66 citations · 178 across the 13 of their papers we have counts for
15 papers
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning
Brihat Sharma, Yanjun Gao, Timothy Miller +3
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medica…
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes
Yanjun Gao, Dmitriy Dligach, Timothy Miller +2
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research ef…
Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy
Shan Chen, Marco Guevara, Nicolas Ramirez +7
Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but…
Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task
Yanjun Gao, Dmitriy Dligach, Timothy Miller +3
Daily progress notes are common types in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designe…
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
Yanjun Gao, Dmitriy Dligach, Timothy Miller +4
The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priori…
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding
Yanjun Gao, Dmitriy Dligach, Timothy Miller +4
Applying methods in natural language processing on electronic health records (EHR) data is a growing field. Existing corpus and annotation focus on modeling textual features and re…
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing
Yanjun Gao, Dmitriy Dligach, Leslie Christensen +7
Objective: to provide a scoping review of papers on clinical natural language processing (NLP) tasks that use publicly available electronic health record data from a cohort of pati…
Classifying Long Clinical Documents with Pre-trained Transformers
Xin Su, Timothy Miller, Xiyu Ding +2
Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria. Phenotyping typically involves classifying long clinical documents that…
Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
Dianbo Liu, Tim Miller
Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years. However, in certain area like healthcar…
Two-stage Federated Phenotyping and Patient Representation Learning
Dianbo Liu, Dmitriy Dligach, Timothy Miller
A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely tim…
Simplified Neural Unsupervised Domain Adaptation
Timothy A Miller
Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target d…
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
Dianbo Liu, Timothy Miller, Raheel Sayeed +1
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to se…
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
Lifeng Jin, Finale Doshi-Velez, Timothy Miller +2
There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model (Ponvert et al., 2011; Noji…
Learning Patient Representations from Text
Dmitriy Dligach, Timothy Miller
Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as…
Unsupervised Grammar Induction with Depth-bounded PCFG
Lifeng Jin, Finale Doshi-Velez, Timothy Miller +2
There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 201…