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Clinical Decision-Making

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Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

Revista brasileira de enfermagem
OBJECTIVE: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex.

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.

Neurocritical care
Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neuro...

Predictive models for clinical decision making: Deep dives in practical machine learning.

Journal of internal medicine
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an int...

Application and potential of artificial intelligence in neonatal medicine.

Seminars in fetal & neonatal medicine
Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become...

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data.

Journal of biomedical informatics
BACKGROUND: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful ...

Artificial intelligence in positioning between mandibular third molar and inferior alveolar nerve on panoramic radiography.

Scientific reports
Determining the exact positional relationship between mandibular third molar (M3) and inferior alveolar nerve (IAN) is important for surgical extractions. Panoramic radiography is the most common dental imaging test. The purposes of this study were t...

Dynamic Bayesian networks for prediction of health status and treatment effect in patients with chronic lymphocytic leukemia.

Scientific reports
Chronic lymphocytic leukemia (CLL) is the most common blood cancer in adults. The course of CLL and patients' response to treatment are varied. This variability makes it difficult to select the most appropriate treatment regimen and predict the progr...

Machine learning and artificial intelligence in research and healthcare.

Injury
Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the proc...

Artificial intelligence for medical image analysis in epilepsy.

Epilepsy research
Given improvements in computing power, artificial intelligence (AI) with deep learning has emerged as the state-of-the art method for the analysis of medical imaging data and will increasingly be used in the clinical setting. Recent work in epilepsy ...

The inclusion of augmented intelligence in medicine: A framework for successful implementation.

Cell reports. Medicine
Artificial intelligence (AI) algorithms are being applied across a large spectrum of everyday life activities. The implementation of AI algorithms in clinical practice has been met with some skepticism and concern, mainly because of the uneasiness th...