AI Medical Compendium Topic:
Models, Statistical

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Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.

International journal of environmental research and public health
BACKGROUND: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was ...

Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients.

PLoS neglected tropical diseases
BACKGROUND: Assessment of the response to the 2014-15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Diseas...

Generating action descriptions from statistically integrated representations of human motions and sentences.

Neural networks : the official journal of the International Neural Network Society
It is desirable for robots to be able to linguistically understand human actions during human-robot interactions. Previous research has developed frameworks for encoding human full body motion into model parameters and for classifying motion into spe...

A novel method of artery stenosis diagnosis using transfer function and support vector machine based on transmission line model: A numerical simulation and validation study.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Transfer function (TF) is an important parameter for the analysis and understanding of hemodynamics when arterial stenosis exists in human arterial tree. Aimed to validate the feasibility of using TF to diagnose arterial ste...

Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS).

BMJ open
OBJECTIVES: Mississippi (MS) is one of the southern states with high rates of foodborne infections. The objectives of this paper are to determine the extent of Salmonella and Escherichia coli infections in MS, and determine the Salmonella infections ...

Sequential detection of learning in cognitive diagnosis.

The British journal of mathematical and statistical psychology
In order to look more closely at the many particular skills examinees utilize to answer items, cognitive diagnosis models have received much attention, and perhaps are preferable to item response models that ordinarily involve just one or a few broad...

Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
The human colorectal carcinoma cell line (Caco-2) is a commonly used in-vitro test that predicts the absorption potential of orally administered drugs. In-silico prediction methods, based on the Caco-2 assay data, may increase the effectiveness of th...

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images.

IEEE transactions on bio-medical engineering
GOAL: In this work, we present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.

Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

Applied clinical informatics
OBJECTIVES: Discharging patients from the Neonatal Intensive Care Unit (NICU) can be delayed for non-medical reasons including the procurement of home medical equipment, parental education, and the need for children's services. We previously created ...

Symmetry Analysis of Gait between Left and Right Limb Using Cross-Fuzzy Entropy.

Computational and mathematical methods in medicine
The purpose of this paper is the investigation of gait symmetry problem by using cross-fuzzy entropy (C-FuzzyEn), which is a recently proposed cross entropy that has many merits as compared to the frequently used cross sample entropy (C-SampleEn). Fi...