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--- projects:workgroups:nlp-wg [2023/03/03 20:38] – [Project Coordinator] vipina
+++ projects:workgroups:nlp-wg [2023/05/10 01:37] (current) – [Ongoing Projects] vipina
@@ Line 5: / Line 5: @@
 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]]\\
@@ Line 20: / Line 20: @@
 **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 - December 14, 2022
+**Monthly Meeting:** Upcoming - May 9, 2023
 **Agenda**
- - Invited talk: Dr. Yanjun Gao, University of Wisconsin
+) Presentation - **Nic Dobbins** (Principal Solutions Architect at UW Medicine Research IT; PhD Candidate in biomedical informatics at the University of Washington)\\
+**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.\\
+) Updates on the progress of ongoing studies
+  - SDoH
+  - Psychiatry
+  - Oncology
+) NLP book chapter
-**Title: Hierarchical Annotation for Building a Suite of Clinical Natural Language Processing Tasks**
-**Abstract:** Applying methods in natural language processing on electronic health records (EHR) data has attracted rising interests. Existing corpus and annotation focus on modeling textual features and relation prediction. However, there are a paucity of annotated corpus built to model clinical diagnostic thinking, a processing involving text understanding, domain knowledge abstraction and reasoning. In this talk, I will introduce a hierarchical annotation schema with three tasks to address clinical text understanding, clinical reasoning and summarization. We create an annotated corpus based on a large collection of publicly available daily progress notes, a type of EHR that is time-sensitive, problem-oriented, and well-documented by the format of Subjective, Objective, Assessment and Plan (SOAP). I will present the experiment results of applying state-of-the-art language models to this new suite. I will also talk about how this new suite of tasks could perform the paradigm shift of clinical NLP from information extraction and outcome prediction to diagnostic reasoning, and ultimately to effective clinical decision support systems for physicians at the bedside care.
-**Presenter:**\\
-Dr. Yanjun Gao is a postdoc research associate in the Critical Care Medicine (ICU) Data Science Lab in the Division of Allergy, Pulmonary and Critical Care Medicine within the Department of Medicine. She serves on the organizing committee of 2022 National NLP Clinical Challenges (N2C2), and Graph-based Natural Language Processing Workshop (TextGraphs). She has publications across major NLP and AI conferences including ACL, COLING, CoNLL, and reviews for several NLP and clinical informatics conferences and journals. Her current focus is developing NLP models for diagnostic reasoning using clinical text and medical knowledge.
 ==== Ongoing Projects ====
-  * Clinical Abbreviations
-  * Post-acute sequelae of SARS-CoV-2 infection (PASC) study
-  * Extraction, Transformation, and Load Process (ETL)
   * 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 ====
@@ Line 49: / Line 53: @@
 A noncomprehensive list of participants: [[ Click here ]]
-==== Upcoming Meeting Dates (2022) ====
+==== Upcoming Meeting Dates (2023) ====
-  * December 14
+  * June 14
+  * July 12
+  * August 9
+  * September 13
+  * October 11
+  * November 8
+  * December 13
 ==== Repository ====
@@ Line 63: / Line 73: @@
 ==== 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**\\
   -[[WG_meeting_dec_14_2022]]