AIMC Topic: Machine Learning

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BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN.

Sensors (Basel, Switzerland)
Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that...

A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm.

Sensors (Basel, Switzerland)
The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart p...

An intelligent medical guidance and recommendation model driven by patient-physician communication data.

Frontiers in public health
Based on the online patient-physician communication data, this study used natural language processing and machine learning algorithm to construct a medical intelligent guidance and recommendation model. First, based on 16,935 patient main complaint d...

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database.

PloS one
INTRODUCTION: Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance.

Early recognition of risk of critical adverse events based on deep neural decision gradient boosting.

Frontiers in public health
INTRODUCTION: Perioperative critical events will affect the quality of medical services and threaten the safety of patients. Using scientific methods to evaluate the perioperative risk of critical illness is of great significance for improving the qu...

Diagnostic quality model (DQM): an integrated framework for the assessment of diagnostic quality when using AI/ML.

Clinical chemistry and laboratory medicine
BACKGROUND: Laboratory medicine has reached the era where promises of artificial intelligence and machine learning (AI/ML) seem palpable. Currently, the primary responsibility for risk-benefit assessment in clinical practice resides with the medical ...

Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data.

Computers in biology and medicine
Microscopic analysis of breast cancer images is the primary task in diagnosing cancer malignancy. Recent attempts to automate this task have employed deep learning models whose success has depended on large volumes of data, while acquiring annotated ...

Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.

Journal of proteome research
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states ...

Deep learning based identification of bone scintigraphies containing metastatic bone disease foci.

Cancer imaging : the official publication of the International Cancer Imaging Society
PURPOSE: Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often ...

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.

Computational intelligence and neuroscience
. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion ...