Abstract
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.
Highlights
Health data from disparate medical sources is collected continuously, leading to the generation of huge amount of information
To address the above issues and challenges, in [2] we proposed a Clinical Decision Support System (CDS) framework that integrates heterogeneous health data collected from disparate sources, such as laboratory test results, medical images and electronic health records
In this context a fundamental challenge is how to correctly model such temporal and high dimensional electronic health records (EHRs) data to significantly improve the performance of prediction
Summary
Health data from disparate medical sources is collected continuously, leading to the generation of huge amount of information. This paper addresses the challenging issue of inferring patient diagnoses, exploiting electronic health records and medical data upon hospitalization To this purpose, is proposed a novel framework for diagnostic prediction based on patientsimilarity, using basic patient-specific information gathered at hospital admissions, including medical history, blood tests, laboratory results and demographics to identify similar patients and subsequently predicting patient outcomes. We apply the word embedding approach to categorize text fragments, at a sentence level, based on the emergent semantics extracted from a corpus of medical text In this context a fundamental challenge is how to correctly model such temporal and high dimensional EHR data to significantly improve the performance of prediction.
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