AIMC Topic: Machine Learning

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Machine learning to evaluate the effects of non-clinical social determinant features in predicting colorectal Cancer mortality in a medically underserved Appalachian population.

Scientific reports
Colorectal cancer (CRC) is the 2nd leading cause of cancer death in the United States (US). Rural Appalachia suffers the highest CRC incidence and mortality rates. There are several non-clinical health-related social determinant factors (SDOH) associ...

New hybrid features extracted from US images for breast cancer classification.

Scientific reports
Artificial intelligence (AI), and image processing fields play a vital role in classifying benign and malignant breast cancer (BC). The novelty of this paper lies in computing original hybrid features (HF) from textural and shape features of BC integ...

Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression.

Scientific reports
Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. However, these models often cannot provide early detection of affected brain regions. ...

In Silico tool for predicting, designing and scanning IL-2 inducing peptides.

Scientific reports
Interleukin-2 (IL-2) based immunotherapy has been approved for treating certain types of cancer, as IL-2 plays a crucial role in regulating the immune system. In this study, we developed a method for predicting IL-2-inducing peptides. Our method was ...

Multi-scale machine learning model predicts muscle and functional disease progression.

Scientific reports
Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, cur...

Forecasting birth trends in Ethiopia using time-series and machine-learning models: a secondary data analysis of EDHS surveys (2000-2019).

BMJ open
OBJECTIVE: Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to fun...

Machine learning model for postpancreaticoduodenectomy haemorrhage prediction: an international multicentre cohort study.

BMJ open
OBJECTIVES: To develop and validate a machine learning model for precise risk stratification of postpancreaticoduodenectomy haemorrhage (PPH), enabling early identification of high-risk patients to guide clinical intervention.

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong.

JMIR cancer
BACKGROUND: Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predomi...

Effectiveness of a Machine Learning-Enabled Skincare Recommendation for Mild-to-Moderate Acne Vulgaris: 8-Week Evaluator-Blinded Randomized Controlled Trial.

JMIR dermatology
BACKGROUND: Acne vulgaris (AV) is one of the most common skin disorders, with a peak incidence in adolescence and early adulthood. Topical treatments are usually used for mild to moderate AV; however, a lack of adherence to topical treatment is seen ...

A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation.

JMIR medical informatics
BACKGROUND: Syndrome differentiation in traditional Chinese medicine (TCM) is an ancient principle that guides disease diagnosis and treatment. Among these, the cold and hot syndromes play a crucial role in identifying the nature of the disease and g...