AIMC Topic: Machine Learning

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Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning.

Biosensors
Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigat...

Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.

BMC medical research methodology
BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the trea...

Identification of metabolite-disease associations based on knowledge graph.

Metabolomics : Official journal of the Metabolomic Society
BACKGROUND: Despite the insights that metabolite analysis can provide into the onset, development, and progression of diseases-thus offering new concepts and methodologies for prevention, diagnosis, and treatment-traditional wet lab experiments are o...

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithms.

Scientific reports
Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its ...

Feasibility study of texture-based machine learning approach for early detection of neonatal jaundice.

Scientific reports
Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. Non-invasive approaches may be beneficial in settings with limited resources. This feasibility study e...

Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning.

Scientific reports
Genomic alterations are the driving force behind pancreatic cancer (PC) tumorigenesis, but they do not fully account for its diverse phenotypes. Investigating the epigenetic landscapes of PC offers a more comprehensive understanding and could identif...

Fast, smart, and adaptive: using machine learning to optimize mental health assessment and monitor change over time.

Scientific reports
In mental health, accurate symptom assessment and precise measurement of patient conditions are crucial for clinical decision-making and effective treatment planning. Traditional assessment methods can be burdensome, especially for vulnerable populat...

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.

BMC medical informatics and decision making
BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool...

Traditional and machine learning models for predicting haemorrhagic transformation in ischaemic stroke: a systematic review and meta-analysis.

Systematic reviews
BACKGROUND: Haemorrhagic transformation (HT) is a severe complication after ischaemic stroke, but identifying patients at high risks remains challenging. Although numerous prediction models have been developed for HT following thrombolysis, thrombect...

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...