AIMC Topic: Machine Learning

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Pivotal challenges in artificial intelligence and machine learning applications for neonatal care.

Seminars in fetal & neonatal medicine
Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available f...

Supervised machine learning and associated algorithms: applications in orthopedic surgery.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Supervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of...

Applications of machine learning techniques for enhancing nondestructive food quality and safety detection.

Critical reviews in food science and nutrition
In considering the need of people all over the world for high-quality food, there has been a recent increase in interest in the role of nondestructive and rapid detection technologies in the food industry. Moreover, the analysis of data acquired by m...

Environmental toxicity risk evaluation of nitroaromatic compounds: Machine learning driven binary/multiple classification and design of safe alternatives.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association
Nitroaromatic compounds (NACs) represent a significant source of organic pollutants in the environment. In this study, a well-rounded dataset containing 371 NACs with rat oral median lethal doses (LD) was developed. Based on the dataset, binary and m...

Comparing the applications of machine learning, PBPK, and population pharmacokinetic models in pharmacokinetic drug-drug interaction prediction.

CPT: pharmacometrics & systems pharmacology
The gold-standard approach for modeling pharmacokinetic mediated drug-drug interactions is the use of physiologically-based pharmacokinetic modeling and population pharmacokinetics. However, these models require extensive amounts of drug-specific dat...

Turnaround time prediction for clinical chemistry samples using machine learning.

Clinical chemistry and laboratory medicine
OBJECTIVES: Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare...

Evaluation of Empirical and Machine Learning Approaches for Estimating Monthly Reference Evapotranspiration with Limited Meteorological Data in the Jialing River Basin, China.

International journal of environmental research and public health
The accurate estimation of reference evapotranspiration () is crucial for water resource management and crop water requirements. This study aims to develop an efficient and accurate model to estimate the monthly in the Jialing River Basin, China. Fo...

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms.

Computational intelligence and neuroscience
In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient ene...

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach.

Computational intelligence and neuroscience
Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals...

Data driven identification of international cutting edge science and technologies using SpaCy.

PloS one
Difficulties in collecting, processing, and identifying massive data have slowed research on cutting-edge science and technology hotspots. Promoting these technologies will not be successful without an effective data-driven method to identify cutting...