AIMC Topic: Machine Learning

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Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.

Scientific reports
The COVID-19 pandemic has initiated a global health emergency, with an exigent need for an effective cure. Progressively, drug repurposing is emerging as a promising solution for saving time, cost, and labor. However, the number of drug candidates th...

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.

Scientific reports
Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulnerability to infections and cancers. HIV spreads primarily via sharing needles, from mother to child during childbirth or breastfeeding, or unprotected ...

A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.

Scientific reports
Chronic obstructive pulmonary disease (COPD) is considered to be one of the most commonly occurring respiratory disorders and is proliferating at an extremely high rate in the recent years. The proposed system aims to classify the various stages of C...

Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation.

JMIR medical informatics
BACKGROUND: Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for tim...

Machine learning predictions of drug release from isocyanate-derived aerogels.

Journal of materials chemistry. B
This work utilized machine learning (ML) algorithms to predict and validate the drug release kinetics of a short worm-like nanostructured isocyanate-derived aerogel: the first time ML has been employed to study the drug delivery properties of this i...

Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study.

Journal of orthopaedic surgery and research
BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a prevalent musculoskeletal condition affecting approximately 2-3% of the adolescent population. Although exercise-based therapeutic interventions are increasingly employed as non-surgical alternat...

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BMC medical informatics and decision making
BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine lea...

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

BMC medical imaging
BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. ...

Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy.

BMC infectious diseases
The increasing incidence of dengue virus (DENV) infections poses significant public health challenges in Bangladesh, demanding advanced forecasting methodologies to guide timely interventions. This study introduces a rigorous multivariate time series...

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.

Scientific reports
Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcript...