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projects:workgroups:nlp-wg [2023/05/10 01:30]
vipina [Past WG meetings (Agenda/Minutes/Recordings)]
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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 **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 - March 8, 2023+**Monthly Meeting:** Upcoming - May 9, 2023
  
 **Agenda** **Agenda**
  
- 1) Presentation - Dr. Daniel G. Smith, Emory University\\ + 1) Presentation - **Nic Dobbins** (Principal Solutions Architect at UW Medicine Research IT; PhD Candidate in biomedical informatics at the University ​of Washington)\\ 
-**Abstract:​** ​Implementing an NLP process into production ​is an often challenging task, with considerable requirements for accuracy prior to being relied upon in a clinical ​workflow. However, ​including other local members ​of the clinical and research  community in the process at each stage of model selection ​and development could provide considerable advantagesAt Winship Cancer Institute, we’re starting to bring NLP to our research abstractorsas well as our tumor registrarsso that they can provide critical feedback on dictionary curation, NER label verification,​ and final accuracy of the models ​in development. We seek feedback from the NLP working group regarding our model of the current processas well as discuss your experiences in bringing ​clinical and research staff into the NLP process.\\+**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 ontologiesTo enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate ​our systemcalled LeafAIwe 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.\\
  
 2) Updates on the progress of ongoing studies 2) Updates on the progress of ongoing studies
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   - Psychiatry   - Psychiatry
   - Oncology   - Oncology
-3) NLP book chapter ​and other OKRs+3) NLP book chapter
  
  
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   * Psychiatry - NLP for capturing administration of neuropsychiatric scales and their scores   * 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   * 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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 ==== Upcoming Meeting Dates (2023) ==== ==== Upcoming Meeting Dates (2023) ====
  
-  * March 8 
-  * April 12 
-  * May 10 
   * June 14   * June 14
   * July 12   * July 12
projects/workgroups/nlp-wg.1683682248.txt.gz · Last modified: 2023/05/10 01:30 by vipina