Artificial intelligence in differentiating tropical infections: A step ahead.

ABSTRACT:

Background and objective

Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections.

Methodology

A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a mac

SUBMITTER: Shenoy S 

PROVIDER: S-EPMC9246149 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature