MADRID, Spain — Artificial intelligence algorithms in thrombosis are complex and face several challenges. These challenges include obtaining quality data and good explicability without loss of accuracy. These emerging tools require more trained staff and patient input to become established in clinical practice.
The cornerstone of all AI systems under the protection of the data protection law is that they are varied, representative of reality, and certified, said Martín. In healthcare, 80% of data are unstructured, and the goal is to convert them into knowledge that brings value.Regarding the explicability of the models, Martín stated,"In the healthcare field, it's essential, although the challenge arises of favoring accuracy over explicability.
She mentioned important conclusions for developing models."The ICD-9 and ICD-10 classification of diseases don't reach sufficient sensitivity in the search for patients with venous thromboembolic disease in electronic medical records, so other diagnostic coding standards should be associated in accordance with daily clinical practice.
He also commented on the development of computable phenotypes and risk prediction models in patients with cancer and thrombosis. Thrombosis occurs seven times more often in patients with cancer vs those without tumors. Some types of cancer have 10% to 20% risk of venous thromboembolism in the first 6 months after diagnosis, and venous thromboembolism is the second leading cause of death, after infection, in these patients.