AI Medical Compendium Topic

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State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into tw...

Pollutant specific optimal deep learning and statistical model building for air quality forecasting.

Environmental pollution (Barking, Essex : 1987)
Poor air quality is becoming a critical environmental concern in different countries over the last several years. Most of the air pollutants have serious consequences on human health and wellbeing. In this context, efficient forecasting of air pollut...

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Statistics in medicine
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe...

A machine learning model for predicting deterioration of COVID-19 inpatients.

Scientific reports
The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predi...

A Topic Recognition Method of News Text Based on Word Embedding Enhancement.

Computational intelligence and neuroscience
Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendatio...

A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models.

Artificial intelligence in medicine
OBJECTIVE: Clinical prediction models (CPMs) constructed based on artificial intelligence have been proven to have positive impacts on clinical activities. However, the deterioration of CPM performance over time has rarely been studied. This paper pr...

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study.

Scientific reports
Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding...

A systematic comparison of generative models for medical images.

International journal of computer assisted radiology and surgery
PURPOSE: This work aims for a systematic comparison of popular shape and appearance models. Here, two statistical and four deep-learning-based shape and appearance models are compared and evaluated in terms of their expressiveness described by their ...

Incomplete Label Multiple Instance Multiple Label Learning.

IEEE transactions on pattern analysis and machine intelligence
With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-...

Developing machine learning models for prediction of mortality in the medical intensive care unit.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scori...