AIMC Topic: Machine Learning

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Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations.

Journal of medical Internet research
BACKGROUND: The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting signif...

Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning.

Physics in medicine and biology
This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual ...

[Artificial intelligence and machine learning].

Handchirurgie, Mikrochirurgie, plastische Chirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Mikrochirurgie der Peripheren Nerven und Gefasse : Organ der V...
Machine learning (ML) is the ability of computers to learn from data without being programmed explicitly for that purpose, and to apply the acquired knowledge to unknown cases. The application of ML in medicine will increase exponentially in the year...

Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas.

PLoS computational biology
Based on morphology it is often challenging to distinguish between the many different soft tissue sarcoma subtypes. Moreover, outcome of disease is highly variable even between patients with the same disease. Machine learning on transcriptome sequenc...

Software Development Effort Estimation Using Regression Fuzzy Models.

Computational intelligence and neuroscience
Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingl...

Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients.

PloS one
Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuv...

Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

European journal of radiology
PURPOSE: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.

Journal of cellular and molecular medicine
Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially meth...

AntiVPP 1.0: A portable tool for prediction of antiviral peptides.

Computers in biology and medicine
Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to discover and design new drug candidates. Antiviral peptides are molecules with confirmed activity a...