AIMC Topic: Risk Assessment

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Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Mortality prediction models for patients with perforated peptic ulcer (PPU) have not yielded consistent or highly accurate results. Given the complex nature of this disease, which has many non-linear associations with outcomes, we explored a...

Environmental contamination, product contamination and workers exposure using a robotic system for antineoplastic drug preparation.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Environmental contamination, product contamination and technicians exposure were measured following preparation of iv bags with cyclophosphamide using the robotic system CytoCare. Wipe samples were taken inside CytoCare, in the clean room environment...

Classifier calibration using splined empirical probabilities in clinical risk prediction.

Health care management science
The aims of supervised machine learning (ML) applications fall into three broad categories: classification, ranking, and calibration/probability estimation. Many ML methods and evaluation techniques relate to the first two. Nevertheless, there are ma...

Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study.

European journal of preventive cardiology
AIMS: Evidence is limited as to who benefit the most from sodium-glucose cotransporter 2 inhibitors (SGLT2i), especially among people without elevated cardiovascular disease (CVD) risk. To address this knowledge gap, we investigated the heterogeneity...

Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003-2016.

European journal of preventive cardiology
AIMS: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases...

[Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) u...

Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.

International journal of cardiology
BACKGROUND: Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This stud...

Artificial intelligence-enhanced electrocardiogram diastolic function grade predicts post-septal myectomy mortality in hypertrophic cardiomyopathy.

The Journal of thoracic and cardiovascular surgery
BACKGROUNDS: Diastolic dysfunction is an important pathophysiologic feature of hypertrophic cardiomyopathy that is often challenging to determine noninvasively. This study investigated whether a novel artificial intelligence-enabled electrocardiograp...

Preoperative risk assessment of invasive endometrial cancer using MRI-based radiomics: a systematic review and meta-analysis.

Abdominal radiology (New York)
OBJECTIVE: Image-derived machine learning (ML) is a robust and growing field in diagnostic imaging systems for both clinicians and radiologists. Accurate preoperative radiological evaluation of the invasive ability of endometrial cancer (EC) can incr...