Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology
| hal.structure.identifier | Modélisation Mathématique pour l'Oncologie [MONC] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | BENZEKRY, Sébastien | |
| dc.date.accessioned | 2024-04-04T02:50:21Z | |
| dc.date.available | 2024-04-04T02:50:21Z | |
| dc.date.issued | 2020-06-18 | |
| dc.identifier.issn | 0009-9236 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191905 | |
| dc.description.abstractEn | The amount of ‘big’ data generated in clinical oncology, whether from molecular, imaging, pharmacological or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically-based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging or electronic health records), pharmacometrics, quantitative systems pharmacology, tumor size kinetics, and metastasis modeling. Focus is set on studies with high potential of clinical translation, as well as applied to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: ‘mechanistic learning’. | |
| dc.language.iso | en | |
| dc.publisher | American Society for Clinical Pharmacology and Therapeutics | |
| dc.title.en | Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1002/cpt.1951 | |
| dc.subject.hal | Informatique [cs]/Modélisation et simulation | |
| dc.subject.hal | Sciences du Vivant [q-bio]/Cancer | |
| dc.subject.hal | Physique [physics]/Physique [physics]/Analyse de données, Statistiques et Probabilités [physics.data-an] | |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | |
| dc.subject.hal | Sciences du Vivant [q-bio]/Sciences pharmaceutiques/Pharmacologie | |
| dc.subject.hal | Statistiques [stat]/Applications [stat.AP] | |
| dc.subject.hal | Informatique [cs]/Intelligence artificielle [cs.AI] | |
| bordeaux.journal | Clinical Pharmacology and Therapeutics | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02916941 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02916941v1 | |
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