AIMC Topic: Machine Learning

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Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

International journal of environmental research and public health
Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared ...

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

International journal of environmental research and public health
This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) Thi...

Generalizability of heterogeneous treatment effects based on causal forests applied to two randomized clinical trials of intensive glycemic control.

Annals of epidemiology
Purpose Machine learning is an attractive tool for identifying heterogeneous treatment effects (HTE) of interventions but generalizability of machine learning derived HTE remains unclear. We examined generalizability of HTE detected using causal fore...

Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics.

Bioresource technology
This study aimed to utilize machine learning algorithems combined with feature reduction for predicting pyrolytic gas yield and compositions based on pyrolysis conditions and biomass characteristics. To this end, random forest (RF) and support vector...

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations].

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particul...

Development of a smartphone-based lateral-flow imaging system using machine-learning classifiers for detection of Salmonella spp.

Journal of microbiological methods
Salmonella spp. are a foodborne pathogen frequently found in raw meat, egg products, and milk. Salmonella is responsible for numerous outbreaks, becoming a frequent major public-health concern. Many studies have recently reported handheld and rapid d...

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge.

Journal of computer-aided molecular design
Accurate prediction of lipophilicity-logP-based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps...

Recognition of honeycomb lung in CT images based on improved MobileNet model.

Medical physics
PURPOSE: The research is to improve the efficiency and accuracy of recognition of honeycomb lung in CT images.

Recent advances in drug repurposing using machine learning.

Current opinion in chemical biology
Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlig...

Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium.

International journal of legal medicine
Reducing the subjectivity of the methods used for biological profile estimation is, at present, a priority research line in forensic anthropology. To achieve this, artificial intelligence (AI) techniques can be a valuable tool yet to be exploited in ...