AIMC Topic: Machine Learning

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Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BMC medical informatics and decision making
BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over tradit...

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism.

Scientific reports
Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balan...

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.

Scientific reports
Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effec...

Detection and quantification of groundnut oil adulteration with machine learning using a comparative approach with NIRS and UV-VIS.

Scientific reports
Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. It has a distinctive fragrant making it ideal for cooking which contribute to its demand on the market. However...

Thermodynamics-inspired explanations of artificial intelligence.

Nature communications
In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promising strate...

Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

Journal of medical Internet research
BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to preve...

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

eLife
Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning (ML) approaches to cross-...

Machine learning powered detection of biological toxins in association with confined lateral flow immunoassay (c-LFA).

The Analyst
Biological weapons, primarily dispersed as aerosols, can spread not only to the targeted area but also to adjacent regions following the movement of air driven by wind. Thus, there is a growing demand for toxin analysis because biological weapons are...

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact ...

Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and charac...