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Liang</li></ul></li><li class="EEJK9">2020</li><li class="EEJK9 XuP8p">AMIA Annual Symposium 2020</li></ul><div class="FN4MA"><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 32 32" aria-hidden="true" class="UI7HX FZFCr"><path d="M17 22L17 14 13 14 13 16 15 16 15 22 12 22 12 24 20 24 20 22 17 22zM16 8a1.5 1.5 0 101.5 1.5A1.5 1.5 0 0016 8z"></path><path d="M16,30A14,14,0,1,1,30,16,14,14,0,0,1,16,30ZM16,4A12,12,0,1,0,28,16,12,12,0,0,0,16,4Z"></path></svg><svg focusable="false" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg" fill="currentColor" width="24" height="24" viewBox="0 0 24 24" aria-hidden="true" class="UI7HX QQGoa"><path d="M14 4L12.9 5.1 18.9 11.2 2 11.2 2 12.8 18.9 12.8 12.9 18.9 14 20 22 12z"></path></svg></div></article></li></ul><nav aria-label="pagination" class="_5KtsN mbGgE"><ul class="tgnrU"><li><a aria-current="location" class="QCIlI" 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However, learning useful and generalized latent representations is a hard problem due to wide ranges of prediction outputs and vast chemical space with large heterogeneity in molecular structures. One way to learn a richer representation is by exploiting multimodality: molecules may be represented as line encodings (e.g SMILES), chemically bonded graphs, images, or 3D structures. Each representation contains potentially complementary information. In this work, we describe architectures and pre-training tasks for training a foundation model that learns from these different views of the molecule by maximizing mutual information between the modalities. By combining multiple modalities through novel pretraining methods, our model shows promising results in various molecular prediction and generation tasks, highlighting the potential of our approach in advancing the field.","linkCode":null,"source":{"__ref":"Source:454"},"sourceInstance":{"__ref":"SourceInstance:21074"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35761"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:208483"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:52810"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:78235"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35272"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Ibmer:715":{"__typename":"Ibmer","id":"715","slug":"elif-eyigoz","displayName":"Elif Eyigoz"},"Author:42664":{"__typename":"Author","id":"42664","ibmer":{"__ref":"Ibmer:715"}},"AuthorName:35078":{"__typename":"AuthorName","id":"35078","firstName":"Elif","firstNameInitials":null,"lastName":"Eyigoz","author":{"__ref":"Author:42664"}},"Author:28656":{"__typename":"Author","id":"28656","ibmer":null},"AuthorName:23240":{"__typename":"AuthorName","id":"23240","firstName":"Bc","firstNameInitials":null,"lastName":"Kwon","author":{"__ref":"Author:28656"}},"Author:42523":{"__typename":"Author","id":"42523","ibmer":null},"AuthorName:35020":{"__typename":"AuthorName","id":"35020","firstName":"Dan","firstNameInitials":null,"lastName":"Platt","author":{"__ref":"Author:42523"}},"Publication:153955":{"__typename":"Publication","id":"153955","slug":"towards-accelerating-small-molecule-drug-discovery-with-pre-trained-late-fusion-multi-view-models","title":"Towards accelerating small molecule drug discovery with pre-trained, late fusion multi-view models","type":{"__typename":"PublicationType","displayValue":"Talk"},"published":"2024-08-18","publishedMeta":{"__typename":"PublishedMeta","source":"ACS Fall 2024","year":"2024"},"abstract":"Foundation models have transformed the execution of many tasks and are an area of active exploration in small molecule drug discovery. Typically, small molecule foundation models focus on a single representation of the molecule, such as a SMILES string input into a text-based model. However, molecules may be represented in numerous ways including as images, chemically bonded graphs, or three-dimensional structures. Each representation or ‘view’ contains different, potentially complementary information that if combined can yield a more accurate and robust model. Here we describe a multi-view foundation model that incorporates several pre-trained representations to achieve this goal. Each view has already been pre-trained on hundreds of millions of molecules. Complementarity of representations in embedding space is evaluated. We explore multi-modal, late fusion techniques and fine-tune our models on datasets covering a large variety of downstream tasks. We find that our multi-view models can overall outperform models reliant on a single representation.","linkCode":null,"source":{"__ref":"Source:454"},"sourceInstance":{"__ref":"SourceInstance:21074"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35272"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:52810"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:208483"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35078"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:23240"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35020"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35761"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:391":{"__typename":"Source","id":"391","longName":"Annual Meeting of the Association for Computational Linguistics","shortName":"ACL","type":"CONFERENCE"},"SourceInstance:362":{"__typename":"SourceInstance","id":"362","name":"ACL-IJCNLP 2021"},"Author:5612":{"__typename":"Author","id":"5612","ibmer":null},"AuthorName:6064":{"__typename":"AuthorName","id":"6064","firstName":"Nancy X. 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In this paper, we address this challenge by presenting a new dataset and tasks that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.","linkCode":null,"source":{"__ref":"Source:391"},"sourceInstance":{"__ref":"SourceInstance:362"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:6064"},"affiliations":[{"__ref":"Affiliation:557"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:673"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5116"},"affiliations":[{"__ref":"Affiliation:673"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4862"},"affiliations":[{"__ref":"Affiliation:673"}]}]},"SourceInstance:1008":{"__typename":"SourceInstance","id":"1008","name":"ACL 2022"},"Author:4531":{"__typename":"Author","id":"4531","ibmer":null},"AuthorName:4987":{"__typename":"AuthorName","id":"4987","firstName":"Jennifer J.","firstNameInitials":null,"lastName":"Liang","author":{"__ref":"Author:4531"}},"Author:41576":{"__typename":"Author","id":"41576","ibmer":null},"AuthorName:34671":{"__typename":"AuthorName","id":"34671","firstName":"Eric","firstNameInitials":null,"lastName":"Lehmer","author":{"__ref":"Author:41576"}},"Affiliation:3020":{"__typename":"Affiliation","id":"3020","isIbm":false},"Author:41577":{"__typename":"Author","id":"41577","ibmer":null},"AuthorName:34672":{"__typename":"AuthorName","id":"34672","firstName":"Ananya","firstNameInitials":null,"lastName":"Iyengar","author":{"__ref":"Author:41577"}},"Author:41578":{"__typename":"Author","id":"41578","ibmer":null},"AuthorName:34673":{"__typename":"AuthorName","id":"34673","firstName":"Preethi","firstNameInitials":null,"lastName":"Raghavan","author":{"__ref":"Author:41578"}},"Author:41579":{"__typename":"Author","id":"41579","ibmer":null},"AuthorName:34674":{"__typename":"AuthorName","id":"34674","firstName":"Cindy","firstNameInitials":null,"lastName":"Chang","author":{"__ref":"Author:41579"}},"Author:41580":{"__typename":"Author","id":"41580","ibmer":null},"AuthorName:34675":{"__typename":"AuthorName","id":"34675","firstName":"Peter","firstNameInitials":null,"lastName":"Szolovits","author":{"__ref":"Author:41580"}},"Publication:15286":{"__typename":"Publication","id":"15286","slug":"towards-generalizable-methods-for-automating-risk-score-calculation","title":"Towards Generalizable Methods for Automating Risk Score Calculation","type":{"__typename":"PublicationType","displayValue":"Workshop paper"},"published":"2022-05-22","publishedMeta":{"__typename":"PublishedMeta","source":"ACL 2022","year":"2022"},"abstract":"Collecting the information necessary to calculate risk scores requires considerable time and effort. Previous studies have focused on specific risk scores and involved manual curation of relevant terms or codes and heuristics for each data element of a risk score. To support more generalizable methods for risk score calculation, here we annotate 100 patients in MIMIC-III with elements of CHA2DS2-VASc and PERC scores, and explore using question answering (QA) and off-the-shelf tools. We show that QA models can achieve comparable or better performance for certain risk score elements as compared to heuristic-based methods, and demonstrate the potential for more scalable risk score automation without the need for expert-curated heuristics. Our annotated dataset will be released to the community to encourage efforts in generalizable methods for automating risk scores.","linkCode":null,"source":{"__ref":"Source:391"},"sourceInstance":{"__ref":"SourceInstance:1008"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4987"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34671"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34672"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34673"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34674"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34675"},"affiliations":[{"__ref":"Affiliation:3020"}]}]},"SourceInstance:5454":{"__typename":"SourceInstance","id":"5454","name":"ACL 2023"},"Ibmer:2594":{"__typename":"Ibmer","id":"2594","slug":"keerthiram-murugesan","displayName":"Keerthiram Murugesan"},"Author:20760":{"__typename":"Author","id":"20760","ibmer":{"__ref":"Ibmer:2594"}},"AuthorName:16942":{"__typename":"AuthorName","id":"16942","firstName":"Keerthiram","firstNameInitials":null,"lastName":"Murugesan","author":{"__ref":"Author:20760"}},"Author:56420":{"__typename":"Author","id":"56420","ibmer":null},"AuthorName:48644":{"__typename":"AuthorName","id":"48644","firstName":"Sarathkrishna","firstNameInitials":null,"lastName":"Swaminathan","author":{"__ref":"Author:56420"}},"Author:58466":{"__typename":"Author","id":"58466","ibmer":null},"AuthorName:50692":{"__typename":"AuthorName","id":"50692","firstName":"Soham","firstNameInitials":null,"lastName":"Dan","author":{"__ref":"Author:58466"}},"Ibmer:2242":{"__typename":"Ibmer","id":"2242","slug":"subhajit-chaudhury","displayName":"Subhajit Chaudhury"},"Author:19892":{"__typename":"Author","id":"19892","ibmer":{"__ref":"Ibmer:2242"}},"AuthorName:16521":{"__typename":"AuthorName","id":"16521","firstName":"SUBHAJIT","firstNameInitials":null,"lastName":"CHAUDHURY","author":{"__ref":"Author:19892"}},"Ibmer:1607":{"__typename":"Ibmer","id":"1607","slug":"chulaka-gunasekara","displayName":"Chulaka Gunasekara"},"Author:23009":{"__typename":"Author","id":"23009","ibmer":{"__ref":"Ibmer:1607"}},"AuthorName:19202":{"__typename":"AuthorName","id":"19202","firstName":"Chulaka","firstNameInitials":null,"lastName":"Gunasekara","author":{"__ref":"Author:23009"}},"Author:47809":{"__typename":"Author","id":"47809","ibmer":null},"AuthorName:38905":{"__typename":"AuthorName","id":"38905","firstName":"Maxwell","firstNameInitials":null,"lastName":"Crouse","author":{"__ref":"Author:47809"}},"Ibmer:3828":{"__typename":"Ibmer","id":"3828","slug":"ibrahim-abdelaziz","displayName":"Ibrahim Abdelaziz"},"Author:19331":{"__typename":"Author","id":"19331","ibmer":{"__ref":"Ibmer:3828"}},"AuthorName:16393":{"__typename":"AuthorName","id":"16393","firstName":"Ibrahim","firstNameInitials":null,"lastName":"Abdelaziz","author":{"__ref":"Author:19331"}},"Author:4381":{"__typename":"Author","id":"4381","ibmer":null},"AuthorName:4838":{"__typename":"AuthorName","id":"4838","firstName":"Achille","firstNameInitials":null,"lastName":"Fokoue","author":{"__ref":"Author:4381"}},"Author:3640":{"__typename":"Author","id":"3640","ibmer":null},"AuthorName:4103":{"__typename":"AuthorName","id":"4103","firstName":"Pavan","firstNameInitials":null,"lastName":"Kapanipathi","author":{"__ref":"Author:3640"}},"Author:3620":{"__typename":"Author","id":"3620","ibmer":null},"AuthorName:4083":{"__typename":"AuthorName","id":"4083","firstName":"Salim","firstNameInitials":null,"lastName":"Roukos","author":{"__ref":"Author:3620"}},"Author:4506":{"__typename":"Author","id":"4506","ibmer":null},"AuthorName:4963":{"__typename":"AuthorName","id":"4963","firstName":"Alexander","firstNameInitials":null,"lastName":"Gray","author":{"__ref":"Author:4506"}},"Publication:21322":{"__typename":"Publication","id":"21322","slug":"mismatch-fine-grained-evaluation-of-machine-generated-text-with-mismatch-error-types","title":"MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types","type":{"__typename":"PublicationType","displayValue":"Paper"},"published":"2023-07-09","publishedMeta":{"__typename":"PublishedMeta","source":"ACL 2023","year":"2023"},"abstract":"With the growing interest in large language models, the need for evaluating the quality of machine text compared to reference (typically human-generated) text has become focal atten- tion. Most recent works focus either on task- specific evaluation metrics or study the proper- ties of machine-generated text captured by the existing metrics. In this work, we propose a new evaluation scheme to model human judg- ments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts. Inspired by the recent efforts in several NLP tasks for fine- grained evaluation, we introduce a set of 13 mis- match error types such as spatial/geographic errors, entity errors, etc, to guide the model for better prediction of human judgments. We propose a neural framework for evaluating ma- chine texts that uses these mismatch error types as auxiliary tasks and re-purposes the existing single-number evaluation metrics as additional scalar features, in addition to textual features extracted from the machine and reference texts. Our experiments reveal key insights about the existing metrics via the mismatch errors. We show that the mismatch errors between the sen- tence pairs on the held-out datasets from 7 NLP tasks align well with the human evaluation.","linkCode":null,"source":{"__ref":"Source:391"},"sourceInstance":{"__ref":"SourceInstance:5454"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16942"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:48644"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:50692"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16521"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:19202"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38905"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16393"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4838"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4103"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4083"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4963"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:20509":{"__typename":"Source","id":"20509","longName":"Nature","shortName":"Nature","type":"JOURNAL"},"Ibmer:1872":{"__typename":"Ibmer","id":"1872","slug":"ching-huei-tsou","displayName":"Ching-Huei 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L.","firstNameInitials":"S.L.","lastName":"Remy","author":{"__ref":"Author:343"}},"Author:45762":{"__typename":"Author","id":"45762","ibmer":null},"AuthorName:36853":{"__typename":"AuthorName","id":"36853","firstName":"Oliver","firstNameInitials":null,"lastName":"Bent","author":{"__ref":"Author:45762"}},"Author:47763":{"__typename":"Author","id":"47763","ibmer":null},"AuthorName:38859":{"__typename":"AuthorName","id":"38859","firstName":"Pooja","firstNameInitials":null,"lastName":"Guhan","author":{"__ref":"Author:47763"}},"Author:44675":{"__typename":"Author","id":"44675","ibmer":null},"AuthorName:35764":{"__typename":"AuthorName","id":"35764","firstName":"Shilpa","firstNameInitials":null,"lastName":"Mahatma","author":{"__ref":"Author:44675"}},"Author:3698":{"__typename":"Author","id":"3698","ibmer":null},"AuthorName:4159":{"__typename":"AuthorName","id":"4159","firstName":"Aisha","firstNameInitials":null,"lastName":"Walcott-Bryant","author":{"__ref":"Author:3698"}},"Author:47764":{"__typename":"Author","id":"47764","ibmer":null},"AuthorName:38860":{"__typename":"AuthorName","id":"38860","firstName":"Divya","firstNameInitials":null,"lastName":"Pathak","author":{"__ref":"Author:47764"}},"Ibmer:28":{"__typename":"Ibmer","id":"28","slug":"michal-rosen-zvi","displayName":"Michal Rosen-Zvi"},"Author:4474":{"__typename":"Author","id":"4474","ibmer":{"__ref":"Ibmer:28"}},"AuthorName:4931":{"__typename":"AuthorName","id":"4931","firstName":"Michal","firstNameInitials":"M.","lastName":"Rosen-Zvi","author":{"__ref":"Author:4474"}},"Publication:17768":{"__typename":"Publication","id":"17768","slug":"ai-assisted-tracking-of-worldwide-non-pharmaceutical-interventions-for-covid-19","title":"AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19","type":{"__typename":"PublicationType","displayValue":"Paper"},"published":"2021-03-25","publishedMeta":{"__typename":"PublishedMeta","source":"Nature","year":"2021"},"abstract":"The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.","linkCode":null,"source":{"__ref":"Source:20509"},"sourceInstance":null,"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35761"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:24932"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27378"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:27377"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35762"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38851"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:16528"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:36854"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38852"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38853"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38854"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4158"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4153"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38855"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38856"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34709"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38857"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38858"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:3960"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:36853"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38859"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:35764"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4159"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:38860"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4931"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Source:474":{"__typename":"Source","id":"474","longName":"American Medical Informatics Association (AMIA) Annual Symposium","shortName":"AMIA Annual Symposium","type":"CONFERENCE"},"SourceInstance:5966":{"__typename":"SourceInstance","id":"5966","name":"AMIA Annual Symposium 2023"},"Publication:22784":{"__typename":"Publication","id":"22784","slug":"capturing-individual-level-social-determinants-from-clinical-text","title":"Capturing Individual-level Social Determinants from Clinical Text","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2023-11-11","publishedMeta":{"__typename":"PublishedMeta","source":"AMIA Annual Symposium 2023","year":"2023"},"abstract":"Knowledge of social determinants of health (SDOH), which refer to nonmedical factors influencing health outcomes, can help providers improve patient care. However, SDOH are often documented in unstructured notes, making them more inaccessible. Although previous works have attempted SDOH extraction from clinical notes, most efforts defined SDOH more narrowly and focused on the note’s social history (SH) section, where social factors are traditionally documented. Here, we introduce a new SDOH dataset covering a broad range of SDOH content that is annotated over entire notes. We characterize what, where, and how SDOH information is documented in clinical text, present baseline systems using a token classification and generative approach, and investigate whether training only on the SH section can effectively extract SDOH from the entire note. The final dataset, consisting of 2,007 annotations covering 7 open-ended SDOH domains over 500 notes, will be publicly released to encourage further research in this area.","linkCode":null,"source":{"__ref":"Source:474"},"sourceInstance":{"__ref":"SourceInstance:5966"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4987"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:34672"},"affiliations":[{"__ref":"Affiliation:3020"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:24932"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"SourceInstance:373":{"__typename":"SourceInstance","id":"373","name":"AMIA Annual Symposium 2021"},"Publication:10098":{"__typename":"Publication","id":"10098","slug":"toward-understanding-clinical-context-of-medication-change-events-in-clinical-narratives","title":"Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2021-10-29","publishedMeta":{"__typename":"PublishedMeta","source":"AMIA Annual Symposium 2021","year":"2021"},"abstract":"Understanding medication events in clinical narratives is essential to achieving a complete picture of a patient's medication history. While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation. Here, we present a framework to capture multi-dimensional context of medication changes documented in clinical notes. We define specific contextual aspects pertinent to medication change events (i.e. Action, Negation, Temporality, Certainty, and Actor), describe the annotation process and challenges encountered while creating the dataset, and explore models based on state-of-the-art transformers to automate the task. The resulting dataset, Contextualized Medication Event Dataset (CMED), consisting of 9,013 medications annotated over 500 clinical notes, will be released to the community as a shared task in 2021-2022.","linkCode":null,"source":{"__ref":"Source:474"},"sourceInstance":{"__ref":"SourceInstance:373"},"authors":[{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:5127"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:4987"},"affiliations":[{"__ref":"Affiliation:73"}]},{"__typename":"PublicationHasAuthorName","authorName":{"__ref":"AuthorName:24932"},"affiliations":[{"__ref":"Affiliation:73"}]}]},"Author:33517":{"__typename":"Author","id":"33517","ibmer":null},"AuthorName:27379":{"__typename":"AuthorName","id":"27379","firstName":"Venkata Naga Sreeram","firstNameInitials":null,"lastName":"Joopudi","author":{"__ref":"Author:33517"}},"Author:33518":{"__typename":"Author","id":"33518","ibmer":null},"AuthorName:27380":{"__typename":"AuthorName","id":"27380","firstName":"John","firstNameInitials":null,"lastName":"Prager","author":{"__ref":"Author:33518"}},"Author:33519":{"__typename":"Author","id":"33519","ibmer":null},"AuthorName:27381":{"__typename":"AuthorName","id":"27381","firstName":"Preethi","firstNameInitials":null,"lastName":"Raghavan","author":{"__ref":"Author:33519"}},"Ibmer:169":{"__typename":"Ibmer","id":"169","slug":"michele-payne","displayName":"Michele Payne"},"Author:33520":{"__typename":"Author","id":"33520","ibmer":{"__ref":"Ibmer:169"}},"AuthorName:27382":{"__typename":"AuthorName","id":"27382","firstName":"Michele","firstNameInitials":null,"lastName":"Payne (Cestone)","author":{"__ref":"Author:33520"}},"Publication:10953":{"__typename":"Publication","id":"10953","slug":"reducing-physicians-cognitive-load-during-chart-review-a-problem-oriented-summary-of-the-patient-electronic-record","title":"Reducing Physicians’ Cognitive Load During Chart Review: A Problem-Oriented Summary of the Patient Electronic Record","type":{"__typename":"PublicationType","displayValue":"Conference paper"},"published":"2021-10-29","publishedMeta":{"__typename":"PublishedMeta","source":"AMIA Annual Symposium 2021","year":"2021"},"abstract":"Overabundance of information within electronic health records (EHRs) has resulted in a need for automated systems to mitigate the cognitive burden on physicians utilizing today’s EHR systems. We present ProSPER, a Problem-oriented Summary of the Patient Electronic Record that displays a patient summary centered around an autogenerated problem list and disease-specific views for chronic conditions. ProSPER was developed using 1,500 longitudinal patient records from two large multi-specialty medical groups in the United States, and leverages multiple natural language processing (NLP) components targeting various fundamental (e.g. syntactic analysis), clinical (e.g. adverse drug event extraction) and summarizing (e.g. problem list generation) tasks. We report evaluation results for each component and discuss how specific components address existing physician challenges in reviewing EHR data. 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