This paper tries to synthesize the discussion of a seminar on medical informatics educational tasks held in May 1998 in Sinaia, Romania, within the frame of the Tempus-Phare Project CME-02555-96 entitled "Know How Transfer from University to Industry" and coordinated by the University of Medicine and Pharmacy Timisoara, Romania. Special emphasis was paid to particular features of medical education requirements in East European countries, with particular reference to Romania.

Download full-text PDF

Source

Publication Analysis

Top Keywords

medical informatics
8
informatics educational
8
educational tasks
8
tempus-phare project
8
project cme-02555-96
8
tasks practical
4
practical perspective
4
perspective tempus-phare
4
cme-02555-96 paper
4
paper synthesize
4

Similar Publications

Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.

View Article and Find Full Text PDF

Background: Health authorities worldwide have invested in digital technologies to establish robust information exchange systems for improving the safety and efficiency of medication management. Nevertheless, inaccurate medication lists and information gaps are common, particularly during care transitions, leading to avoidable harm, inefficiencies, and increased costs. Besides fragmented health care processes, the inconsistent incorporation of patient-driven changes contributes to these problems.

View Article and Find Full Text PDF

Background: In data-sparse areas such as health care, computer scientists aim to leverage as much available information as possible to increase the accuracy of their machine learning models' outputs. As a standard, categorical data, such as patients' gender, socioeconomic status, or skin color, are used to train models in fusion with other data types, such as medical images and text-based medical information. However, the effects of including categorical data features for model training in such data-scarce areas are underexamined, particularly regarding models intended to serve individuals equitably in a diverse population.

View Article and Find Full Text PDF

Background: Information exchange regarding the scope and content of health studies is becoming increasingly important. Digital methods, including study websites, can facilitate such an exchange.

Objective: This scoping review aimed to describe how digital information exchange occurs between the public and researchers in health studies.

View Article and Find Full Text PDF

Multidimensional scaling improves distance-based clustering for microbiome data.

Bioinformatics

January 2025

Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, WI 53726, United States.

Motivation: Clustering patients into subgroups based on their microbial compositions can greatly enhance our understanding of the role of microbes in human health and disease etiology. Distance-based clustering methods, such as partitioning around medoids (PAM), are popular due to their computational efficiency and absence of distributional assumptions. However, the performance of these methods can be suboptimal when true cluster memberships are driven by differences in the abundance of only a few microbes, a situation known as the sparse signal scenario.

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!