Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets.

ABSTRACT: BACKGROUND:Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution. OBJECTIVE:The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains. METHODS:Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked l

SUBMITTER: Chishti S 

PROVIDER: S-EPMC7218591 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature