AIMC Topic: Machine Learning

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The use of artificial intelligence for automating or semi-automating biomedical literature analyses: A scoping review.

Journal of biomedical informatics
OBJECTIVE: Evidence-based medicine (EBM) is a decision-making process based on the conscious and judicious use of the best available scientific evidence. However, the exponential increase in the amount of information currently available likely exceed...

Artificial intelligence and laboratory data in rheumatic diseases.

Clinica chimica acta; international journal of clinical chemistry
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping...

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Contrast media & molecular imaging
Rice () is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recogniz...

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review.

European radiology
OBJECTIVES: Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver c...

Exploring the influence of nasal vestibule structure on nasal obstruction using CFD and Machine Learning method.

Medical engineering & physics
Motivated by clinical findings about the nasal vestibule, this study analyzes the aerodynamic characteristics of the nasal vestibule and attempt to determine anatomical features which have a large influence on airflow through a combination of Computa...

Animal disease surveillance: How to represent textual data for classifying epidemiological information.

Preventive veterinary medicine
The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the early warning and preparedness context is recognized. This study evaluates machine learning methods for...

Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning.

Journal of cancer research and clinical oncology
PURPOSE: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, ho...

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives.

British journal of haematology
Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods...

EpiTEAmDNA: Sequence feature representation via transfer learning and ensemble learning for identifying multiple DNA epigenetic modification types across species.

Computers in biology and medicine
Methylation is a major DNA epigenetic modification for regulating the biological processes without altering the DNA sequence, and multiple types of DNA methylations have been discovered, including 6mA, 5hmC, and 4mC. Multiple computational approaches...

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Journal of chemical information and modeling
Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs th...