One of the most important reasons for clinicians needing a fast o

One of the most important reasons for clinicians needing a fast overview is when the record concerns a patient who is unknown [14]. We present here a computational system (a Report Generator) that automatically www.selleckchem.com/products/GDC-0941.html produces textual summaries of medical histories, and a study of its use by clinicians. We show that summaries, even when computer

generated, can be a useful tool for clinicians at the point of care, providing an accurate overview of the patient’s history in half the time. We developed a natural language generation system that produces a range of summarised reports of patient records from data-encoded views of patient histories derived from a repository of medical records of cancer patients, composed of narrative documents (e.g., letters, discharge reports, etc.) and structured data (e.g., test results, prescriptions, etc.) [20]. Although we are concentrating on cancer patients, we aim to produce good quality reports without the need to construct extensive domain models. Our typical user is a GP or clinician who uses electronic patient records at the point of care to familiarise themselves with a patient’s medical history and current situation. Information is extracted from medical narratives, using NLP techniques, as described in [21] and aggregated with structured data in order to build complex images of a patient’s medical

history which model the story of Osimertinib ic50 how the patient’s illnesses and treatments unfolded through time: what happened, when, what was done, when it was done, and why. The resulting complex semantic network, termed by us a Chronicle, allows the construction of targeted summarised reports which do more than present individual events in a medical history: they present, in coherent text, events that are Endonuclease semantically and temporally linked to each other. We provide here a brief general overview; more detailed technical descriptions of the Report Generator are available in [22] and [23]. The input to the Report Generator is a Chronicle. The methodology involved in transforming an EPR into a Chronicle is complex and involves

Information Extraction from narratives, solving multi-document coreference, temporal abstraction and inferencing over both structured and information extraction data [21]. The main advantage in using a Chronicle as opposed to a less structured Electronic Patient Record lies in the richness of information provided. Having access to not only facts, but to also the relations between them, has important implications in the design of the content selection and text structuring stages. This facilitates better and easier text generation and allows for a higher degree of flexibility of the generated text. The output of the Report Generator is a range of textual summaries of the information contained in the Chronology. These range in length from short paragraphs to many pages.

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