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Bayes Theorem

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In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study.

BMC medical informatics and decision making
BACKGROUND: Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with...

Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction.

ACS synthetic biology
Predicting properties of proteins is of interest for basic biological understanding and protein engineering alike. Increasingly, machine learning (ML) approaches are being used for this task. However, the accuracy of such ML models typically degrades...

Bayesian parametric models for survival prediction in medical applications.

BMC medical research methodology
BACKGROUND: Evidence-based treatment decisions in medicine are made founded on population-level evidence obtained during randomized clinical trials. In an era of personalized medicine, these decisions should be based on the predicted benefit of a tre...

Application of statistical machine learning in biomarker selection.

Scientific reports
In the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in ...

Artificial intelligence extracts key insights from legal documents to predict intimate partner femicide.

Scientific reports
Legal documents serve as valuable repositories of information pertaining to crimes, encompassing not only legal aspects but also relevant details about criminal behaviors. To date and the best of our knowledge, no studies in the field examine legal d...

A decoupled Bayesian method for snake robot control in unstructured environment.

Bioinspiration & biomimetics
This paper presents a method which avoids the common practice of using a complex coupled snake robot model and performing kinematic analysis for control in cluttered environments. Instead, we introduce a completely decoupled dynamical Bayesian formul...

Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.

BMC medical imaging
BACKGROUND: The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model pre...

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

Surgical endoscopy
BACKGROUND: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotatio...

IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques.

Computational intelligence and neuroscience
Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is com...

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Scientific reports
Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously...