AI Medical Compendium Topic:
Models, Statistical

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Using a stacked-autoencoder neural network model to estimate sea state bias for a radar altimeter.

PloS one
This paper constructed a stacked-autoencoder neural network model (SAE model) to estimate sea state bias (SSB) based on radar altimeter data. Six cycles of the geophysical data record (GDR) from Jason-1/2 radar altimeters were used as a training data...

Non-invasive prediction of blood glucose trends during hypoglycemia.

Analytica chimica acta
Over the last four decades, there has been a pursuit for a non-invasive solution for glucose measurement, but there is not yet any viable product released. Of the many sensor modalities tried, the combination of electrical and optical measurement is ...

Tuning model parameters in class-imbalanced learning with precision-recall curve.

Biometrical journal. Biometrische Zeitschrift
An issue for class-imbalanced learning is what assessment metric should be employed. So far, precision-recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study i...

Validation of Prediction Models for Critical Care Outcomes Using Natural Language Processing of Electronic Health Record Data.

JAMA network open
IMPORTANCE: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, usin...

Applying Machine Learning to Linked Administrative and Clinical Data to Enhance the Detection of Homelessness among Vulnerable Veterans.

AMIA ... Annual Symposium proceedings. AMIA Symposium
U.S. military veterans who were discharged from service for misconduct are at high risk for homelessness. Stratifying homelessness risk based on both military service factors and clinical characteristics could facilitate targeted provision of prevent...

Combination of conditional random field with a rule based method in the extraction of PICO elements.

BMC medical informatics and decision making
BACKGROUND: Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it fro...

Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning.

IEEE journal of biomedical and health informatics
Drug-side effect association contains the information on marketed medicines and their recorded adverse drug reactions. Traditional experimental method is time consuming and expensive. All associations of drugs and side-effects are seen as a bipartite...

Machine learning methods for leveraging baseline covariate information to improve the efficiency of clinical trials.

Statistics in medicine
Clinical trials are widely considered the gold standard for treatment evaluation, and they can be highly expensive in terms of time and money. The efficiency of clinical trials can be improved by incorporating information from baseline covariates tha...

Adversarial MACE Prediction After Acute Coronary Syndrome Using Electronic Health Records.

IEEE journal of biomedical and health informatics
Acute coronary syndrome (ACS), as an emergent and severe syndrome due to decreased blood flow in the coronary arteries, is a leading cause of death and serious long-term disability globally. ACS is usually caused by one of three problems: ST elevatio...

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer.

Environmental science and pollution research international
TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrati...