This paper proposes text mining of clinical practices to extract decision-making steps. These steps should be formed in- logical functions capable of branching on different plan set on some deciding variables. The probable action sequence will be notified on the data of patient given to the conditions of clinical guideline and this will also give critical conditions that need immediate attention. In this project medical grammar rules are applied to extract key decision making steps from the clinical guidelines. In the first step lexical analysis is performed to key- words like ‘if this then perform this, all the medical terms will be identified and this extracted rule set will be used to create a XSLT file. The patient data in form of an XML file will be then applied to the XSLT transformations or rule sets to derive final result of action plan specific to that patient.
This paper proposes text mining of clinical practices to extract decision-making steps. These steps should be formed in- logical functions capable of branching on different plan set on some deciding variables. The probable action sequence will be notified on the data of patient given to the conditions of clinical guideline and this will also give critical conditions that need immediate attention. In this project medical grammar rules are applied to extract key decision making steps from the clinical guidelines. In the first step lexical analysis is performed to key- words like ‘if this then perform this, all the medical terms will be identified and this extracted rule set will be used to create a XSLT file. The patient data in form of an XML file will be then applied to the XSLT transformations or rule sets to derive final result of action plan specific to that patient.