AIMC Topic: Machine Learning

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An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While t...

Classification and Prediction of Food Safety Policy Tools in China Based on Machine Learning.

Journal of food protection
Governments use policy interventions to mitigate food safety risks. Despite its crucial role, empirical studies evaluating the effectiveness of China's food safety policy tools are scarce. Drawing on a dataset encompassing 11,236 food safety policy t...

Multi-objective location-routing optimization based on machine learning for green municipal waste management.

Waste management (New York, N.Y.)
Most of the existing municipal waste management (MWM) systems focus on the optimization of the waste disposal center locations and waste collection paths, which can be modeled based on the location-routing problem (LRP). This study models a green MWM...

Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.

Environmental science and pollution research international
Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and...

Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer.

Aging
Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gast...

Development and validation of machine learning models to predict frailty risk for elderly.

Journal of advanced nursing
AIMS: Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to pred...

Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.

Investigative radiology
BACKGROUND: Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artific...

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review.

Seminars in thrombosis and hemostasis
Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the...

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML mo...

Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation.

The Laryngoscope
INTRODUCTION: Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process. This study hypothesizes that LORs do contain valuable insights into applicants and can be used to ...