AIMC Topic: Machine Learning

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FADE: Forecasting for anomaly detection on ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...

Predicting responsiveness to a dialectical behaviour therapy skills training app for recurrent binge eating: A machine learning approach.

Behaviour research and therapy
OBJECTIVE: Smartphone applications (apps) show promise as an effective and scalable intervention modality for disordered eating, yet responsiveness varies considerably. The ability to predict user responses to app-based interventions is currently lim...

Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.

International journal of surgery (London, England)
Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable prognosis, but lymph node metastasis (LNM) complicates treatment and increases recurrence risk. Current preoperative methods like neck ultrasound often miss...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...

Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.

European journal of medical research
BACKGROUND: The application of machine learning (ML) in predicting the requirement for total knee arthroplasty (TKA) at knee osteoarthritis (KOA) patients has been acknowledged. Nonetheless, the variables employed in the development of ML models are ...

Fast and effective assessment for individuals with special needs form optimization and prediction models.

BMC psychology
The aim of this study was to determine which items in the psychological assessment forms used by counselling and research centres for individuals with special needs are effective in classifying individuals into special needs diagnostic categories. Da...

Preoperative lymph node metastasis risk assessment in invasive micropapillary carcinoma of the breast: development of a machine learning-based predictive model with a web-based calculator.

World journal of surgical oncology
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer characterized by a high risk of lymph node metastasis (LNM). The study aimed to identify predictors of LNM and to develop a machine learning (ML)-based risk predi...

Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

BMC medical informatics and decision making
OBJECTIVE: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.

BMC pediatrics
BACKGROUND: Healthcare practitioners require a robust predictive system to accurately diagnose diseases, especially in young children with conditions such as anemia. Delays in diagnosis and treatment can have severe consequences, potentially leading ...