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Found 16 papers, 8 papers with code
Comparing representations of long clinical texts for the task of patient note-identification
In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corresponding patient, represented by a set of related notes.
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
Clinical diagnostic guidelines outline the key questions to answer to reach a diagnosis.
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia
In practice, clinicians achieve a diagnosis by following a sequence of steps, such as laboratory exams, observations, or imaging.
Enhancing Clinical Data Warehouses with Provenance and Large File Management: The gitOmmix Approach for Clinical Omics Data
We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data.
Facilitating phenotyping from clinical texts: the medkit library
In addition to the core of the library, we share the operations and pipelines we already developed and invite the phenotyping community for their reuse and enrichment.
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
We illustrate with our two use cases their advantages: they generate step-by-step pathways that are self-explanatory; and their correctness is competitive when compared to state-of-the-art approaches.
Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning
Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis.
Investigating ADR mechanisms with knowledge graph mining and explainable AI
We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or not for a given type of ADR.
Experiments on transfer learning architectures for biomedical relation extraction
Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts.
Discovering alignment relations with Graph Convolutional Networks: a biomedical case study
In this article, we propose to match nodes within a knowledge graph by (i) learning node embeddings with Graph Convolutional Networks such that similar nodes have low distances in the embedding space, and (ii) clustering nodes based on their embeddings, in order to suggest alignment relations between nodes of a same cluster.
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking.
Knowledge-Based Matching of $n$-ary Tuples
In particular, units should be matched within and across sources, and their level of relatedness should be classified into equivalent, more specific, or similar.
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies.
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences).
