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Codification and representation of knowledge from structured and unstructured repositories have been the requirement for Expert Systems designing. Structured repositories remain open for knowledge extraction and representation for building a knowledgebase (KB). ICD-v10 (International Classification of Diseases), a structured repository, is the 10th...
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Citations
... The hope is to have good methods to efficiently extract relevant features from electronic health records (EHR) with little information loss to solve these problems. For example, the codification and representation of knowledge from structured and unstructured repositories was the prerequisite for the design of expert systems based on the use of graph representations (Hema and Justus, 2015b). ...
Currently, the healthcare sector strives to increase the quality of patient management and improve the economic performance of healthcare providers. The data contained in electronic health records (EHRs) offer the potential to discover relevant patterns that aim to relate diseases and therapies, and thus discover patterns that could help identify empirical medical guidelines that reflect best practices in the healthcare system. Based on this pattern identification, it is then possible to implement recommendation systems based on the idea that a higher volume of procedures is associated with high-quality models.
Although there are several applications that use machine learning methods to identify these patterns, this identification is still a challenge, in part because these methods often ignore the basic structure of the population, considering the similarity of diagnoses and patient typology. To this end, we have developed graph methods that aim to cluster similar patients. In such models, patients are linked when the same or similar patterns can be observed for these patients, a concept that enables the construction of a network-like structure. This structure can then be analyzed with Graph Neural Networks (GNN) to identify relevant labels, in this case the appropriate medical procedures.
We report the construction of a patient Graph structure based on basic patient information like age and gender as well as the diagnoses and trained GNNs models to identify the corresponding patient therapies using a synthetic patient database. We compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and compared the performance of the different model methods. We have found that GNNs are superior, with an average improvement of the f1 score of 6.48% respect to the baseline models. In addition, the GNNs are useful for performing additional clustering analyses that allow specific identification of specific therapeutic clusters related to a particular combination of diagnoses.
We found that GNNs are a promising way to model the distribution of diagnoses in a patient population and thus better model how similar patients can be identified based on the combination of morbidities and comorbidities. Nevertheless, network building is still challenging and prone to prejudice, as it depends on how ICD distribution affects the patient network embedding space. This network setup requires not only a high quality of the underlying diagnostic ecosystem, but also a good understanding of how to identify related patients by disease. For this reason, additional work is needed to improve and better standardize patient embedding in graph structures for future investigations and applications of services based on this technology, and therefore is not yet an interventional study.
Background
Currently, the healthcare sector strives to improve the quality of patient care management and to enhance/increase its economic performance/efficiency (e.g., cost-effectiveness) by healthcare providers. The data stored in electronic health records (EHRs) offer the potential to uncover relevant patterns relating to diseases and therapies, which in turn could help identify empirical medical guidelines to reflect best practices in a healthcare system. Based on this pattern of identification model, it is thus possible to implement recommender systems with the notion that a higher volume of procedures is often associated with better high-quality models.
Methods
Although there are several different applications that uses machine learning methods to identify such patterns, such identification is still a challenge, due in part because these methods often ignore the basic structure of the population, or even considering the similarity of diagnoses and patient typology. To this end, we have developed a method based on graph-data representation aimed to cluster ‘similar’ patients. Using such a model, patients will be linked when there is a same and/or similar patterns are being observed amongst them, a concept that will enable the construction of a network-like structure which is called a patient graph.¹ This structure can be then analyzed by Graph Neural Networks (GNN) to identify relevant labels, and in this case the appropriate medical procedures that will be recommended.
Results
We were able to construct a patient graph structure based on the patient's basic information like age and gender as well as the diagnosis and the trained GNNs models to identify the corresponding patient's therapies using a synthetic patient database. We have even compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and also against the performance of these different model-methods. We have found that the GNNs models are superior, with an average improvement of the f1 score of 6.48 % in respect to the baseline models. In addition, the GNNs models are useful in performing additional clustering analysis which allow a distinctive identification of specific therapeutic/treatment clusters relating to a particular combination of diagnoses.
Conclusions
We found that the GNNs models offer a promising lead to model the distribution of diagnoses in patient population, and is thus a better model in identifying patients with similar phenotype based on the combination of morbidities and/or comorbidities. Nevertheless, network/graph building is still challenging and prone to biases as it is highly dependent on how the ICD distribution affects the patient network embedding space. This graph setup not only requires a high quality of the underlying diagnostic ecosystem, but it also requires a good understanding on how patients at hand are identified by disease respectively. For this reason, additional work is still needed to better improve patient embedding in graph structures for future investigations and the applications of this service-based technology. Therefore, there has not been any interventional study yet.
