AIMC Topic: Data Collection

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Prescriptive Method for Optimizing Cost of Data Collection and Annotation in Machine Learning of Clinical Ultrasound.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
UNLABELLED: Machine learning in medical ultrasound faces a major challenge: the prohibitive costs of producing and annotating clinical data. Optimizing the data collection and annotation will improve model training efficiency, reducing project cost a...

The Inverse Data Law: Market Imperatives, Data, and Quality in AI Supported Care.

Studies in health technology and informatics
Over the last decade, the explosion of "Big Data" and its fusion with AI has led many to believe that the development and integration of AI systems in healthcare will usher in a transformative revolution that democratises access to high quality healt...

Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies.

The Lancet. Digital health
Integration of patient-reported outcome measures (PROMs) in artificial intelligence (AI) studies is a critical part of the humanisation of AI for health. It allows AI technologies to incorporate patients' own views of their symptoms and predict outco...

Artificial Intelligence: its Future and Impact on Acute Medicine.

Acute medicine
This commentary explores the potential impact of artificial intelligence (AI) in acute medicine, considering its possibilities and challenges. With its ability to simulate human intelligence, AI holds the promise for supporting timely decision-making...

An analysis of the effects of limited training data in distributed learning scenarios for brain age prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Distributed learning avoids problems associated with central data collection by training models locally at each site. This can be achieved by federated learning (FL) aggregating multiple models that were trained in parallel or training a s...

Cautious Artificial Intelligence Improves Outcomes and Trust by Flagging Outlier Cases.

JCO clinical cancer informatics
PURPOSE: Artificial intelligence (AI) models for medical image diagnosis are often trained and validated on curated data. However, in a clinical setting, images that are outliers with respect to the training data, such as those representing rare dise...

A scoping review of publicly available language tasks in clinical natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients.

STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.

Briefings in bioinformatics
Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of d...

Performance Evaluation of Embedded Image Classification Models Using Edge Impulse for Application on Medical Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This work explores the possibility of applying edge machine learning technology in the context of portable medical image diagnostic systems. This was done by evaluating the performance of two machine learning (ML) algorithms, that are widely used on ...

A Time-Series Augmentation Method Based on Empirical Mode Decomposition and Integrated LSTM Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Adequate patients' data have always been critical for disease assessment. However, large amounts of patient data are often difficult to collect, especially when patients are required to complete a series of assessment movements. For example, assessin...