AIMC Topic: Machine Learning

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A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

Scientific reports
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value...

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.

Frontiers in public health
Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients...

A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation.

Computational intelligence and neuroscience
Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation ba...

prPred-DRLF: Plant R protein predictor using deep representation learning features.

Proteomics
Plant resistance (R) proteins play a significant role in the detection of pathogen invasion. Accurately predicting plant R proteins is a key task in phytopathology. Most plant R protein predictors are dependent on traditional feature extraction metho...

Deep Ensemble Learning Approaches in Healthcare to Enhance the Prediction and Diagnosing Performance: The Workflows, Deployments, and Surveys on the Statistical, Image-Based, and Sequential Datasets.

International journal of environmental research and public health
With the development of information and technology, especially with the boom in big data, healthcare support systems are becoming much better. Patient data can be collected, retrieved, and stored in real time. These data are valuable and meaningful f...

Prediction of viral symptoms using wearable technology and artificial intelligence: A pilot study in healthcare workers.

PloS one
Conventional testing and diagnostic methods for infections like SARS-CoV-2 have limitations for population health management and public policy. We hypothesize that daily changes in autonomic activity, measured through off-the-shelf technologies toget...

Machine learning-assisted screening for cognitive impairment in the emergency department.

Journal of the American Geriatrics Society
BACKGROUND/OBJECTIVES: Despite a high prevalence and association with poor outcomes, screening to identify cognitive impairment (CI) in the emergency department (ED) is uncommon. Identification of high-risk subsets of older adults is a critical chall...

Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison.

Medical engineering & physics
Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize and classify leukemia. But these methods are costly, slow and affected by the proficiency and exper...

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy.

Cancer research
Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate th...

Revealing Contamination and Sequence of Overlapping Fingerprints by Unsupervised Treatment of a Hyperspectral Secondary Ion Mass Spectrometry Dataset.

Analytical chemistry
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been successfully applied for chemical imaging of overlapping fingermarks. The resulting big dataset has been treated by means of an unsupervised machine learning approach based on uniform...