AIMC Topic: Machine Learning

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Comparison of machine learning models to predict complications of bariatric surgery: A systematic review.

Health informatics journal
Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machi...

Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia: Evidence from 2016 EDHS.

Health informatics journal
: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictiv...

Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients.

Cancer biomarkers : section A of Disease markers
BACKGROUND: Chronic atrophy gastritis (CAG) is a high-risk pre-cancerous lesion for gastric cancer (GC). The early and accurate detection and discrimination of CAG from benign forms of gastritis (e.g. chronic superficial gastritis, CSG) is critical f...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. A comprehensive search was conducted across PubMed, Web of...

Machine learning approaches for asthma disease prediction among adults in Sri Lanka.

Health informatics journal
Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Data from 6,665 participants in the...

[Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma].

Vestnik oftalmologii
The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring a...

Trusting AI made decisions in healthcare by making them explainable.

Science progress
OBJECTIVES: In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The aim of this paper is t...

Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

Water science and technology : a journal of the International Association on Water Pollution Research
The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have l...

Machine learning approaches for adequate prediction of flow resistance in alluvial channels with bedforms.

Water science and technology : a journal of the International Association on Water Pollution Research
In natural rivers, flow conditions are mainly dependent on flow resistance and type of roughness. The interactions among flow and bedforms are complex in nature as bedform dynamics primarily regulate the flow resistance. Manning's equation is the mos...

Development of Drug Discovery Platforms Using Artificial Intelligence and Cheminformatics.

Chemical & pharmaceutical bulletin
Recently, remarkable progress has been achieved in artificial intelligence (AI), including machine learning. Various AI models have been proposed for drug discovery, including the design of small molecules, activity prediction, and three-dimensional ...