AIMC Topic: Machine Learning

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Explainable prediction of hypothermia risk in laparoscopic surgery: a retrospective cross-sectional study using machine learning.

BMC surgery
OBJECTIVE: This study aims to develop multiple machine learning models for predicting hypothermia risk in laparoscopic surgery and to perform interpretability analysis of the best-performing model. Our goal is to provide robust decision support for c...

Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods.

BMC surgery
OBJECTIVE: The presence of pancreaticobiliary maljunction (PBM) in pediatric patients with congenital choledochal malformation significantly impacts clinical management and surgical decision-making. Current preoperative evaluation of PBM coexistence ...

An accelerometer-based dataset for monitoring slag in steel manufacturing.

BMC research notes
OBJECTIVES: Slag detection in steel manufacturing is essential for ensuring high product quality and process efficiency. The purpose of the accelerometer-based data is to allow for accurate monitoring and differentiation between slag and molten metal...

Biology-driven insights into the power of single-cell foundation models.

Genome biology
BACKGROUND: Single-cell foundation models (scFMs) have emerged as powerful tools for integrating heterogeneous datasets and exploring biological systems. Despite high expectations, their ability to extract unique biological insights beyond standard m...

GENEOnet: a breakthrough in protein binding pocket detection using group equivariant non-expansive operators.

Scientific reports
Structure-based virtual screening approaches like molecular docking rely on accurately identifying and precisely calculating binding pockets to efficiently search for potential ligands. In this paper, we introduce GENEOnet, a machine learning model d...

An earth observation and explainable machine learning approach for determining the drivers of invasive species - a water hyacinth case study.

Environmental monitoring and assessment
Invasive species management is often constrained by limited resources and complicated by ecological and socio-economic variability across landscapes, leading to inconsistent outcomes. We use water hyacinth (Pontederia crassipes) in South Africa as a ...

Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms.

Scientific reports
The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the male and female distinctions. Machine learning algorithms are widely used for various applic...

Identifying prenatal risk factors of postpartum depression with machine learning.

Scientific reports
Postpartum depression (PPD), a common mental illness among mothers, can affect the well-being of both mothers and their children. Early intervention is essential but hindered by difficulties in identifying at-risk women, as it remains unclear how soo...

Application of machine learning models for predicting depression among older adults with non-communicable diseases in India.

Scientific reports
Depression among older adults is a critical public health issue, particularly when coexisting with non-communicable diseases (NCDs). In India, where population ageing and NCDs burden are rising rapidly, scalable data-driven approaches are needed to i...