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Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies

Year: 2024

Type: book-chapter

Abstract: Thrombophilia, a predisposition to thrombosis, poses significant diagnostic challenges due to its multi-factorial nature, encompassing genetic and acquired factors. Current diagnostic paradigms, primarily relying on a combination of clinical assessment and targeted laboratory tests, often fail to capture the complex interplay of factors contributing to thrombophilia risk. This paper proposes an innovative artificial intelligence (AI)-based methodology aimed to enhance the prediction of thrombophilia risk. The designed multidimensional risk assessment model integrates and elaborates through AI a comprehensive collection of patient data types, including genetic markers, clinical parameters, patient history, and lifestyle factors, in order to obtain advanced and personalized explainable diagnoses.

Source: Studies in health technology and informatics

DOI: 10.3233/shti240073

Authors: Daniela Mazzuca, Francesco Zinno, Agostino Forestiero

Institutions: Magna Graecia University, Ospedale Annunziata di Cosenza, Institute for High Performance Computing and Networking, National Research Council

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4398782800

FWCI:

Cites: 4

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Related to: 10