AIMC Topic: Forecasting

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Applications of machine learning in routine laboratory medicine: Current state and future directions.

Clinical biochemistry
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory te...

Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes.

Global change biology
The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and p...

Prediction of clinical trial enrollment rates.

PloS one
Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by ins...

Forecasting COVID-19 new cases using deep learning methods.

Computers in biology and medicine
After nearly two years since the first identification of SARS-CoV-2 virus, the surge in cases because of virus mutations is a cause of grave public health concern across the globe. As a result of this health crisis, predicting the transmission patter...

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.

Radiation oncology (London, England)
BACKGROUND: In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient's body surface using a prediction model. In this work, we ...

A new automatic forecasting method based on a new input significancy test of a single multiplicative neuron model artificial neural network.

Network (Bristol, England)
The model adequacy and input significance tests have not been proposed as features for the specification of a single multiplicative neuron model artificial neural networks in the literature. Moreover, there is no systematic approach based on hypothes...

The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

PloS one
BACKGROUND AND OBJECTIVE: Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population's health but also affects economic and social development. It requires an accurate prediction analysis to help to m...

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting.

Computational intelligence and neuroscience
In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour...

Integrating Multimodal Electronic Health Records for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical cod...

Deep reinforcement learning stock market trading, utilizing a CNN with candlestick images.

PloS one
Billions of dollars are traded automatically in the stock market every day, including algorithms that use neural networks, but there are still questions regarding how neural networks trade. The black box nature of a neural network gives pause to entr...