AIMC Topic: Models, Statistical

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Integrating AI for infectious disease prediction: A hybrid ANN-XGBoost model for leishmaniasis in Pakistan.

Acta tropica
Addressing leishmaniasis infection remains a substantial challenge in KP-Pakistan due to the increased infection prevalence. Understanding its spreading tool offerings is a major challenge. We essentially design effective approaches to pinpoint its e...

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features.

Orthopaedic surgery
OBJECTIVE: Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial for proper treatment planning. Studies have shown that in emergency settings, the misdiagnosis rate of hand/wrist fractures can reach ...

An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.

Journal of global health
BACKGROUND: Modelling can contribute to disease prevention and control strategies. Accurate predictions of future cases and mortality rates were essential for establishing appropriate policies during the COVID-19 pandemic. However, no single model yi...

An Interpretable Model With Probabilistic Integrated Scoring for Mental Health Treatment Prediction: Design Study.

JMIR medical informatics
BACKGROUND: Machine learning (ML) systems in health care have the potential to enhance decision-making but often fail to address critical issues such as prediction explainability, confidence, and robustness in a context-based and easily interpretable...

Machine learning models for prediction of NPVR ≥80% with HIFU ablation for uterine fibroids.

International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group
BACKGROUND: Currently high-intensity focused ultrasound (HIFU) is widely used to treat uterine fibroids (UFs). The aim of this study is to develop a machine learning model that can accurately predict the efficacy of HIFU ablation for UFs, assisting t...

Current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems.

Journal of chromatography. A
Aqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whole cells. They are known for selective separations, creating a stabilizing, low-shear environment, an...

Effective Semi-Supervised Medical Image Segmentation With Probabilistic Representations and Prototype Learning.

IEEE transactions on medical imaging
Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous lit...

Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.

PloS one
INTRODUCTION: Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fracture risk, is a significant health concern. Traditional diagnostic methods, such as Dual-energy X-ray A...

Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo study.

Computers in biology and medicine
The increasing use of ionizing radiation has raised concerns about adverse and long-term health risks for individuals. Therefore, to evaluate the range of risks and protection against ionizing radiation, it is necessary to assess the dosimetry calcul...

Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review.

BMJ open
OBJECTIVES: To evaluate whether postpartum haemorrhage (PPH) can be predicted using both machine learning (ML) and traditional statistical models.