MIMIC III
Related Papers
Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs
AbstractFew/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It …...
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification
AbstractLarge-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with …...
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
AbstractLarge Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity …...
Effective Convolutional Attention Network for Multi-label Clinical Document Classification
AbstractMulti-label document classification (MLDC) problems can be challenging, especially for long documents with a large label set and a long-tail distribution over labels. In this paper, we present an effective …...
On the Impact of Random Seeds on the Fairness of Clinical Classifiers
AbstractRecent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s). We explore the implications of this phenomenon for model fairness across demographic groups in …...
Predicting in-hospital mortality by combining clinical notes with time-series data
In intensive care units (ICUs), patient health is monitored through (1) continuous vital signals from various medical devices, and (2) clinical notes consisting of opinions and summaries from doctors which …...
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports
AbstractThis paper proposes a new cross-document coreference resolution (CDCR) dataset for identifying co-referring radiological findings and medical devices across a patient’s radiology reports. Our annotated corpus contains 5872 mentions (findings …...
A New Public Corpus for Clinical Section Identification: MedSecId
AbstractThe process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing …...
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning
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 …...
MDACE: MIMIC Documents Annotated with Code Evidence
AbstractWe introduce a dataset for evidence/rationale extraction on an extreme multi-label classification task over long medical documents. One such task is Computer-Assisted Coding (CAC) which has improved significantly in recent …...
Modelling Temporal Document Sequences for Clinical ICD Coding
AbstractPast studies on the ICD coding problem focus on predicting clinical codes primarily based on the discharge summary. This covers only a small fraction of the notes generated during each …...