AIMC Topic: Forecasting

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Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

American journal of clinical oncology
OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for ...

MOLI: multi-omics late integration with deep neural networks for drug response prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to ...

Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all...

Model-free Cardiorespiratory Motion Prediction from X-ray Angiography Sequence with LSTM Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We present a novel model-free approach for cardiorespiratory motion prediction from X-ray angiography time series based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Cardiorespiratory motion prediction is defined as a problem of est...

Automated Classification of Airborne Pollen using Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pollen allergies are considered as a global epidemic nowadays, as they influence more than a quarter of the worldwide population, with this percentage expected to rapidly increase because of ongoing climate change. To date, alerts on high-risk allerg...

Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain's anatomi...

Sparse Embedding for Interpretable Hospital Admission Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder to embed the original registry data into a much lower dimension, with sparsity as a goal. Then, t-S...

Predicting Gastrointestinal Bleeding Events from Multimodal In-Hospital Electronic Health Records Using Deep Fusion Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Applying machine learning (ML) methods on electronic health records (EHRs) that accurately predict the occurrence of a variety of diseases or complications related to medications can contribute to improve healthcare quality. EHRs by nature contain mu...

Application of Machine Learning to Prediction of Surgical Site Infection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Surgical site infections are an important health concern, particularly in low-resource areas, where there is poor access to clinical facilities or trained clinical staff. As an application of machine learning, we present results from a study conducte...