Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies.
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 ca...
| Publicado en: | Studies in Health Technology & Informatics Vol. 314; pp. 125 - 127 |
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| Autores principales: | , , |
| Formato: | proceedings research Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177548710&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177548710 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 314 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 177548710 177548710 177548710 10.3233/SHTI240073 177548710 ppf: 125 ppct: 2 formats: tig: atl: Enhancing Thrombophilia Risk Prediction Through AI-Based Methodologies. aug: au: MAZZUCA, Daniela ZINNO, Francesco FORESTIERO, Agostino affil: Immunohaematology Section, Annunziata Hospital, Cosenza, Italy. sug: subj: Hematologic Diseases Risk Factors Artificial Intelligence Utilization Research Methodology Evaluation Individualized Medicine Risk Assessment Congresses and Conferences Italy Italy ab: 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. pubtype: Academic Journal doctype: proceedings research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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