Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develop a novel graph-based framework for ADR signal detection using healthcare claims data. We construct a Drug-disease graph with nodes representing the medical codes. The edges are given as the relationships between two codes, computed using the data. We apply Graph Neural Network to predict ADR signals, using labels from the Side Effect Resource database. The model shows improved AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms, showing that it successfully learns node representations expressive of those relationships. Furthermore, our model predicts ADR pairs that do not exist in the established ADR database, showing its capability to supplement the ADR database.Comment: To appear at PAKDD 202

Health System Support for Childbirth care in Southern Tanzania: Results from a Health Facility Census.

Progress towards reaching Millennium Development Goals four (child health) and five (maternal health) is lagging behind, particularly in sub-Saharan Africa, despite increasing efforts to scale up high impact interventions. Increasing the proportion of birth attended by a skilled attendant is a main indicator of progress, but not much is known about the quality of childbirth care delivered by these skilled attendants. With a view to reducing maternal mortality through health systems improvement we describe the care routinely offered in childbirth offered at dispensaries, health centres and hospitals in five districts in rural Southern Tanzania. We use data from a health facility census assessing 159 facilities in five districts in early 2009. A structural and operational assessment was undertaken based on staff reports using a modular questionnaire assessing staffing, work load, equipment and supplies as well as interventions routinely implemented during childbirth. Health centres and dispensaries attended a median of eight and four deliveries every month respectively. Dispensaries had a median of 2.5 (IQR 2--3) health workers including auxiliary staff instead of the recommended four clinical officer and certified nurses. Only 28% of first-line facilities (dispensaries and health centres) reported offering active management in the third stage of labour (AMTSL). Essential childbirth care comprising eight interventions including AMTSL, infection prevention, partograph use including foetal monitoring and newborn care including early breastfeeding, thermal care at birth and prevention of ophthalmia neonatorum was offered by 5% of dispensaries, 38% of health centres and 50% of hospitals consistently. No first-line facility had provided all signal functions for emergency obstetric complications in the previous six months. Essential interventions for childbirth care are not routinely implemented in first-line facilities or hospitals. Dispensaries have both low staffing and low caseload which constraints the ability to provide high-quality childbirth care. Improvements in quality of care are essential so that women delivering in facility receive "skilled attendance" and adequate care for common obstetric complications such as post-partum haemorrhage

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution

Preparation and Application of Electrodes in Capacitive Deionization (CDI): a State-of-Art Review

As a promising desalination technology, capacitive deionization (CDI) have shown practicality and cost-effectiveness in brackish water treatment. Developing more efficient electrode materials is the key to improving salt removal performance. This work reviewed current progress on electrode fabrication in application of CDI. Fundamental principal (e.g. EDL theory and adsorption isotherms) and process factors (e.g. pore distribution, potential, salt type and concentration) of CDI performance were presented first. It was then followed by in-depth discussion and comparison on properties and fabrication technique of different electrodes, including carbon aerogel, activated carbon, carbon nanotubes, graphene and ordered mesoporous carbon. Finally, polyaniline as conductive polymer and its potential application as CDI electrode-enhancing materials were also discussed

Green and Sustainable Method to Improve Fixation of a Natural Functional Dye onto Cotton Fabric Using Cationic Dye-Fixing Agent/D5 Microemulsion

The low fixation rate and colorfastness of natural dyes limit their practical application in modern textile coloration. Further, hazardous mordants are used in conventional natural dyeing to achieve better fixation and colorfastness. Herein, a green, sustainable, and environmental benign fixation process of natural dye was developed using a non-aqueous medium in the absence of mordants to enhance the fixation rate and colorfastness of the dyed fabric. The process was executed by the treatment of the cacao husk extracts/decamethylcyclopentasiloxane (D5) dyed cotton fabric with a cationic dye-fixation agent (CFA)/D5 microemulsion. The conditions of optimal dye fixation process including water content, fixation time, fixation temperature, and CFA mass were determined by using L-9 orthogonal array. Significant improvements in the fixation rate (95.03%) and color strength (15.26) were found after CFA treatment under the optimal conditions. Although the light fastness of the CFA-treated dyed fabrics was poor, the colorfastness to rubbing and washing were remarkable. The cacao husk extracts natural functional dye significantly enhanced the UV resistance of the dyed fabric, and the CFA treatment improved the crease recovery characteristic of the dyed fabric. The stiffness of the fabric decreased slightly after dyeing and CFA treatment. Consequently, this study paved the way for the sustainable and green dyeing process

Toward improved performance of reactive dyeing on cotton fabric using process sensitivity analysis

Purpose The conventional textile dyeing process requires various operational characteristics, and determining the most reliable factor in dyeing performance has always been a challenge for the textile industry. Thus, the present paper aimed to evaluate the process sensitivity of C. I. Reactive Blue 194 dyeing of cotton fabric using a statistical technique. Design/methodology/approach An L-27 orthogonal array-based Taguchi's methodology was used with six parameters and three levels of each parameter. The signal-to-noise (S/N) ratio and analysis of variance were studied using total fixation efficiency (T%) as the response of the process sensitivity. Findings Results showed that dyebath pH was the most influential factor on the process and total fixation efficiency (p-value = 0.00 and contribution percentage 45.03%), followed by dye-fixing temperature, dye mass, electrolyte concentration, dye-fixing time and material to liquor ratio. Originality/value Overall this study provides a foundation for the determination of dyeing process sensitivity that will be useful in textile industries toward further development

Clustering Diagnostic Profiles of Patients

Part 4: Biomedical AIInternational audienceElectronic Health Records provide a wealth of information about the care of patients and can be used for checking the conformity of planned care, computing statistics of disease prevalence, or predicting diagnoses based on observed symptoms, for instance. In this paper, we explore and analyze the recorded diagnoses of patients in a hospital database in retrospect, in order to derive profiles of diagnoses in the patient database. We develop a data representation compatible with a clustering approach and present our clustering approach to perform the exploration. We use a k-means clustering model for identifying groups in our binary vector representation of diagnoses and present appropriate model selection techniques to select the number of clusters. Furthermore, we discuss possibilities for interpretation in terms of diagnosis probabilities, in the light of external variables and with the common diagnoses occurring together