AIMC Topic: Algorithms

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Extraction from Medical Records.

Studies in health technology and informatics
Despite using electronic medical records, free narrative text is still widely used for medical records. Such text cannot be analyzed by statistical tools and be proceed by decision support systems. To make data from texts available for such tasks a s...

Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model.

Combinatorial chemistry & high throughput screening
AIM AND OBJECTIVE: Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategi...

Atherosclerotic carotid plaque on panoramic radiographs: neural network detection.

International journal of computerized dentistry
AIM: Atherosclerotic carotid plaques (ACPs) constitute the main etiological factor in about 15% of strokes. ACPs can be detected on routine dental panoramic radiographs. As these are one of the most commonly performed dental images, they can be used ...

Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?

Studies in health technology and informatics
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms ...

Ensemble Based Approach for Time Series Classification in Metabolomics.

Studies in health technology and informatics
BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare.

Evaluation of Chatbot Prototypes for Taking the Virtual Patient's History.

Studies in health technology and informatics
In medical education Virtual Patients (VP) are often applied to train students in different scenarios such as recording the patient's medical history or deciding a treatment option. Usually, such interactions are predefined by software logic and data...

Supervised Machine Learning with CITRUS for Single Cell Biomarker Discovery.

Methods in molecular biology (Clifton, N.J.)
CITRUS is a supervised machine learning algorithm designed to analyze single cell data, identify cell populations, and identify changes in the frequencies or functional marker expression patterns of those populations that are significantly associated...

Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population.

The journal of prevention of Alzheimer's disease
BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R and D pipeline includes around 112 agents. I...

Three-Dimensional Classification Structure-Activity Relationship Analysis Using Convolutional Neural Network.

Chemical & pharmaceutical bulletin
Quantitative structure-activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related rese...

A hierarchically trained generative network for robust facial symmetrization.

Technology and health care : official journal of the European Society for Engineering and Medicine
Face symmetrization has extensive applications in both medical and academic fields, such as facial disorder diagnosis. Human face possesses an important characteristic, which is as known as symmetry. However, in many scenarios, the perfect symmetry d...