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Identifying and Reporting Dependent Adult abuse

Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.

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Showing 358-378 of 1,915 articles
Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.

Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. ...

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment.

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed...

Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control.

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In re...

Machine learning approach to identify adverse events in scientific biomedical literature.

Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in d...

Automatic recognition of whole-spine sagittal alignment and curvature analysis through a deep learning technique.

PURPOSE: Artificial intelligence based on deep learning (DL) approaches enables the automatic recogn...

Deep Learning-Based Indoor Localization Using Multi-View BLE Signal.

In this paper, we present a novel Deep Neural Network-based indoor localization method that estimate...

Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization.

Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an "ave...

A deep learning-based system for real-time image reporting during esophagogastroduodenoscopy: a multicenter study.

BACKGROUND AND STUDY AIMS: Endoscopic reports are essential for the diagnosis and follow-up of gastr...

Technical note: A PET/MR coil with an integrated, orbiting 511 keV transmission source for PET/MR imaging validated in an animal study.

BACKGROUND: MR-based methods for attenuation correction (AC) in PET/MRI either neglect attenuation o...

A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies.

Gleason grading, a risk stratification method for prostate cancer, is subjective and dependent on ex...

A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction.

Morphological attributes from histopathological images and molecular profiles from genomic data are ...

A machine learning-based on-demand sweat glucose reporting platform.

Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and alteri...

Deep learning models for triaging hospital head MRI examinations.

The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global short...

The BMS-LM ontology for biomedical data reporting throughout the lifecycle of a research study: From data model to ontology.

Biomedical research data reuse and sharing is essential for fostering research progress. To this aim...

Quality in MR reporting of the prostate – improving acquisition, the role of AI and future perspectives.

The high quality of MRI reporting of the prostate is the most critical component of the service prov...

Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting.

BACKGROUND: There has been a considerable increase in total joint arthroplasty (TJA) research using ...

Learning to Compose and Reason with Language Tree Structures for Visual Grounding.

Grounding natural language in images, such as localizing "the black dog on the left of the tree", is...

Ambiguous and Incomplete: Natural Language Processing Reveals Problematic Reporting Styles in Thyroid Ultrasound Reports.

OBJECTIVE: Natural language processing (NLP) systems convert unstructured text into analyzable data....

What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms.

In this paper, we examine the qualitative moral impact of machine learning-based clinical decision s...

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