Development and Implementation of Clinical Decision Algorithms for Oncology.

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge.

A novel method for continuous measurements of clinical practice guideline adherence.

Introduction: Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels.

Conversion of a colorectal cancer guideline into clinical decision trees with assessment of validity.

Objective: The interpretation and clinical application of guidelines can be challenging and time-consuming, which may result in noncompliance to guidelines. The aim of this study was to convert the Dutch guideline for colorectal cancer (CRC) into decision trees and subsequently implement decision trees in an online decision support environment to facilitate guideline application.

Methods: The recommendations of the Dutch CRC guidelines (published in 2014) were translated into decision trees consisting of decision nodes, branches and leaves that represent data items, data item values and recommendations, respectively.

Clinical decision trees support systematic evaluation of multidisciplinary team recommendations.

Purpose: EUSOMA's recommendation that "each patient has to be fully informed about each step in the diagnostic and therapeutic pathway" could be supported by guideline-based clinical decision trees (CDTs). The Dutch breast cancer guideline has been modeled into CDTs ( www.oncoguide.

Transformation of the National Breast Cancer Guideline Into Data-Driven Clinical Decision Trees.

Purpose: The essence of guideline recommendations often is intertwined in large texts. This impedes clinical implementation and evaluation and delays timely modular revisions needed to deal with an ever-growing amount of knowledge and application of personalized medicine. The aim of this project was to model guideline recommendations as data-driven clinical decision trees (CDTs) that are clinically interpretable and suitable for implementation in decision support systems.

NEUROD1 acts in vitro as an upstream regulator of NEUROD2 in trophoblast cells.

The basic helix-loop-helix (bHLH) transcription factors NEUROD1, NEUROD2 and ATH2 are expressed during first trimester human placental development. We determined the transactivation potential of each of these factors in trophoblasts by measuring changes in the endogenous gene activity using absolute quantification by real-time quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) after transient transfection. In these assays, NEUROD1 was found to transiently transactivate NEUROD2 in trophoblast cells.