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Empirical Research

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Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments.

Journal of evaluation in clinical practice
RATIONALE, AIMS AND OBJECTIVES: Interventions with multivalued treatments are common in medical and health research; examples include comparing the efficacy of competing interventions and contrasting various doses of a drug. In recent years, there ha...

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Annals of the New York Academy of Sciences
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuo...

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PloS one
In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's v...

Can We Train Machine Learning Methods to Outperform the High-dimensional Propensity Score Algorithm?

Epidemiology (Cambridge, Mass.)
The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for misme...

Classification of gait patterns between patients with Parkinson's disease and healthy controls using phase space reconstruction (PSR), empirical mode decomposition (EMD) and neural networks.

Neural networks : the official journal of the International Neural Network Society
Parkinson's disease (PD) is a common neurodegenerative disorder that affects human's quality of life, especially leading to locomotor deficits such as postural instability and gait disturbances. Gait signal is one of the best features to characterize...

Non-Gaussian Methods for Causal Structure Learning.

Prevention science : the official journal of the Society for Prevention Research
Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Neverth...

An Empirical Study on the Artificial Intelligence Writing Evaluation System in China CET.

Big data
The Artificial Intelligence Writing Evaluation system is widely used in China College English writing. It provides for both teachers and the English learners services of automated composition evaluation on the net in order that teacher's working load...

Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences.

Hippocampus
Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs...

Abstract art paintings, global image properties, and verbal descriptions: An empirical and computational investigation.

Acta psychologica
While global image properties (GIPs) relate to preference ratings in many categories of visual stimuli, this relationship is typically not seen for abstract art paintings. Using computational network science and empirical methods, we further investig...