AIMC Topic: Case-Control Studies
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Machine learning for early detection and severity classification in people with Parkinson's disease.
Scientific reports
Jan 2, 2025
Early detection of Parkinson's disease (PD) and accurate assessment of disease progression are critical for optimizing treatment and rehabilitation. However, there is no consensus on how to effectively detect early-stage PD and classify motor symptom...
Healthy core: Harmonizing brain MRI for supporting multicenter migraine classification studies.
PloS one
Dec 31, 2024
Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large sample sizes for developing accurate predictive models. However, multicenter studies, incorporating varying research participant characteristics, MRI scanners...
A machine learning tool for early identification of celiac disease autoimmunity.
Scientific reports
Dec 28, 2024
Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical reco...
Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study.
Medicina (Kaunas, Lithuania)
Dec 27, 2024
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...
Identifying potential medical aid beneficiaries using machine learning: A Korean Nationwide cohort study.
International journal of medical informatics
Dec 25, 2024
OBJECTIVE: To identify potential medical aid beneficiaries using demographic and medical history of individuals and analyzing important features qualitatively.
Computer model for gait assessments in Parkinson's patients using a fuzzy inference model and inertial sensors.
Artificial intelligence in medicine
Dec 24, 2024
Patients with Parkinson's disease (PD) in the moderate and severe stages can present several walk alterations. They can show slow movements and difficulty initiating, varying, or interrupting their gait; freezing; short steps; speed changes; shufflin...
Effect of wearable robot Bot Fit's hip joint-centered assist torque and voice coach on walking.
BMC musculoskeletal disorders
Dec 23, 2024
BACKGROUND: The main key to the 4th industrial era is robots, and wearable robots are incorporated into human healthcare. Samsung Electronics' Bot Fit is a hip joint-centered assistive robot that can induce walking posture and energetic walking exerc...
Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.
BMC medical research methodology
Dec 20, 2024
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...
Logistic Models
Natural Language Processing
Antitubercular Agents
Drug Interactions
Drug-Related Side Effects and Adverse Reactions
Adult
Male
Middle Aged
Case-Control Studies
Isoniazid
Chemical and Drug Induced Liver Injury
Humans
Machine Learning
Retrospective Studies
Electronic Health Records
Female
Accuracy deficits during robotic time-constrained reaching are related to altered prefrontal cortex activity in children with cerebral palsy.
Journal of neuroengineering and rehabilitation
Dec 19, 2024
BACKGROUND: The prefrontal cortex (PFC) is an important node for action planning in the frontoparietal reaching network but its role in reaching in children with cerebral palsy (CP) is unexplored. This case-control study combines a robotic task with ...
Individual risk assessment for rupture of abdominal aortic aneurysm using artificial intelligence.
Journal of vascular surgery
Dec 13, 2024
OBJECTIVE: This study aimed to develop a prediction tool to identify abdominal aortic aneurysms (AAAs) at increased risk of rupture incorporating demographic, clinical, imaging, and medication data using artificial intelligence (AI).
Aortic Rupture
Denmark
Predictive Value of Tests
Female
Aortic Aneurysm, Abdominal
Computed Tomography Angiography
Reproducibility of Results
Aortography
Risk Factors
Aged
Aged, 80 and over
Humans
Male
Artificial Intelligence
Middle Aged
Decision Support Techniques
Risk Assessment
Prognosis
Case-Control Studies
Retrospective Studies
Decision Trees
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