The Hidden Costs of Behavioral and Psychiatric Emergencies.

Emerg Med Clin North Am

Section of Emergency Medicine, Department of Medicine, Baylor College of Medicine, 1504 Taub Loop, Houston, TX 77030, USA. Electronic address:

Published: November 2015

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.emc.2015.09.001DOI Listing

Publication Analysis

Top Keywords

hidden costs
4
costs behavioral
4
behavioral psychiatric
4
psychiatric emergencies
4
hidden
1
behavioral
1
psychiatric
1
emergencies
1

Similar Publications

Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.

View Article and Find Full Text PDF

Cost analysis of hidden hepatitis D virus infection in Spain.

Gastroenterol Hepatol

January 2025

Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, España. Electronic address:

Introduction: A significant percentage of patients coinfected with hepatitis B virus (HBV) and hepatitis D virus (HDV) are undiagnosed. Coinfected patients progress to advanced liver disease faster than HBV monoinfected patients, thereby consuming more healthcare resources. The aim was to perform an analysis to determine the cost of hidden HDV infection in Spain.

View Article and Find Full Text PDF

In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior.

View Article and Find Full Text PDF

The biomedical applications of artificial intelligence: an overview of decades of research.

J Drug Target

January 2025

Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India.

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact.

View Article and Find Full Text PDF

Incremental broad learning system (IBLS) is an effective and efficient incremental learning method based on broad learning paradigm. Owing to its streamlined network architecture and flexible dynamic update scheme, IBLS can achieve rapid incremental reconstruction on the basis of the previous model without the entire retraining from scratch, which enables it adept at handling streaming data. However, two prominent deficiencies still persist in IBLS and constrain its further promotion in large-scale data stream scenarios.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!