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material ID: electronichealthrecords

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Key Concept Identification for Medical Information Retrieval

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

The difficult language in Electronic Health Records (EHRs) presents a challenge to patients’ understanding of their own con- ditions. One approach to lowering the barrier is to provide tailored patient …...

34785

Coherence-based Modeling of Clinical Concepts Inferred from Heterogeneous Clinical Notes for ICU Patient Risk Stratification

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractIn hospitals, critical care patients are often susceptible to various complications that adversely affect their morbidity and mortality. Digitized patient data from Electronic Health Records (EHRs) can be utilized to …...

41658

Leveraging Medical Literature for Section Prediction in Electronic Health Records

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractElectronic Health Records (EHRs) contain both structured content and unstructured (text) content about a patient’s medical history. In the unstructured text parts, there are common sections such as Assessment and …...

9029

A Semi-supervised Approach for De-identification of Swedish Clinical Text

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAn abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done …...

14437

Biomedical Concept Relatedness – A large EHR-based benchmark

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractA promising application of AI to healthcare is the retrieval of information from electronic health records (EHRs), e.g. to aid clinicians in finding relevant information for a consultation or to …...

3791

Extracting Adherence Information from Electronic Health Records

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractPatient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, …...

3741

On the Contribution of Per-ICD Attention Mechanisms to Classify Health Records in Languages with Fewer Resources than English

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractWe introduce a multi-label text classifier with per-label attention for the classification of Electronic Health Records according to the International Classification of Diseases. We apply the model on two Electronic …...

20348

DialMed: A Dataset for Dialogue-based Medication Recommendation

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMedication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some details of interactions between doctors and patients may …...

5152

That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recent multimodal models induce soft local alignments …...

31133

TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the …...

4948

Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractLeveraging knowledge from electronic health records (EHRs) to predict a patient’s condition is essential to the effective delivery of appropriate care. Clinical notes of patient EHRs contain valuable information from …...

38564