AIMC Topic: Case-Control Studies

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Predicting the risk of threatened abortion using machine learning methods: a comparative study.

BMC pregnancy and childbirth
BACKGROUND AND OBJECTIVE: Threatened abortion, a common pregnancy complication that often leading to abortion, is hard to predict due to its non-specific symptoms and difficulty in differentiating from other early pregnancy bleeding causes. Current d...

Development and validation of a diagnostic prediction model for pancreatic ductal adenocarcinoma: VAPOR 1, protocol for a prospective multicentre case-control study.

BMJ open
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) continues to have extremely poor patient outcomes, unlike other cancer types which have seen significant improvements in their treatments and survival. A major contributing factor is that PDAC is ...

Comparison of the number of peripapillary perforating scleral vessels between glaucomatous eyes and healthy eyes.

Scientific reports
This study aimed to compare the number of peripapillary perforating scleral vessels (PPSVs) between eyes with and without glaucoma. A retrospective case-control analysis was performed on patients with glaucoma and control participants who underwent s...

Constructing a predictive model for acute mastitis in lactating women based on machine learning.

Scientific reports
Acute lactational mastitis is a frequently occurring complication for lactating women, exerting a certain degree of influence on their physical condition, breastfeeding, mental health, and daily life. The etiology of this disease is complex, and the ...

Altered brain structure age gap estimation in major depressive disorder patients with and without anhedonia: a machine learning-based study.

Translational psychiatry
Previous studies have found that major depressive disorder (MDD) may accelerate overall structural brain aging. Nevertheless, it still remains unknown whether anhedonia, a critical negative prognostic indicator in MDD, further leads to advanced brain...

MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach.

European radiology experimental
BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and ...

Screening for Parkinson's disease using "computer vision".

PloS one
BACKGROUND: Identifying bradykinesia is crucial for diagnosing Parkinson's disease (PD). Traditionally, the finger-tapping test has been used, relying on subjective assessments by physicians. Computer vision offers a non-contact and cost-effective al...

Circulating cell-free RNA signatures for the characterization and diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome.

Proceedings of the National Academy of Sciences of the United States of America
People living with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) experience heterogeneous and debilitating symptoms that lack sufficient biological explanation, compounded by the absence of accurate, noninvasive diagnostic tools. To add...

Machine Learning-Based Analysis of Lifestyle Risk Factors for Atherosclerotic Cardiovascular Disease: Retrospective Case-Control Study.

JMIR medical informatics
BACKGROUND: The risk of developing atherosclerotic cardiovascular disease (ASCVD) varies among individuals and is related to a variety of lifestyle factors in addition to the presence of chronic diseases.

Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.

Scientific reports
Traditional diagnostic methods for asthma, a widespread chronic respiratory illness, are often limited by factors such as patient cooperation with spirometry. Non-invasive acoustic analysis using machine learning offers a promising alternative for ob...