AIMC Topic: Data Curation

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Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.

International journal of medical informatics
OBJECTIVE: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.

Influence of Data Curation and Confidence Levels on Compound Predictions Using Machine Learning Models.

Journal of chemical information and modeling
While data curation principles and practices are a major topic in data science, they are often not explicitly considered in machine learning (ML) applications in chemistry. We have been interested in evaluating the potential effects of data curation ...

Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study.

Laboratory investigation; a journal of technical methods and pathology
Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated...

Annotation of epilepsy clinic letters for natural language processing.

Journal of biomedical semantics
BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the d...

SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information.

ACS synthetic biology
The progress and utility of synthetic biology is currently hindered by the lengthy process of studying literature and replicating poorly documented work. Reconstruction of crucial design information through post hoc curation is highly noisy and error...

Facilitating the use of routine data to evaluate artificial intelligence solutions: lessons from the NIHR/RCR data curation workshop.

Clinical radiology
Radiology currently stands at the forefront of artificial intelligence (AI) development and deployment over many other medical subspecialities within the scope of both research and clinical practice. Given this current leadership position, it is impe...

Pseudo-class part prototype networks for interpretable breast cancer classification.

Scientific reports
Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key...

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

BMC medical imaging
The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, a...

On-the-fly point annotation for fast medical video labeling.

International journal of computer assisted radiology and surgery
PURPOSE: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and time-consuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two co...

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Journal of imaging informatics in medicine
In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tack...