AIMC Topic: Retrospective Studies

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Systemic coagulation-inflammation index in the prediction of ISR in patients undergoing drug-eluting stents implant: A retrospective study based on multiple machine learning methods.

International journal of cardiology
BACKGROUND: The Systemic Coagulation-Inflammation index (SCI) is an innovative hematological metric that accurately reflects both coagulopathic and inflammatory dynamics. In this paper, the objective of this paper is to explain the prognostic impact ...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

International journal of medical informatics
BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractures in the elderly are associated with osteoporosis, making it particularly important to screen the ...

Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

International journal of cardiology
BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning...

CBCT radiomics features combine machine learning to diagnose cystic lesions in the jaw.

Dento maxillo facial radiology
OBJECTIVE: The aim of this study was to develop a radiomics model based on cone beam CT (CBCT) to differentiate odontogenic cysts (OCs), odontogenic keratocysts (OKCs), and ameloblastomas (ABs).

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Shock (Augusta, Ga.)
Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and mortality. Machine learning (ML), combined with parameters such as shock indices, can potentially imp...

EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.

Shock (Augusta, Ga.)
Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-a...

ChatGPT artificial intelligence in clinical data analysis: an example comparing standard fusion prostate biopsy outcomes after robotic-assisted radical prostatectomy (RaRP).

Archivio italiano di urologia, andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica
OBJECTIVE: To compare statistical outputs from ChatGPT 4.0 and human experts in both comparative and correlation analyses in the evaluation of multiparametric MRI/ultrasound fusion-targeted biopsy plus random biopsy versus standard random biopsy alon...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

European journal of cancer (Oxford, England : 1990)
PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.