AI-Driven Clinical Decision Support: Enhancing Disease Diagnosis Exploiting Patients Similarity

Detecting diseases at early stage can help to overcome and treat them accurately. A Clinical Decision Support System (CDS) facilitates the identification of diseases together with the most suitable treatments. In this paper, we propose a CDS framework able to integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, health records and social media data. Using the data so collected, innovative machine learning and deep learning approaches can be employed. A neural network model for predicting patients' future health conditions is proposed. The approach employs word embedding to model the semantic relations of hospital admissions, symptoms and diagnosis, and it introduces a mechanism to measure the relationships of different diagnosis in terms of symptoms similarity to exploit for the prediction task. Several CDSs, including diagnostic decision support systems for inferring patient diagnosis, have been proposed in the literature. However, these methods typically focus on a single patient and apply manually or automatically constructed decision rules to produce a diagnosis. Even worst, they consider only a single medical condition, whereas it is not uncommon that a patient has more than one medical condition at the same time. The novelty of the proposed approach is the combination of supervised and unsupervised artificial intelligence methods allowing to combine several and heterogeneous data sources related to a multitude of patients and concerning different medical conditions. Furthermore, with respect to previous approaches, the diagnosis prediction problem is formulated to predict the exact diagnosis in terms of semantic meaning by exploiting Natural Language Processing concepts. Experimental results, performed on a real-world EHR dataset, show that the proposed approach is effective and accurate and provides clinically meaningful interpretations. The obtained outcomes are promising for future extensions of the framework that could be a valuable means for automatic inferring disease diagnosis.