projects:workgroups:wg_meeting_sep_08_2021
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projects:workgroups:wg_meeting_sep_08_2021 [2021/10/05 14:06] vipina created |
projects:workgroups:wg_meeting_sep_08_2021 [2021/10/05 14:07] vipina [Invited talk] |
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| ==== Invited talk ==== | ==== Invited talk ==== | ||
| **Title: Leveraging longitudinal and multi-modal EHR in Survival Analysis**\\ | **Title: Leveraging longitudinal and multi-modal EHR in Survival Analysis**\\ | ||
| - | **Abstract:** Survival analysis is a fundamental statistical tool that predicts the time of an event. It has multiple healthcare applications in areas, such as hospitalization and patient mortality. Clinicians predict patient outcomes by heterogeneous modalities (e.g., text, images, and lab values). Such data poses significant challenges for traditional survival analysis techniques. In this talk, we present our effort to expand the survival analysis using multimodal, longitudinal EHR data. Our results indicate that extracted high-dimensional features from text and image provide complementary information in addition to structured EHR, and incorporating longitudinal data is useful in time-to-event prediction. | + | **Abstract:** Rapid growth in adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets. Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice. In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents. Meanwhile, a number of clinical NLP systems, such as MedLEE, MetaMap/MetaMap Lite, cTAKES, MedTagger, and i2b2 have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports. This talk will walk through some successful applications of NLP techniques in the clinical domain with potential opportunities and challenges. |
| **Presenter:** Dr. Yifan Peng\\ | **Presenter:** Dr. Yifan Peng\\ | ||
| Dr. Peng is an assistant professor at the Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis, such as named entity recognition, information extraction, and eye disease diagnosis and prognosis. Before joining Cornell Medicine, Dr. Peng was a research fellow at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical relation extraction, with a focus on deep analysis of the linguistic structures of biomedical texts. | Dr. Peng is an assistant professor at the Department of Population Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis, such as named entity recognition, information extraction, and eye disease diagnosis and prognosis. Before joining Cornell Medicine, Dr. Peng was a research fellow at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical relation extraction, with a focus on deep analysis of the linguistic structures of biomedical texts. | ||
projects/workgroups/wg_meeting_sep_08_2021.txt ยท Last modified: 2021/11/09 04:48 by vipina
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