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Quality Control

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Ontology-based metabolomics data integration with quality control.

Bioanalysis
 The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. This p...

Label-Free Identification of White Blood Cells Using Machine Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state-of-the-art method for determining WBC differential cou...

Importance of coding co-morbidities for APR-DRG assignment: Focus on cardiovascular and respiratory diseases.

Health information management : journal of the Health Information Management Association of Australia
BACKGROUND: The All Patient-Refined Diagnosis-Related Groups (APR-DRGs) system has adjusted the basic DRG structure by incorporating four severity of illness (SOI) levels, which are used for determining hospital payment. A comprehensive report of all...

Interactive biomedical ontology matching.

PloS one
Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems,...

Simulation program of a cytotoxic compounding robot for monoclonal antibodies and anti-infectious sterile drug preparation.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
The aim of this study was to develop a specific simulation program for the validation of a cytotoxic compounding robot, KIRO® Oncology, for the preparation of sterile monoclonal antibodies and anti-infectious drugs. The impact of excipient formulatio...

Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data.

NeuroImage
Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality ...

Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning.

Medical physics
PURPOSE: Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output p...

Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks.

Medical physics
PURPOSE: Patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) is a ubiquitous clinical procedure, but conventional methods have often been criticized as being insensitive to errors or less effective than other comm...

Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

Practical radiation oncology
PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectiv...