AIMC Topic: Hip Fractures

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Development of a machine learning-based model for predicting the functional outcome of patients with proximal femur fractures.

Scientific reports
Early-stage rehabilitation is crucial for the functional recovery of patients with proximal femur fractures. Predicting functional prognosis at such an early stage can simplify the process of planning for transfers and discharge destinations, as well...

Establishment of a postoperative delirium risk prediction model for elderly hip fracture patients based on machine learning algorithms.

BMC geriatrics
BACKGROUND: Although no definitive treatment exists, 30-40% of postoperative delirium cases are preventable through early risk identification and intervention. Therefore our aim was to develop and evaluate a postoperative delirium risk prediction mod...

Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.

BMC geriatrics
BACKGROUND: Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine ...

Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis.

JMIR medical informatics
BACKGROUND: Standardized registries, such as the International Classification of Diseases (ICD) codes, are commonly built using administrative codes assigned to patient encounters. However, patients with fall injury are often coded using subsequent i...

Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases.

Acta orthopaedica
BACKGROUND AND PURPOSE:  Treatment-related shared decision-making (SDM) in older adults with hip fractures is complex due to the need to balance patient-specific factors such as life goals, frailty, and surgical risks. It includes considerations such...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Scientific reports
Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures. In this study, we developed machine learning (ML) algorithms to estimate 30-day mortality risk post...

Automatic pain classification in older patients with hip fracture based on multimodal information fusion.

Scientific reports
Given the limitations of unimodal pain recognition approaches, this study aimed to develop a multimodal pain recognition system for older patients with hip fractures using multimodal information fusion. The proposed system employs ResNet-50 for facia...

The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis.

Journal of orthopaedic surgery and research
BACKGROUND: Thrombus formation is a severe complication in orthopedic surgery, significantly increasing mortality in patients with fractures. Therefore, identifying feature genes to determine thrombus presence in fracture surgeries is critical.

Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study.

BMC geriatrics
BACKGROUND: Acute heart failure (AHF) has become a significant challenge in older people with hip fractures. Timely identification and assessment of preoperative AHF have become key factors in reducing surgical risks and improving outcomes.