Biomedical subjects

Shawn N Murphy

Publications and source records attributed to Shawn N Murphy.

3 recordsLinked to original sources

PubMed2006

Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system.

BACKGROUND: The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease. METHODS: The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard. RESULTS: The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded. CONCLUSION: We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.

Asthma↗

PubMed2003

A visual interface designed for novice users to find research patient cohorts in a large biomedical database.

One of the more difficult tasks of informatics is allowing for the navigation of complex databases. At Partners Healthcare Inc. we have developed an analytical database to allow for searching clinical data to obtain cohorts of patients for research studies. The characteristics of the patients within the cohorts must often comply with complex inclusion and exclusion criteria. The users of the database are research clinicians, often with no prior database experience. To assist these clinicians in finding their patient cohorts, we constructed a Querytool that they use directly to find their desired populations. In order to understand if the Querytool could indeed be used successfully by novice users, we analyzed the first 10 queries of 219 users. This analysis was able show that novice users are able to achieve excellent success using the Querytool

Computer Graphics↗

PubMed2002

A security architecture for query tools used to access large biomedical databases.

Disseminating information from large biomedical databases can be crucial for research. Often this data will be patient-specific, and therefore require that the privacy of the patient be protected. In response to this requirement, HIPAA released regulations for the dissemination of patient data. In many cases, the regulations are so restrictive as to render data useless for many purposes. We propose in this paper a model for obfuscation of data when served to a client application, that will make it extremely unlikely that an individual will be identified. At Partners Healthcare Inc, with over 1.4 million patients and 400 research clinician users, we implemented this model. Based on the results, we believe that a web-client could be made generally available using the proposed data obfuscation scheme that could allow general usage of large biomedical databases of patient information without risk to patient privacy.

Computer Security↗