CaregiverMatcher: graph neural networks for connecting caregivers of rare disease patients

Guerranti, Filippo; Mannino, Mirco; Baccini, Federica; Bongini, Pietro; Pancino, Niccolò; Visibelli, Anna; Marziali, Sara

doi:10.1016/j.procs.2021.08.174

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Furthermore, we plan to use more advanced deep learning methods, such as graph neural networks (GNNs) [31,32], to handle interactions between employees based on the hypothesis of homophily, that is, employees who are "closer" to each other are more likely to behave similarly. GNNs have been used in several real-world applications when there exists a relational structure between the entities under consideration [33,34]. In addition, the collection of a dataset with a larger number of observations, further features, and-possibly-a relational structure could certainly lead to obtaining better results.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Machine Learning Approaches for Predicting Employee Attrition

Guerranti

Dimitri

2022

Applied Sciences

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Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Machine Learning Approaches for Predicting Employee Attrition

Guerranti

Dimitri

2022

Applied Sciences

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“…Also, the power of GNNs in community analysis was exploited to build the proof of concept of a mechanism to create a community of caregivers of rare disease patients. The implementation of a smartphone app to connect caregivers with each other would be beneficial to them as being a caregiver is often a challenge from many point of views, and sharing experiences and sensations could improve their capability of facing such challenges [65].…”

Section: Graph Processing Via Graph Neural Networkmentioning

confidence: 99%

Deep learning techniques for biomedical data processing

Bianchini¹,

Dimitri²

2023

IDT

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The interest in Deep Learning (DL) has seen an exponential growth in the last ten years, producing a significant increase in both theoretical and applicative studies. On the one hand, the versatility and the ability to tackle complex tasks have led to the rapid and widespread diffusion of DL technologies. On the other hand, the dizzying increase in the availability of biomedical data has made classical analyses, carried out by human experts, progressively more unlikely. Contextually, the need for efficient and reliable automatic tools to support clinicians, at least in the most demanding tasks, has become increasingly pressing. In this survey, we will introduce a broad overview of DL models and their applications to biomedical data processing, specifically to medical image analysis, sequence processing (RNA and proteins) and graph modeling of molecular data interactions. First, the fundamental key concepts of DL architectures will be introduced, with particular reference to neural networks for structured data, convolutional neural networks, generative adversarial models, and siamese architectures. Subsequently, their applicability for the analysis of different types of biomedical data will be shown, in areas ranging from diagnostics to the understanding of the characteristics underlying the process of transcription and translation of our genetic code, up to the discovery of new drugs. Finally, the prospects and future expectations of DL applications to biomedical data will be discussed.

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“…In the biological domain, GNNs and GCNs have been applied to drug discovery [ 18 ], the prediction of compound mutagenicity [ 19 ], anti–HIV activity [ 20 , 21 ], and protein–protein interactions [ 22 ], just to name a few tasks. In particular, our method [ 23 ], which is specifically well-suited to biological applications [ 24 ], has been successfully applied to the following: molecular graph generation [ 25 ], integrating the generation method to a full pipeline of GNN-based filters for candidate selection in the drug discovery domain [ 26 ]; drug side-effect prediction on a heterogeneous graph integrating many different sources of information [ 27 ], on molecular graphs only [ 28 ], and on a combination of these two setups; the identification of protein–protein interactions [ 22 ]; link prediction for suggesting possible matches in a caregiver support network [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Composite Graph Neural Networks for Molecular Property Prediction

Bongini,

Pancino,

Bendjeddou

et al. 2024

IJMS

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Graph Neural Networks have proven to be very valuable models for the solution of a wide variety of problems on molecular graphs, as well as in many other research fields involving graph-structured data. Molecules are heterogeneous graphs composed of atoms of different species. Composite graph neural networks process heterogeneous graphs with multiple-state-updating networks, each one dedicated to a particular node type. This approach allows for the extraction of information from s graph more efficiently than standard graph neural networks that distinguish node types through a one-hot encoded type of vector. We carried out extensive experimentation on eight molecular graph datasets and on a large number of both classification and regression tasks. The results we obtained clearly show that composite graph neural networks are far more efficient in this setting than standard graph neural networks.

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CaregiverMatcher: graph neural networks for connecting caregivers of rare disease patients

Cited by 4 publications

References 20 publications

A Comparison of Machine Learning Approaches for Predicting Employee Attrition

A Comparison of Machine Learning Approaches for Predicting Employee Attrition

Deep learning techniques for biomedical data processing

Composite Graph Neural Networks for Molecular Property Prediction

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