AIMC Topic: Machine Learning

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An Improved Extreme Learning Machine (ELM) Algorithm for Intent Recognition of Transfemoral Amputees With Powered Knee Prosthesis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
To overcome the challenges posed by the complex structure and large parameter requirements of existing classification models, the authors propose an improved extreme learning machine (ELM) classifier for human locomotion intent recognition in this st...

The relationship between satisfaction with life, dietary attitude, and comfort level in hemodialysis patients: Analysis with machine learning approach.

Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
INTRODUCTION: Hemodialysis (HD) patients have become a serious public health problem, the number of which is increasing every year worldwide. This study aimed to determine the relationship between satisfaction with life, dietary attitude, and comfort...

Machine Teaching Allows for Rapid Development of Automated Systems for Retinal Lesion Detection From Small Image Datasets.

Ophthalmic surgery, lasers & imaging retina
Machine teaching, a machine learning subfield, may allow for rapid development of artificial intelligence systems able to automatically identify emerging ocular biomarkers from small imaging datasets. We sought to use machine teaching to automaticall...

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Acta tropica
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collaps...

Automated Machine Learning and Explainable AI (AutoML-XAI) for Metabolomics: Improving Cancer Diagnostics.

Journal of the American Society for Mass Spectrometry
Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best algorithms and tuning hyperparameters, particularly for...

AKIML: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in c...

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants.

Bioresource technology
Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption model...

Automated annotation of disease subtypes.

Journal of biomedical informatics
BACKGROUND: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications...

Developing a Machine-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Patients Undergoing Total Knee Arthroplasty.

Orthopaedic surgery
OBJECTIVE: Predicting whether the posterior cruciate ligament (PCL) should be preserved during total knee arthroplasty (TKA) procedures is a complex task in the preoperative phase. The choice to either retain or excise the PCL has a substantial effec...

Combining serum microRNAs and machine learning algorithms for diagnosing infectious fever after HSCT.

Annals of hematology
Infection post-hematopoietic stem cell transplantation (HSCT) is one of the main causes of patient mortality. Fever is the most crucial clinical symptom indicating infection. However, current microbial detection methods are limited. Therefore, timely...