Several attempts have been recently provided to define Oral Anticoagulant (OA) guidelines. These guidelines include indications for oral anticoagulation and suggested arrangements for the management of an oral anticoagulant service. They aim to take care of the current practical difficulties involved in the safe monitoring of the rapidly expanding numbers of patients on long-term anticoagulant therapy. Nowadays, a number of computer-based systems exist for supporting hematologists in the oral anticoagulation therapy. Nonetheless, computer-based support improves the quality of the Oral Anticoagulant Therapy (OAT) and also possibly reduces the number of scheduled laboratory controls. In this paper, we describe DNTAO-SE, a system which integrates both knowledge based and statistical techniques in order to support hematologists in the definition of OAT prescriptions to solve the limitations of the currently proposed OAT systems. The statistical method is used to learn both the optimal dose adjustment for OA and the time date required for the next laboratory control. In the paper, besides discussing the validity of these approaches, we also present experimental results obtained by running DNTAO-SE on a database containing more than 13000 OAT prescriptions. This paper is a better structured and complete version of a paper previously published in the “Intelligenza Artificiale” national italian journal edited by the AI*IA society [3].
An expert system for the oral anticoagulation treatment
GAMBERONI, Giacomo;LAMMA, Evelina;STORARI, Sergio
2005
Abstract
Several attempts have been recently provided to define Oral Anticoagulant (OA) guidelines. These guidelines include indications for oral anticoagulation and suggested arrangements for the management of an oral anticoagulant service. They aim to take care of the current practical difficulties involved in the safe monitoring of the rapidly expanding numbers of patients on long-term anticoagulant therapy. Nowadays, a number of computer-based systems exist for supporting hematologists in the oral anticoagulation therapy. Nonetheless, computer-based support improves the quality of the Oral Anticoagulant Therapy (OAT) and also possibly reduces the number of scheduled laboratory controls. In this paper, we describe DNTAO-SE, a system which integrates both knowledge based and statistical techniques in order to support hematologists in the definition of OAT prescriptions to solve the limitations of the currently proposed OAT systems. The statistical method is used to learn both the optimal dose adjustment for OA and the time date required for the next laboratory control. In the paper, besides discussing the validity of these approaches, we also present experimental results obtained by running DNTAO-SE on a database containing more than 13000 OAT prescriptions. This paper is a better structured and complete version of a paper previously published in the “Intelligenza Artificiale” national italian journal edited by the AI*IA society [3].I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
