AIMC Topic: Malaria

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Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases Nov 26, 2020
OBJECTIVES: This study aims to identify significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia.
Pregnancy Female Adult Humans Machine Learning Indonesia Fever Endemic Diseases Male Middle Aged Malaria
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Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Spatial and spatio-temporal epidemiology Jul 4, 2020
Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regress...
Nonlinear Dynamics Incidence Malaria Malaria, Falciparum Humans Prevalence
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An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra.

PloS one Jun 18, 2020
After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing th...
Oviparity Humans Neural Networks, Computer Malaria Mosquito Vectors Anopheles Female Animals Spectroscopy, Near-Infrared
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An Effective Convolutional Neural Network for Classifying Red Blood Cells in Malaria Diseases.

Interdisciplinary sciences, computational life sciences May 11, 2020
Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classificat...
Humans Neural Networks, Computer Reproducibility of Results Erythrocytes Plasmodium Deep Learning Malaria Models, Biological
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Expert-level automated malaria diagnosis on routine blood films with deep neural networks.

American journal of hematology Apr 30, 2020
Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban hea...
Malaria Malaria, Falciparum Humans Neural Networks, Computer
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Sequential classification system for recognition of malaria infection using peripheral blood cell images.

Journal of clinical pathology Mar 16, 2020
AIMS: Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood ...
Machine Learning Malaria Microscopy Image Interpretation, Computer-Assisted Erythrocytes Inclusion Bodies Humans
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An AI-based approach in determining the effect of meteorological factors on incidence of malaria.

Frontiers in bioscience (Landmark edition) Mar 1, 2020
This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers i...
Humans India Models, Theoretical Support Vector Machine Malaria Insect Vectors Meteorological Concepts Reproducibility of Results Animals Incidence Neural Networks, Computer Algorithms
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Deep Learning-driven research for drug discovery: Tackling Malaria.

PLoS computational biology Feb 18, 2020
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is ...
Malaria Antimalarials Quantitative Structure-Activity Relationship Drug Discovery Deep Learning Structure-Activity Relationship Humans Reproducibility of Results
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A new backpropagation neural network classification model for prediction of incidence of malaria.

Frontiers in bioscience (Landmark edition) Jan 1, 2020
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classifie...
Reproducibility of Results Incidence India Models, Theoretical Support Vector Machine Malaria Neural Networks, Computer Algorithms Geography Temperature Rain Humans Seasons Humidity
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A novel model for malaria prediction based on ensemble algorithms.

PloS one Dec 26, 2019
BACKGROUND AND OBJECTIVE: Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can s...
Malaria Humans China Incidence Algorithms Communicable Diseases Deep Learning
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