AIMC Topic: Neural Networks, Computer

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Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines.

Journal of animal science
The identification of different meat cuts for labeling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for not only error but also bacterial cross-contaminati...

Applying and improving AlphaFold at CASP14.

Proteins
We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the "human" category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is ent...

Are rib fractures stable? An analysis of progressive rib fracture offset in the acute trauma setting.

The journal of trauma and acute care surgery
BACKGROUND: Rib fractures serve as both a marker of injury severity and a guide for clinical decision making for trauma patients. Although recent studies have suggested that rib fractures are dynamic, the degree of progressive offset remains unknown....

Classification of white blood cells using weighted optimized deformable convolutional neural networks.

Artificial cells, nanomedicine, and biotechnology
BACKGROUND: Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of gold standard data sets, and even the imple...

Applying Intelligent Algorithms to Automate the Identification of Error Factors.

Journal of patient safety
OBJECTIVES: Medical errors are the manifestation of the defects occurring in medical processes. Extracting and identifying defects as medical error factors from these processes are an effective approach to prevent medical errors. However, it is a dif...

Transferability of neural network clinical deidentification systems.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Neural network deidentification studies have focused on individual datasets. These studies assume the availability of a sufficient amount of human-annotated data to train models that can generalize to corresponding test data. In real-world...

Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem o...

Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases.

Studies in health technology and informatics
Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited ti...

Encoding Health Records into Pathway Representations for Deep Learning.

Studies in health technology and informatics
There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient's data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways fro...