language samples.

Conclusions:

• Computer-automated approaches represent a significant advancement in language sample analysis.
• These methods offer enhanced efficiency and accessibility for diagnosing and treating childhood language disorders.
• Addressing challenges like bias, access, and generalizability is crucial for widespread adoption.