AI Medical Compendium Topic

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Malaria

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Predicting malaria epidemics in Burkina Faso with machine learning.

PloS one
Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations ...

A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate and early diagnosis is critical to proper malaria treatment and hence death prevention. Several computer vision technologies have emerged in recent years as alternatives to traditional microscopy and rapid diagnostic tests. In this work, we ...

Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.

International journal of environmental health research
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test opt...

On the Efficiency of Machine Learning Models in Malaria Prediction.

Studies in health technology and informatics
Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support a...

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso.

Parasites & vectors
BACKGROUND: Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the...

Multi-stage malaria parasite recognition by deep learning.

GigaScience
MOTIVATION: Malaria, a mosquito-borne infectious disease affecting humans and other animals, is widespread in tropical and subtropical regions. Microscopy is the most common method for diagnosing the malaria parasite from stained blood smear samples....

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks.

Computers in biology and medicine
Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the reco...

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study.

BMJ open
OBJECTIVES: Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to i...

The relationship between rising temperatures and malaria incidence in Hainan, China, from 1984 to 2010: a longitudinal cohort study.

The Lancet. Planetary health
BACKGROUND: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and t...