To Tweak or Not to Tweak. How Exploiting Flexibilities in Gene Set Analysis Leads to Overoptimism.

Gene set analysis, a popular approach for analyzing high-throughput gene expression data, aims to identify sets of genes that show enriched expression patterns between two conditions. In addition to the multitude of methods available for this task, users are typically left with many options when creating the required input and specifying the internal parameters of the chosen method. This flexibility can lead to uncertainty about the "right" choice, further reinforced by a lack of evidence-based guidance.

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

Background: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.

Methods: This study aims to determine whether a machine learning (ML) model can be trained to predict the need for blood transfusion (BT) in patients on the ICU after a wide range of surgeries, utilizing only data from the ICU.

On-site electronic consent in pediatrics using generic Informed Consent Service (gICS): Creating a specialized setup and collecting consent data.

Enrolling in a clinical trial or study requires informed consent. Furthermore, it is crucial to ensure proper consent when storing samples in biobanks for future research, as these samples may be used in studies beyond their initial purpose. For pediatric studies, consent must be obtained from both the child and their legal guardians, requiring the recording of multiple consents at once.

Simplifying Multiparty Computation: A Client-Driven Metaprotocol for Federated Secure Computing.

Introduction: Secure Multi-Party Computation (SMPC) offers a powerful tool for collaborative healthcare research while preserving patient data privacy.

State Of The Art: However, existing SMPC frameworks often require separate executions for each desired computation and measurement period, limiting user flexibility.

Concept: This research explores the potential of a client-driven metaprotocol for the Federated Secure Computing (FSC) framework and its SImple Multiparty ComputatiON (SIMON) protocol as a step towards more flexible SMPC solutions.

Optimizing Data Extraction: Harnessing RAG and LLMs for German Medical Documents.

In the field of medical data analysis, converting unstructured text documents into a structured format suitable for further use is a significant challenge. This study introduces an automated local deployed data privacy secure pipeline that uses open-source Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) architecture to convert medical German language documents with sensitive health-related information into a structured format. Testing on a proprietary dataset of 800 unstructured original medical reports demonstrated an accuracy of up to 90% in data extraction of the pipeline compared to data extracted manually by physicians and medical students.