AI Medical Compendium Topic:
Reproducibility of Results

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Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hamp...

Global prediction of extreme floods in ungauged watersheds.

Nature
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simula...

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Chemical research in toxicology
Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential D...

Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks.

Water science and technology : a journal of the International Association on Water Pollution Research
The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an i...

Drug Repositioning via Graph Neural Networks: Identifying Novel JAK2 Inhibitors from FDA-Approved Drugs through Molecular Docking and Biological Validation.

Molecules (Basel, Switzerland)
The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling...

Inter-reviewer reliability of human literature reviewing and implications for the introduction of machine-assisted systematic reviews: a mixed-methods review.

BMJ open
OBJECTIVES: Our main objective is to assess the inter-reviewer reliability (IRR) reported in published systematic literature reviews (SLRs). Our secondary objective is to determine the expected IRR by authors of SLRs for both human and machine-assist...

Predicting the onset of end-stage knee osteoarthritis over two- and five-years using machine learning.

Seminars in arthritis and rheumatism
OBJECTIVE: Identifying participants who will progress to advanced stage in knee osteoarthritis (KOA) trials remains a significant challenge. Current tools, relying on total knee replacements (TKR), fall short in reliability due to the extraneous fact...

Accuracy of machine learning in the preoperative identification of ovarian borderline tumors: a meta-analysis.

Clinical radiology
AIM: The objective of this study is to explore the diagnostic value of machine learning (ML) in borderline ovarian tumors through meta-analysis.

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).

Sensors (Basel, Switzerland)
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Glob...

Checklist for Reproducibility of Deep Learning in Medical Imaging.

Journal of imaging informatics in medicine
The application of deep learning (DL) in medicine introduces transformative tools with the potential to enhance prognosis, diagnosis, and treatment planning. However, ensuring transparent documentation is essential for researchers to enhance reproduc...