AIMC Topic: Machine Learning

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Development and external validation of an interpretable machine learning model for predicting perinatal depression in Chinese women during mid- and late pregnancy.

International journal of medical informatics
OBJECTIVE: This study aimed to develop a machine learning (ML)-based prediction model for antenatal depression (AND) in Chinese women. Given the significant impact of AND on maternal and infant health, the goal was to create an accurate and interpret...

Machine learning-based error detection in the clinical laboratory: a critical review.

Critical reviews in clinical laboratory sciences
Laboratory test results play a crucial role in the modern medical decision-making process. As such, errors in any phase of the testing process can have substantial clinical and operational impacts. While the development of increasingly robust quality...

Predicting takotsubo syndrome subtypes: An interpretable machine learning model for differentiating emotional versus physical aetiologies.

International journal of cardiology
BACKGROUND: Takotsubo syndrome (TTS) is an acute coronary syndrome characterized by a reversible, mostly apical dysfunction of the left ventricle. Based on the triggering event, TTS has been classified as primary due to emotional causes and secondary...

RCMIX model based on pre-treatment MRI imaging predicts T-downstage in MRI-cT4 stage rectal cancer.

Cancer letters
Neoadjuvant therapy (NAT) is the standard treatment strategy for MRI-defined cT4 rectal cancer. Predicting tumor regression can guide the resection plane to some extent. Here, we covered pre-treatment MRI imaging of 363 cT4 rectal cancer patients rec...

Digital health framework for the predictive surveillance and diagnosis of atopic dermatitis.

Water research
Atopic dermatitis (AD) is an inflammatory skin disease with immunological and environmental triggers that reduces the quality of life and increases the burden on health services. It is thus important to establish effective surveillance and diagnosis ...

Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.

Journal of hazardous materials
Micro(nano)plastics are ubiquitous and pose a severe threat to the environment and human health. Despite increasing research, most existing studies have focused on the toxicity of micro(nano)plastics as individual pollutants. Furthermore, fragmented ...

Use of machine learning for real-time antibiotic treatment adjustment in high-risk patients with CRGNB infection.

Computer methods and programs in biomedicine
BACKGROUND: Infections caused by carbapenem resistant gram-negative bacilli (CRGNB) are associated with high mortality and pose a great challenge for clinical treatment. We aim to identify patients at high risk for CRGNB as early as possible and aler...

A plasma metabolome-derived model predicts severe liver outcomes of nonalcoholic fatty liver disease in the UK Biobank.

Diabetes, obesity & metabolism
AIMS: Severe liver disease (SLD) in nonalcoholic fatty liver disease (NAFLD) is often diagnosed late due to the long asymptomatic period of progressive fibrosis. We aimed to identify metabolomic profiles associated with SLD and develop a predictive m...