AIMC Topic: Algorithms

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An intentional approach to managing bias in general purpose embedding models.

The Lancet. Digital health
Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical image...

Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences
OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specifi...

High Accuracy Open-Source Clinical Data De-Identification: The CliniDeID Solution.

Studies in health technology and informatics
Clinical data de-identification offers patient data privacy protection and eases reuse of clinical data. As an open-source solution to de-identify unstructured clinical text with high accuracy, CliniDeID applies an ensemble method combining deep and ...

Explainable Artificial Intelligence for Deep-Learning Based Classification of Cystic Fibrosis Lung Changes in MRI.

Studies in health technology and informatics
Algorithms increasing the transparence and explain ability of neural networks are gaining more popularity. Applying them to custom neural network architectures and complex medical problems remains challenging. In this work, several algorithms such as...

Elucidating Discrepancy in Explanations of Predictive Models Developed Using EMR.

Studies in health technology and informatics
The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these meth...

Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach.

Studies in health technology and informatics
Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much informatio...

A Five-Step Workflow to Manually Annotate Unstructured Data into Training Dataset for Natural Language Processing.

Studies in health technology and informatics
Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there i...

Deep Learning for Midfacial Fracture Detection in CT Images.

Studies in health technology and informatics
This study deploys the deep learning-based object detection algorithms to detect midfacial fractures in computed tomography (CT) images. The object detection models were created using faster R-CNN and RetinaNet from 2,000 CT images. The best detectio...

Temporomandibular Joint Disorders Multi-Class Classification Using Deep Learning.

Studies in health technology and informatics
Temporomandibular joint (TMJ) disorders have been misinterpreted by various normal TMJ features leading to treatment failure. This study assessed deep learning algorithms, DenseNet-121 and InceptionV3, for multi-class classification of TMJ normal var...