AIMC Topic: Humans

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Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study.

BMC medical informatics and decision making
BACKGROUND: Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BMC biology
BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementa...

Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis.

BMC medical informatics and decision making
BACKGROUND: Cephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently in...

The fuzzy system ensembles entomological, epidemiological, demographic and environmental data to unravel the dengue transmission risk in an endemic city.

BMC public health
BACKGROUND: The effectiveness of dengue control interventions depends on an effective integrated surveillance system that involves analysis of multiple variables associated with the natural history and transmission dynamics of this arbovirus. Entomol...

Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study.

BMC medical education
INTRODUCTION: Artificial intelligence (AI) enables machines to perform many complicated human skills which require various levels of human intelligence. In the field of medicine, AI helps physicians in making diagnoses and treatments for patients wit...

Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.

BMC oral health
BACKGROUND: This study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.

An open-source fine-tuned large language model for radiological impression generation: a multi-reader performance study.

BMC medical imaging
BACKGROUND: The impression section integrates key findings of a radiology report but can be subjective and variable. We sought to fine-tune and evaluate an open-source Large Language Model (LLM) in automatically generating impressions from the remain...

An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.

BMC medical research methodology
Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective w...

Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm.

BMC medical research methodology
BACKGROUND: Imbalanced datasets pose significant challenges in predictive modeling, leading to biased outcomes and reduced model reliability. This study addresses data imbalance in diabetes prediction using machine learning techniques. Utilizing data...

Feature-based detection of breast cancer using convolutional neural network and feature engineering.

Scientific reports
Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To impro...