J Am Med Inform Assoc - Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

Tópicos

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Resumo

JECTIVE: As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.DESIGN: The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline.MEASUREMENTS: Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set.RESULTS: The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second).CONCLUSION: For all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.

Resumo Limpo

jectiv clinic text mine continu matur potenti enabl technolog innov patient care clinic research becom realiti critic part process rigid benchmark test natur languag process method realist clinic narrat paper author describ design perform three stateoftheart textmin applic nation research council canada evalu within ib challengedesign three system perform three key step clinic inform extract extract medic problem test treatment discharg summari progress note classif assert made medic problem classif relat medic concept machin learn system perform task use largedimension bag featur deriv text extern sourc uml ctake medlinemeasur perform measur per subtask use microaverag fscore calcul compar system annot groundtruth annot test setresult system rank high among submit system competit follow fscore concept extract rank first assert detect rank first relationship detect rank secondconclus task found introduct wide rang featur crucial success import choic machin learn algorithm allow us versatil featur design introduc larg number featur without overfit without encount computingresourc bottleneck

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