AIMC Topic: Algorithms

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Predictive Risk Models for Wound Infection-Related Hospitalization or ED Visits in Home Health Care Using Machine-Learning Algorithms.

Advances in skin & wound care
OBJECTIVE: Wound infection is prevalent in home healthcare (HHC) and often leads to hospitalizations. However, none of the previous studies of wounds in HHC have used data from clinical notes. Therefore, the authors created a more accurate descriptio...

Thirteen Questions About Using Machine Learning in Causal Research (You Won't Believe the Answer to Number 10!).

American journal of epidemiology
Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspeci...

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.

Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.

The Journal of chemical physics
Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulation...

Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.

Neural computation
Supervised learning corresponds to minimizing a loss or cost function expressing the differences between model predictions yn and the target values tn given by the training data. In neural networks, this means backpropagating error signals through th...

Randomized Self-Organizing Map.

Neural computation
We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess rando...

[Application of neural network autoencoder algorithm in the cancer informatics research].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Cancers have been widely recognized as highly heterogeneous diseases, and early diagnosis and prognosis of cancer types have become the focus of cancer research. In the era of big data, efficient mining of massive biomedical data has become a grand c...