Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system
<p>Abstract</p> <p>Background</p> <p>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.<...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-07-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Online Access: | http://www.biomedcentral.com/1472-6947/6/30 |
| _version_ | 1811319844759404544 |
|---|---|
| author | Sordo Margarita Weiss Scott Goryachev Sergey Zeng Qing T Murphy Shawn N Lazarus Ross |
| author_facet | Sordo Margarita Weiss Scott Goryachev Sergey Zeng Qing T Murphy Shawn N Lazarus Ross |
| author_sort | Sordo Margarita |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.</p> |
| first_indexed | 2024-04-13T12:50:27Z |
| format | Article |
| id | doaj.art-a9ff2298177240d290173f2eb25c2318 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-13T12:50:27Z |
| publishDate | 2006-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-a9ff2298177240d290173f2eb25c23182022-12-22T02:46:15ZengBMCBMC Medical Informatics and Decision Making1472-69472006-07-01613010.1186/1472-6947-6-30Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing systemSordo MargaritaWeiss ScottGoryachev SergeyZeng Qing TMurphy Shawn NLazarus Ross<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.</p>http://www.biomedcentral.com/1472-6947/6/30 |
| spellingShingle | Sordo Margarita Weiss Scott Goryachev Sergey Zeng Qing T Murphy Shawn N Lazarus Ross Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system BMC Medical Informatics and Decision Making |
| title | Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system |
| title_full | Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system |
| title_fullStr | Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system |
| title_full_unstemmed | Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system |
| title_short | Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system |
| title_sort | extracting principal diagnosis co morbidity and smoking status for asthma research evaluation of a natural language processing system |
| url | http://www.biomedcentral.com/1472-6947/6/30 |
| work_keys_str_mv | AT sordomargarita extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem AT weissscott extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem AT goryachevsergey extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem AT zengqingt extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem AT murphyshawnn extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem AT lazarusross extractingprincipaldiagnosiscomorbidityandsmokingstatusforasthmaresearchevaluationofanaturallanguageprocessingsystem |