Learn More
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)
Information Extraction, from the electronic clinical record is a comparatively new topic for computational linguists. In order to utilize the records to improve the efficiency and quality of health care, the knowledge content should be automatically encoded; however this poses a number of challenges for Natural Language Processing (NLP). In this paper, we(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)