AIMC Topic: Machine Learning

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Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports
We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort ...

Leveraging fundus images for on device eye disease diagnosis with AI powered lightweight software hardware framework.

Scientific reports
Vision loss due to illness can result from various medical conditions that affect the eyes. Advanced devices like OCT and ultra-widefield retinal cameras are expensive, making them less accessible in resource-limited settings. While eye image capture...

Label-free estimation of regulatory T cell activation markers using Raman spectroscopy with machine learning.

Scientific reports
Regulatory T cells are a class of T lymphocytes which respond to activation signals by expanding their cell numbers, and whose culturing and expansion are of significant clinical interest. Cellular activation states are used to inform process control...

Interpretable radiomics-based machine learning model for differentiating glioblastoma from primary central nervous system lymphoma using contrast-enhanced T1-weighted imaging.

Scientific reports
This study aimed to develop and validate an interpretable radiomics-based machine learning model using contrast-enhanced T1-weighted imaging (CE-T1WI) to differentiate glioblastoma (GB) from primary central nervous system lymphoma (PCNSL), while comp...

Enhancing cardiotocography classification via ensemble learning and threshold optimization.

Scientific reports
Machine learning classifiers trained on imbalanced healthcare datasets often exhibit bias, leading to poor performance on critical cases. The cardiotocography (CTG) dataset exemplifies this issue, where misclassification of pathological cases arises ...

A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.

Journal of medical systems
Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates mul...

AI-based approach for heart failure readmission prediction using SCG, ECG, and GSR signals.

Physiological measurement
Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by pred...

High-resolution time-lapse imaging of droplet-cell dynamics optimal transport and contrastive learning.

Lab on a chip
Single-cell analysis is essential for uncovering heterogeneous biological functions that arise from intricate cellular responses. Here, microfluidic droplet arrays enable high-throughput data collection through cell encapsulation in picoliter volumes...

Analysis of sperm beating characteristics using microfluidic trapping and machine-learning-based flagellum tracking.

Lab on a chip
Male infertility affects a significant portion of couples worldwide, with standard semen analysis often failing to identify functional deficiencies in sperm performance. This study presents a microfluidic platform for characterizing sperm flagellar b...

Functional biomaterials and machine learning approaches for phenotyping heterogeneous tumor cells and extracellular vesicles.

Biomaterials science
Heterogeneity in cancer is known to be a contributor to the formation of metastatic lesions, poor prognosis, and ultimately undermines therapeutic efficacy. This same tumor heterogeneity is reflected in circulating tumor cells (CTCs) and tumor derive...