A de-identifier for medical discharge summaries

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A de-identifier for medical discharge summaries

OBJECTIVE Clinical records contain significant medical information that can be useful to researchers in various disciplines. However, these records also contain personal health information (PHI) whose presence limits the use of the records outside of hospitals. The goal of de-identification is to remove all PHI from clinical records. This is a challenging task because many records contain forei...

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Automated Annotation for Medical Discharge Summaries: A Preliminary Study

Presumably, with sufficient training data, it would be possible to build a classifier for each of the twenty predetermined complication labels. However since our training set consists of only a hundred and forty patients in total, and 14 of the 20 labels were associated with 10 or fewer patients, this approach was clearly infeasible. Similarly for the second task, there were only twenty patient...

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Research Paper: Electronically Screening Discharge Summaries for Adverse Medical Events

OBJECTIVE Detecting adverse events is pivotal for measuring and improving medical safety, yet current techniques discourage routine screening. The authors hypothesized that discharge summaries would include information on adverse events, and they developed and evaluated an electronic method for screening medical discharge summaries for adverse events. DESIGN A cohort study including 424 rando...

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Electronically Screening Discharge Summaries for Adverse Medical Events

Design: A cohort study including 424 randomly selected admissions to the medical services of an academic medical center was conducted between January and July 2000. The authors developed a computerized screening tool that searched free-text discharge summaries for trigger words representing possible adverse events. Measurements: All discharge summaries with a trigger word present underwent char...

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Identifying Smoking Status From Implicit Information in Medical Discharge Summaries

Human annotators and natural language applications are able to identify smoking status from discharge summaries with high accuracy when explicit evidence regarding their smoking status is present in the summary. We explore the possibility of identifying the smoking status from discharge summaries when these smoking terms have been removed. We present results using a Näıve Bayes classifier on a ...

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ژورنال

عنوان ژورنال: Artificial Intelligence in Medicine

سال: 2008

ISSN: 0933-3657

DOI: 10.1016/j.artmed.2007.10.001