AIMC Topic: Machine Learning

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A machine learning approach identified a diagnostic model for pancreatic cancer through using circulating microRNA signatures.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
Late diagnosis of pancreatic cancer (PC) due to the limited effectiveness of modern testing approaches, causes many patients to miss the chance of surgery and consequently leads to a high mortality rate. Pivotal improvements in circulating microRNA e...

A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels.

Journal of biomedical informatics
PURPOSE: To present a Machine Learning pipeline for automatically relabeling anatomical structure sets in the Digital Imaging and Communications in Medicine (DICOM) format to a standard nomenclature that will enable data abstraction for research and ...

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.

Biochimica et biophysica acta. Molecular basis of disease
Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and ...

cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.

Proteins
Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurat...

Anatomical classification of upper gastrointestinal organs under various image capture conditions using AlexNet.

Computers in biology and medicine
BACKGROUND: Machine learning has led to several endoscopic studies about the automated localization of digestive lesions and prediction of cancer invasion depth. Training and validation dataset collection are required for a disease in each digestive ...

Reliability and accuracy of EEG interpretation for estimating age in preterm infants.

Annals of clinical and translational neurology
OBJECTIVES: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.

AutoAudio: Deep Learning for Automatic Audiogram Interpretation.

Journal of medical systems
Hearing loss is the leading human sensory system loss, and one of the leading causes for years lived with disability with significant effects on quality of life, social isolation, and overall health. Coupled with a forecast of increased hearing loss ...

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Nature communications
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accurac...

Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

Journal of medical systems
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this resear...

A machine learning and network framework to discover new indications for small molecules.

PLoS computational biology
Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or ex...