International Statistical Classification of Diseases and Related Health Problems
PulseAugur coverage of International Statistical Classification of Diseases and Related Health Problems — every cluster mentioning International Statistical Classification of Diseases and Related Health Problems across labs, papers, and developer communities, ranked by signal.
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New framework enhances ICU risk prediction using ontology and cross-modal learning
Researchers have developed OC-Distill, a novel two-stage framework designed for improved ICU risk prediction using machine learning. The first stage employs an ontology-aware contrastive objective that leverages the ICD…
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New LLM method improves disease classification mapping accuracy
Researchers have developed a new method for mapping disease classification systems, addressing the challenge of one-to-many relationships between codes. This approach, inspired by entity resolution pipelines, uses a blo…
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LLMs outperform specialized ML for hospital ICD coding
Hospitals are increasingly using AI for diagnostic classification, particularly for the ICD coding system. A prototype test revealed that a large language model (LLM) outperformed a specialized machine learning model in…
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Clinical NLP datasets shape suicidality detection, study finds
A new paper argues that the way clinical text datasets are constructed significantly influences the accuracy and interpretation of suicidality detection in Natural Language Processing (NLP). The research highlights that…
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New EHR models leverage ICD code hierarchy for improved predictions
Researchers have developed new methods for electronic health record (EHR) foundation models to better utilize the hierarchical structure of ICD diagnosis codes. Current models treat these codes as flat tokens, ignoring …
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Study: Post-training boosts LLMs for medical coding
A new study explores the effectiveness of post-training techniques for large language models (LLMs) in the domain of International Classification of Diseases (ICD) coding. The research indicates that while LLMs may perf…
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Graph Learning Model Enhances Early Detection of Inflammatory Bowel Disease
Researchers have developed GraD-IBD, a novel graph-based model for early detection of Inflammatory Bowel Disease (IBD). This model represents patient diagnosis trajectories as temporally directed graphs, overcoming limi…
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LLMs automate psychiatric diagnosis classification with 86.6% accuracy
Researchers have developed an automated system to classify psychiatric diagnoses using Natural Language Processing (NLP) and Machine Learning (ML). The study evaluated various text representation methods, including clas…
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Multi-version training boosts rare ICD code prediction accuracy
Researchers have developed a multi-version training approach to improve the accuracy of automated clinical coding, particularly for rare medical codes. By incorporating data from different versions of the International …
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LLMs trained with Span-Centric Learning improve ICD coding accuracy and efficiency
Researchers have developed a new training framework called Span-Centric Learning (SCL) to improve the accuracy of Large Language Models (LLMs) in assigning International Classification of Diseases (ICD) codes to clinica…