AI Medical Compendium Journal:
BMC medical informatics and decision making

Showing 121 to 130 of 718 articles

Assessing the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in making patient-specific drug-therapy and healthcare-related decisions.

BMC medical informatics and decision making
BACKGROUND: Interactive artificial intelligence tools such as ChatGPT have gained popularity, yet little is known about their reliability as a reference tool for healthcare-related information for healthcare providers and trainees. The objective of t...

Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.

BMC medical informatics and decision making
BACKGROUND: The detection and classification of lung nodules are crucial in medical imaging, as they significantly impact patient outcomes related to lung cancer diagnosis and treatment. However, existing models often suffer from mode collapse and po...

Automated redaction of names in adverse event reports using transformer-based neural networks.

BMC medical informatics and decision making
BACKGROUND: Automated recognition and redaction of personal identifiers in free text can enable organisations to share data while protecting privacy. This is important in the context of pharmacovigilance since relevant detailed information on the cli...

A prior-knowledge-guided dynamic attention mechanism to predict nocturnal hypoglycemic events in type 1 diabetes.

BMC medical informatics and decision making
Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source ...

Application of deep learning in wound size measurement using fingernail as the reference.

BMC medical informatics and decision making
OBJECTIVE: Most current wound size measurement devices or applications require manual wound tracing and reference markers. Chronic wound care usually relies on patients or caregivers who might have difficulties using these devices. Considering a more...

Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.

BMC medical informatics and decision making
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BMC medical informatics and decision making
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...

Target informed client recruitment for efficient federated learning in healthcare.

BMC medical informatics and decision making
BACKGROUND: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available ...

Uncertainty-aware automatic TNM staging classification for [F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning.

BMC medical informatics and decision making
BACKGROUND: [F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised ...

An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

BMC medical informatics and decision making
BACKGROUND: Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explain...