Disease Identification From Unstructured User Input
This work addresses disease identification from user-generated text, which is an incremental advancement in medical informatics.
The paper tackles the problem of identifying probable diseases from unstructured textual input, such as health forum posts, by developing a two-phase text classification module and a symptom-disease correlation-based similarity measurement module, resulting in a method that extracts inherent features for improved decision-making.
A method to identify probable diseases from the unstructured textual input (eg, health forum posts) by incorporating a lexicographic and semantic feature based two-phase text classification module and a symptom-disease correlation-based similarity measurement module. One notable aspect of my approach was to develop a competent algorithm to extract all inherent features from the data source to make a better decision.