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 discuss an approach based on statistical methods for learning both the optimal dose adjustment for OA and the time date required for the next laboratory control. This approach has been integrated in DNTAO-SE, an expert system for supporting hematologists in the definition of OAT prescriptions. In the paper, besides discussing the approach, we also present experimental results obtained by running DNTAO-SE on a database containing more than 4500 OAT prescriptions, collected from a hematological laboratory for the period December 2003 - February 2004.
Learning the dose adjustment for the oral anticoagulation treatment
GAMBERONI, Giacomo;LAMMA, Evelina;STORARI, Sergio;
2004
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 discuss an approach based on statistical methods for learning both the optimal dose adjustment for OA and the time date required for the next laboratory control. This approach has been integrated in DNTAO-SE, an expert system for supporting hematologists in the definition of OAT prescriptions. In the paper, besides discussing the approach, we also present experimental results obtained by running DNTAO-SE on a database containing more than 4500 OAT prescriptions, collected from a hematological laboratory for the period December 2003 - February 2004.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
