Papers Medical Code Prediction
“Medical Code Prediction” 태그가 달린 논문 27편 · 필터 해제
Uncertainty-aware abstention in medical diagnosis based on medical texts
This study addresses the critical issue of reliability for AI-assisted medical diagnosis. We focus on the selection prediction approach that allows the diagnosis system to abstain from providing the decision if it is not…
Anxiety DetectionMedical Code PredictionMedical DiagnosisMortality Prediction+3An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
Electronic healthcare records are vital for patient safety as they document conditions, plans, and procedures in both free text and medical codes. Language models have significantly enhanced the processing of such record…
Adversarial RobustnessExplainable Artificial Intelligence (XAI)Feature ImportanceMedical Code PredictionEffective Medical Code Prediction via Label Internal Alignment
The clinical notes are usually typed into the system by physicians. They are typically required to be marked by standard medical codes, and each code represents a diagnosis or medical treatment procedure. Annotating thes…
Medical Code PredictionPredictionAutomated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study
Medical coding is the task of assigning medical codes to clinical free-text documentation. Healthcare professionals manually assign such codes to track patient diagnoses and treatments. Automated medical coding can consi…
Medical Code PredictionCan Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged d…
Knowledge DistillationMedical Code PredictionMedical Codes PredictionPredictionKnowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging due to a high-dimensional space of multi-…
Contrastive LearningMedical Code PredictionAutomatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings
The International Classification of Diseases (ICD) is the foundation of global health statistics and epidemiology. The ICD is designed to translate health conditions into alphanumeric codes. A number of approaches have b…
EpidemiologyMedical Code PredictionHiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding
There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of …
Medical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAn exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups
Recent studies show that neural natural processing models for medical code prediction suffer from a label imbalance issue. This study aims to investigate further imbalance in a medical code prediction dataset in terms of…
Medical Code PredictionA Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge
Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Rec…
Medical Code PredictionPredictionCode Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding
Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs). Existing methods usually apply label attention with code representations to match related text snippets. Unlike these works…
Medical Code PredictionRepresentation LearningA Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge
Medical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code prediction. Rec…
Medical Code PredictionPredictionEffective Convolutional Attention Network for Multi-label Clinical Document Classification
Multi-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 convolutional …
ClassificationDocument ClassificationMedical Code PredictionMultitask Balanced and Recalibrated Network for Medical Code Prediction
Human coders assign standardized medical codes to clinical documents generated during patients' hospitalization, which is error-prone and labor-intensive. Automated medical coding approaches have been developed using mac…
Medical Code PredictionMulti-Task LearningPredictionRead, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines
Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and exces…
Medical Code PredictionMulti-Label Classification Of Biomedical TextsSentenceParameter Selection: Why We Should Pay More Attention to It
The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleadin…
Medical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONModeling Diagnostic Label Correlation for Automatic ICD Coding
Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical …
DiagnosticMedical Code PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention
Clinical notes are unstructured text generated by clinicians during patient encounters. Clinical notes are usually accompanied by a set of metadata codes from the International Classification of Diseases(ICD). ICD code i…
Medical Code PredictionMedical DiagnosisMulti-Label Text ClassificationMultitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coder…
Medical Code PredictionPredictionRepresentation LearningDoes the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study
Unsupervised pretraining is an integral part of many natural language processing systems, and transfer learning with language models has achieved remarkable results in many downstream tasks. In the clinical application o…
Medical Code PredictionTransfer Learning