projects:workgroups:nlp-wg
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projects:workgroups:nlp-wg [2022/08/04 18:44] vipina [OHDSI NLP WG Monthly Meeting] |
projects:workgroups:nlp-wg [2023/05/10 01:37] (current) vipina [Ongoing Projects] |
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| The primary goal of the NLP working group is to promote the use of textual information from Electronic Health Records (EHRs) for observational studies under the OHDSI umbrella. To facilitate this objective, the group will develop methods and software that can be implemented to utilize clinical text for studies by the OHDSI community. | The primary goal of the NLP working group is to promote the use of textual information from Electronic Health Records (EHRs) for observational studies under the OHDSI umbrella. To facilitate this objective, the group will develop methods and software that can be implemented to utilize clinical text for studies by the OHDSI community. | ||
| - | ==== Project Lead ==== | + | ==== Workgroup Lead ==== |
| [[https://www.ohdsi.org/who-we-are/collaborators/hua-xu/|Hua Xu]]\\ | [[https://www.ohdsi.org/who-we-are/collaborators/hua-xu/|Hua Xu]]\\ | ||
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| ==== Project Coordinator ==== | ==== Project Coordinator ==== | ||
| - | [[Vipina.KuttichiKeloth@uth.tmc.edu | Vipina K Keloth ]] | + | [[vipina.kuttichikeloth@yale.edu | Vipina K. Keloth ]] |
| ==== OHDSI NLP WG Monthly Meeting ==== | ==== OHDSI NLP WG Monthly Meeting ==== | ||
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| **Where:** [[https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Acd9841fec6df4f3d8eb6a6bf49ea305f%40thread.tacv2%2F1610663053273%3Fcontext%3D%257b%2522Tid%2522%253a%2522a30f0094-9120-4aab-ba4c-e5509023b2d5%2522%252c%2522Oid%2522%253a%252200626e72-b11c-482a-9dc4-d8eff51c5e5f%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=42431bac-788d-4a7b-8531-5eb2612224a6&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true|Click here to join the meeting]] | **Where:** [[https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2Fmeetup-join%2F19%3Acd9841fec6df4f3d8eb6a6bf49ea305f%40thread.tacv2%2F1610663053273%3Fcontext%3D%257b%2522Tid%2522%253a%2522a30f0094-9120-4aab-ba4c-e5509023b2d5%2522%252c%2522Oid%2522%253a%252200626e72-b11c-482a-9dc4-d8eff51c5e5f%2522%257d%26anon%3Dtrue&type=meetup-join&deeplinkId=42431bac-788d-4a7b-8531-5eb2612224a6&directDl=true&msLaunch=true&enableMobilePage=true&suppressPrompt=true|Click here to join the meeting]] | ||
| - | **Monthly Meeting:** Upcoming - August 10, 2022 | + | **Monthly Meeting:** Upcoming - May 9, 2023 |
| **Agenda** | **Agenda** | ||
| - | - Note_NLP proposal revision | + | 1) Presentation - **Nic Dobbins** (Principal Solutions Architect at UW Medicine Research IT; PhD Candidate in biomedical informatics at the University of Washington)\\ |
| - | - Call for participation - Mayo clinic | + | **Title:** LeafAI: query generator for clinical cohort discovery rivaling a human programmer\\ |
| + | **Abstract:** Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. LeafAI matched a mean 43% of enrolled patients with 27,225 eligible across 8 clinical trials, compared to 27% matched and 14,587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Finally, we introduce a novel multimodal user interface for interaction with LeafAI.\\ | ||
| - | **Project Abstract**: Delirium is a syndrome with symptoms that present as confusion and is characterized by an acute change in mental status, fluctuating course, lack of attention, and disorganized thinking or altered level of consciousness. Delirium is routinely underdiagnosed, particularly mild cases, and electronic billing codes incompletely capture this condition. In addition, incomplete identification of delirium can substantially hamper clinical research efforts that use large databases to identify risk factors for and outcomes of delirium. Although billing codes in administrative datasets may incompletely identify delirium cases, clinical notes frequently contain details that are relevant to a delirium diagnosis. Therefore, we have developed a natural language processing (NLP) algorithm to identify delirium episodes from electronic health record (EHR) clinical notes based on the confusion assessment method (CAM) framework for identifying episodes. To characterize changes in delirium over time, we aim to apply this algorithm to different institutions, and compare the cases identified via the algorithm to cases identified using international classification of diseases (ICD) billing codes. We will also examine differences in rates of delirium over time and by age, sex, race, and ethnicity using these methods. | + | 2) Updates on the progress of ongoing studies |
| + | - SDoH | ||
| + | - Psychiatry | ||
| + | - Oncology | ||
| + | 3) NLP book chapter | ||
| - | Original NLP study: https://academic.oup.com/biomedgerontology/article/77/3/524/5943765 | ||
| - | |||
| - | **Presented by**: Dr. Sunyang Fu, Dr. Jennifer St. Sauver | ||
| - | |||
| - | **Bio**: Sunyang Fu is an incoming Research Associate at Mayo Clinic Department of AI and Informatics Research. The overarching goal of his research is to accelerate, improve and govern the secondary use of Electronic Health Records (EHRs) for clinical and translational research towards high throughput, reproducible, fair, and trustworthy discoveries. He has extensive informatics research experience in (1) assessing EHR data quality and heterogeneity, (2) developing natural language processing (NLP) techniques for clinical information extraction, and (3) designing and developing informatics frameworks for clinical research workflow optimization. He also has extensive collaborative research experience in clinical and translational science, epidemiology, outcome research, standards, and precision medicine. | ||
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| - | Dr. Jennifer St. Sauver is the associate scientific director of the Population Health Science Program at Mayo Clinic's Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery. She is also co-principal investigator of the Rochester Epidemiology Project, a National Institutes of Health-funded research infrastructure that collates and indexes health care information from medical care available to residents of a 27-county region of southeast Minnesota and southwest Wisconsin. These data have been used by investigators throughout the United States, resulting in nearly 3,000 publications on a wide range of health care topics. | ||
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| ==== Ongoing Projects ==== | ==== Ongoing Projects ==== | ||
| - | * Clinical Abbreviations | ||
| - | * Post-acute sequelae of SARS-CoV-2 infection (PASC) study | ||
| - | * Extraction, Transformation, and Load Process (ETL) | ||
| * Note type normalization | * Note type normalization | ||
| - | * Open source Python NLP package | + | * Social Determinants of Health |
| + | * Psychiatry - NLP for capturing administration of neuropsychiatric scales and their scores | ||
| + | * Oncology - NLP for getting oncology data using Tumor Reg data as a gold standard for assessing the information obtained through the NLP algorithm | ||
| + | * Book of OHDSI NLP Chapter | ||
| ==== Past Projects ==== | ==== Past Projects ==== | ||
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| A noncomprehensive list of participants: [[ Click here ]] | A noncomprehensive list of participants: [[ Click here ]] | ||
| - | ==== Upcoming Meeting Dates (2022) ==== | + | ==== Upcoming Meeting Dates (2023) ==== |
| - | * June 8 | + | * June 14 |
| - | * July 13 | + | * July 12 |
| - | * August 10 | + | * August 9 |
| - | * September 14 | + | * September 13 |
| - | * October 12 | + | * October 11 |
| - | * November 9 | + | * November 8 |
| - | * December 14 | + | * December 13 |
| ==== Repository ==== | ==== Repository ==== | ||
| Line 77: | Line 73: | ||
| ==== Past WG meetings (Agenda/Minutes/Recordings) ==== | ==== Past WG meetings (Agenda/Minutes/Recordings) ==== | ||
| + | **2023**\\ | ||
| + | -[[WG_meeting_may_10_2023]] | ||
| + | -[[WG_meeting_apr_12_2023]] | ||
| + | -[[WG_meeting_mar_08_2023]] | ||
| + | -[[WG_meeting_feb_08_2023]] | ||
| + | -[[WG_meeting_jan_11_2023]] | ||
| + | |||
| **2022**\\ | **2022**\\ | ||
| + | -[[WG_meeting_dec_14_2022]] | ||
| + | -[[WG_meeting_nov_09_2022]] | ||
| + | -[[WG_meeting_sep_14_2022]] | ||
| + | -[[WG_meeting_aug_10_2022]] | ||
| -[[WG_meeting_jun_08_2022]] | -[[WG_meeting_jun_08_2022]] | ||
| -[[WG_meeting_may_11_2022]] | -[[WG_meeting_may_11_2022]] | ||
projects/workgroups/nlp-wg.1659638688.txt.gz · Last modified: 2022/08/04 18:44 by vipina
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