AI Medical Compendium Topic:
Forecasting

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Forecasting influenza activity using machine-learned mobility map.

Nature communications
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials a...

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.

BMC medical informatics and decision making
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Ide...

Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.

PloS one
BACKGROUND: Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavele...

Algorithmic and human prediction of success in human collaboration from visual features.

Scientific reports
As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in...

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...

How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis.

European journal of radiology
PURPOSE: We aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits, and reflects on its limitations.

Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.

Modeling multivariate clinical event time-series with recurrent temporal mechanisms.

Artificial intelligence in medicine
In this work, we propose a novel autoregressive event time-series model that can predict future occurrences of multivariate clinical events. Our model represents multivariate event time-series using different temporal mechanisms aimed to fit differen...

A unified machine learning approach to time series forecasting applied to demand at emergency departments.

BMC emergency medicine
BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provid...

Promises and perils of artificial intelligence in dentistry.

Australian dental journal
Artificial intelligence (AI) is a subdiscipline of computer science that has made substantial progress in medicine and there is a growing body of AI research in dentistry. Dentists should have an understanding of the foundational concepts and the abi...