Extracting Adherence Information from Electronic Health Records (2020.coling-main)
Copied to clipboard
Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, Kirk Roberts
| Challenge: | adherence is a critical factor in health outcomes, and is often modeled as a binary decision . adherence models include intentional and unintentional non-adherence, social support and other patient attributes such as age and time since diagnosis. |
| Approach: | They propose to extract adherence information from electronic health records using de-identified sentences and a corpus of 3,000 de-identified sentences. |
| Outcome: | The proposed framework extracts medication adherence information from electronic health records. |
Similar Papers
Extracting relevant information from physician-patient dialogues for automated clinical note taking (D19-62)
Copied to clipboard
| Challenge: | a system that extracts pertinent medical information from dialogues between clinicians and patients is proposed . entering data into EMRs is currently slow and error-prone, and clinicians spend up to 50% of their time on data entry. |
| Approach: | They propose a system that automatically extracts medical information from dialogues between clinicians and patients using context and time information. |
| Outcome: | The proposed system extracts medical information from dialogues and automatically generates a patient note. |
Towards Extracting Medical Family History from Natural Language Interactions: A New Dataset and Baselines (D19-1)
Copied to clipboard
| Challenge: | Using dialog agents, we can collect family history data from in-person consultations and crowdsource it to a genetic counselor. |
| Approach: | They propose to use natural language interactions annotated with medical family histories to collect information from a genetic counselor and crowdsourcing. |
| Outcome: | The proposed system averages 0.87 on complex sentences on the targeted relations. |
MIE: A Medical Information Extractor towards Medical Dialogues (2020.acl-main)
Copied to clipboard
| Challenge: | EMRs are important but many doctors suffer from writing them, which is time-consuming and tedious. |
| Approach: | They propose an automatic conversion of medical dialogues to EMRs using a window-sliding style . they propose a medical information extractor (MIE) that extracts medical information from medical dialogue . |
| Outcome: | The proposed model extracts medical information from doctor-patient dialogues. |
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
Copied to clipboard
| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022.emnlp-main)
Copied to clipboard
Yuan Xia, Zhenhui Shi, Jingbo Zhou, Jiayu Xu, Chao Lu, Yehui Yang, Lei Wang, Haifeng Huang, Xia Zhang, Junwei Liu
| Challenge: | With the development of medical digitization, the extraction and structuring of electronic medical records (EMRs) have become challenging but fundamental tasks. |
| Approach: | They propose a speaker-aware dialogue encoder with multi-task learning which takes the speaker's identity into account and a co-attention fusion network to aggregate the utterance information. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the public medical dialogue extraction datasets to demonstrate its superiority. |
Leveraging Medical Literature for Section Prediction in Electronic Health Records (D19-1)
Copied to clipboard
| Challenge: | Prior approaches to section prediction have only used text data from EHRs and required significant manual annotation. |
| Approach: | They propose to use sections from medical literature to train models to predict sections in EHRs. |
| Outcome: | The proposed model uses sections from medical literature that contain similar content to those found in EHR sections. |
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (D19-1)
Copied to clipboard
| Challenge: | Recent studies have shown that structured domain knowledge can be used for textual inference tasks in the medical domain. |
| Approach: | They propose to integrate structured domain knowledge into a knowledge graph for the Medical NLI task. |
| Outcome: | The proposed approach improves the baseline BioELMo architecture for the Medical NLI task. |
Incorporating medical knowledge in BERT for clinical relation extraction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models (PLMs) are used for diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question/Answering. |
| Approach: | They propose to add medical knowledge to pre-trained language models to facilitate clinical relation extraction using a large text corpus. |
| Outcome: | The proposed model outperforms the state-of-the-art systems on the benchmark i2b2/VA 2010 clinical relation extraction dataset. |
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing (2020.lrec-1)
Copied to clipboard
| Challenge: | Clinical trials require that patients meet eligibility criteria to ensure safety and effectiveness of studies. |
| Approach: | They propose a dataset that includes the first-of-its-kind eligibility-criteria corpus and queries for criteria-to-sql . they propose 'neuro semantic parser' which can translate eligibility criteria to executable SQL queries . |
| Outcome: | The proposed parser outperforms existing state-of-the-art general-purpose models while highlighting the challenges presented by the new dataset. |
MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets lacking comprehensive annotations for medical history-taking are non-English . existing datasets lack comprehensive annotation for medical slots and their attributes . |
| Approach: | They propose a dataset of doctor-patient dialogues in English for medical history-taking task. |
| Outcome: | The proposed datasets are available in English and are compared with existing datasets. |