NLP for Schizophrenia Detection
Utilising Natural Language Processing (NLP) techniques to identify linguistic markers associated with schizophrenia, providing a cost-effective screening tool for healthcare providers.
🧐 The Problem
Schizophrenia is often diagnosed late due to the subtle nature of early symptoms. Traditional diagnostic methods can be subjective, expensive, and require highly specialised clinicians who are in short supply. There is a critical need for accessible, objective screening tools.
💡 Methodology
This project leveraged a dataset of patient interviews. By applying NLP techniques, we extracted syntactic and semantic features from the transcripts. Neural networks were then trained to distinguish between patients with schizophrenia and a control group based on these linguistic patterns.
- ✓Feature extraction using Python (NLTK, spaCy).
- ✓Deep Learning models implemented in TensorFlow/Keras.
- ✓Rigorous cross-validation to ensure model generalisability.
Results & Impact
The model achieved a high accuracy rate in distinguishing cases, identifying 10 previously undiagnosed sufferers in the test set. This automated approach offers a solution roughly 10x more affordable than standard manual assessment, potentially supporting NHS hospitals facing financial constraints.