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OBJECTIVE Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records. DESIGN Authors developed a novel supervised learning model that incorporates two machine learning algorithms and several rule-based(More)
The automatic conversion of free text into a medical ontology can allow computational access to important information currently locked within clinical notes and patient reports. This system introduces a new method for automatically identifying medical concepts from the SNOMED Clinical Terminology in free text in near real time. The system presented consists(More)
OBJECTIVE Information extraction and classification of clinical data are current challenges in natural language processing. This paper presents a cascaded method to deal with three different extractions and classifications in clinical data: concept annotation, assertion classification and relation classification. MATERIALS AND METHODS A pipeline system(More)
This paper presents a rationale, created from first principles, for the design criteria for the architecture of clinical information systems. The criteria are developed according to the heuristic axiom of Ockham's Razor, presented here for the first time and operationalised in the form of three principles; Generalization, Minimalization and Coverage. The(More)
OBJECTIVE Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a(More)
Objective: There are abundant mentions of clinical conditions, anatomical sites, medications and procedures in clinical documents. This paper describes use of a cascade of machine learners to automatically extract mentions of named entities about disorders from clinical notes. Tasks: A Conditional Random Field (CRF) machine learner has been used for named(More)