AI Medical Compendium Topic

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Databases, Factual

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Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.

Sensors (Basel, Switzerland)
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbea...

Large language models can accurately populate Vascular Quality Initiative procedural databases using narrative operative reports.

Journal of vascular surgery
OBJECTIVE: Participation in the Vascular Quality Initiative (VQI) provides important resources to surgeons, but the ability to do so is often limited by time and data entry personnel. Large language models (LLMs) such as ChatGPT (OpenAI) are examples...

Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal of cardiothoracic and vascular anesthesia
BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict...

The critical need for an open medical imaging database in Japan: implications for global health and AI development.

Japanese journal of radiology
Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, cont...

Where, why, and how is bias learned in medical image analysis models? A study of bias encoding within convolutional networks using synthetic data.

EBioMedicine
BACKGROUND: Understanding the mechanisms of algorithmic bias is highly challenging due to the complexity and uncertainty of how various unknown sources of bias impact deep learning models trained with medical images. This study aims to bridge this kn...

Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.

JCO clinical cancer informatics
PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial fo...

Generative Biomedical Event Extraction With Constrained Decoding Strategy.

IEEE/ACM transactions on computational biology and bioinformatics
Currently, biomedical event extraction has received considerable attention in various fields, including natural language processing, bioinformatics, and computational biomedicine. This has led to the emergence of numerous machine learning and deep le...

Relation Extraction in Biomedical Texts: A Cross-Sentence Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, relationships ...

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.

IEEE/ACM transactions on computational biology and bioinformatics
Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scien...