The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
Yingjun Guan - One of the best experts on this subject based on the ideXlab platform.
-
Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.
Journal of Biomedical Informatics, 2019Co-Authors: Catherine Blake, Yingjun GuanAbstract:Abstract Objectives Treatment used in a randomized clinical trial is a Critical Data Element both for physicians at the point of care and reviewers who are evaluating different interventions. Much of existing work on treatment extraction from the biomedical literature has focused on the extraction of pharmacological interventions. However, non-pharmacological interventions (e.g., exercise, diet, etc.) that are frequently used to address chronic conditions are less well studied. The goal of this study is to compare knowledge-based and machine learning strategies for the extraction of both drug and non-drug treatments. Methods We collected 800 randomized clinical trial abstracts each for breast cancer and diabetes from PubMed. The treatments in the result/conclusion sentences of the abstracts were manually annotated and marked as drug/non-drug treatments. We then designed three methods to identify the treatments and evaluated the systems with respect to drug/non-drug treatments. The first method is solely based on knowledge base (here we used MetaMap). The second method is based on a machine learning model trained mainly on contextual features (ML_only). The third method is a combination approach that integrates the previous two approaches. Results/discussion Results show that MetaMap, when used with high precision semantic types, has better performance for drug compared to non-drug treatments (F1 = 0.77 vs. 0.64). The ML_only approach has smaller performance difference between drug and non-drug treatments compared with the KB-based approach (F1 = 0.02 vs. 0.05, 0.07, and 0.13). The combination approach achieves significantly better performance than all MetaMap approaches alone for total treatments (F1 = 0.76 vs. 0.72, p Conclusion These results suggest that a knowledge-based approach should be employed for medical conditions that are primarily treated with drugs whereas conditions that are treated with either a combination of drug and non-drug interventions or primarily non-drug interventions should use automated tools that combine machine learning and a knowledge-based approach to achieve optimal performance.
Steven J. Jacobsen - One of the best experts on this subject based on the ideXlab platform.
-
Variability in date of prostate cancer diagnosis: a comparison of cancer registry, pathology report, and electronic health Data sources
Annals of Epidemiology, 2014Co-Authors: Kimberly R. Porter, Chun Chao, Virginia P. Quinn, Steven J. JacobsenAbstract:Abstract Purpose The date of cancer diagnosis is a Critical Data Element for clinical care and research. Because this date can be abstracted from various Data sources, its comparability from source to source is unclear. This study compared the date of diagnosis from multiple sources within the same population of prostate cancer patients. Methods We linked cancer registry, pathology report, and electronic health Data sources from the Kaiser Permanente Southern California health Data systems for a cohort of 22,666 members diagnosed with prostate cancer between 2000 and 2010. The magnitude and direction of the differences in date of diagnosis were assessed for each date pairwise comparison. We reviewed 454 medical records to determine reasons for date discrepancies. Results Among the date pairwise comparisons, differences in date of diagnosis spanned from 9.6 years earlier to 10 years later than each other. However, the overall median difference ranged from 1 to 16 days, thus suggesting that the vast majority of the date differences were small. Chart review results identified major categories of date discrepancies. Conclusions These Data demonstrate variability in date of diagnosis across these Data sources. This variability may have implications for epidemiologic estimates or patient identification in research studies using different Data sources.
Catherine Blake - One of the best experts on this subject based on the ideXlab platform.
-
Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.
Journal of Biomedical Informatics, 2019Co-Authors: Catherine Blake, Yingjun GuanAbstract:Abstract Objectives Treatment used in a randomized clinical trial is a Critical Data Element both for physicians at the point of care and reviewers who are evaluating different interventions. Much of existing work on treatment extraction from the biomedical literature has focused on the extraction of pharmacological interventions. However, non-pharmacological interventions (e.g., exercise, diet, etc.) that are frequently used to address chronic conditions are less well studied. The goal of this study is to compare knowledge-based and machine learning strategies for the extraction of both drug and non-drug treatments. Methods We collected 800 randomized clinical trial abstracts each for breast cancer and diabetes from PubMed. The treatments in the result/conclusion sentences of the abstracts were manually annotated and marked as drug/non-drug treatments. We then designed three methods to identify the treatments and evaluated the systems with respect to drug/non-drug treatments. The first method is solely based on knowledge base (here we used MetaMap). The second method is based on a machine learning model trained mainly on contextual features (ML_only). The third method is a combination approach that integrates the previous two approaches. Results/discussion Results show that MetaMap, when used with high precision semantic types, has better performance for drug compared to non-drug treatments (F1 = 0.77 vs. 0.64). The ML_only approach has smaller performance difference between drug and non-drug treatments compared with the KB-based approach (F1 = 0.02 vs. 0.05, 0.07, and 0.13). The combination approach achieves significantly better performance than all MetaMap approaches alone for total treatments (F1 = 0.76 vs. 0.72, p Conclusion These results suggest that a knowledge-based approach should be employed for medical conditions that are primarily treated with drugs whereas conditions that are treated with either a combination of drug and non-drug interventions or primarily non-drug interventions should use automated tools that combine machine learning and a knowledge-based approach to achieve optimal performance.
Kimberly R. Porter - One of the best experts on this subject based on the ideXlab platform.
-
Variability in date of prostate cancer diagnosis: a comparison of cancer registry, pathology report, and electronic health Data sources
Annals of Epidemiology, 2014Co-Authors: Kimberly R. Porter, Chun Chao, Virginia P. Quinn, Steven J. JacobsenAbstract:Abstract Purpose The date of cancer diagnosis is a Critical Data Element for clinical care and research. Because this date can be abstracted from various Data sources, its comparability from source to source is unclear. This study compared the date of diagnosis from multiple sources within the same population of prostate cancer patients. Methods We linked cancer registry, pathology report, and electronic health Data sources from the Kaiser Permanente Southern California health Data systems for a cohort of 22,666 members diagnosed with prostate cancer between 2000 and 2010. The magnitude and direction of the differences in date of diagnosis were assessed for each date pairwise comparison. We reviewed 454 medical records to determine reasons for date discrepancies. Results Among the date pairwise comparisons, differences in date of diagnosis spanned from 9.6 years earlier to 10 years later than each other. However, the overall median difference ranged from 1 to 16 days, thus suggesting that the vast majority of the date differences were small. Chart review results identified major categories of date discrepancies. Conclusions These Data demonstrate variability in date of diagnosis across these Data sources. This variability may have implications for epidemiologic estimates or patient identification in research studies using different Data sources.
Chun Chao - One of the best experts on this subject based on the ideXlab platform.
-
Variability in date of prostate cancer diagnosis: a comparison of cancer registry, pathology report, and electronic health Data sources
Annals of Epidemiology, 2014Co-Authors: Kimberly R. Porter, Chun Chao, Virginia P. Quinn, Steven J. JacobsenAbstract:Abstract Purpose The date of cancer diagnosis is a Critical Data Element for clinical care and research. Because this date can be abstracted from various Data sources, its comparability from source to source is unclear. This study compared the date of diagnosis from multiple sources within the same population of prostate cancer patients. Methods We linked cancer registry, pathology report, and electronic health Data sources from the Kaiser Permanente Southern California health Data systems for a cohort of 22,666 members diagnosed with prostate cancer between 2000 and 2010. The magnitude and direction of the differences in date of diagnosis were assessed for each date pairwise comparison. We reviewed 454 medical records to determine reasons for date discrepancies. Results Among the date pairwise comparisons, differences in date of diagnosis spanned from 9.6 years earlier to 10 years later than each other. However, the overall median difference ranged from 1 to 16 days, thus suggesting that the vast majority of the date differences were small. Chart review results identified major categories of date discrepancies. Conclusions These Data demonstrate variability in date of diagnosis across these Data sources. This variability may have implications for epidemiologic estimates or patient identification in research studies using different Data sources.