An online belief rule-based group clinical decision support system

Around ten percent of patients admitted to National Health Service (NHS) hospitals have experienced a patient safety incident, and an important reason for the high rate of patient safety incidents is medical errors. Research shows that appropriate increase in the use of clinical decision support systems (CDSSs) could help to reduce medical errors and result in substantial improvement in patient safety. However several barriers continue to impede the effective implementation of CDSSs in clinical settings, among which representation of and reasoning about medical knowledge particularly under uncertainty are areas that require refined methodologies and techniques. Particularly, the knowledge base in a CDSS needs to be updated automatically based on accumulated clinical cases to provide evidence-based clinical decision support. In the research, we employed the recently developed belief Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) for design and development of an online belief rule-based group CDSS prototype. In the system, belief rule base (BRB) was used to model uncertain clinical domain knowledge, the evidential reasoning (ER) approach was employed to build inference engine, a BRB training module was developed for learning the BRB through accumulated clinical cases, and an online discussion forum together with an ER-based group preferences aggregation tool were developed for providing online clinical group decision support.We used a set of simulated patients in cardiac chest pain provided by our research collaborators in Manchester Royal Infirmary to validate the developed online belief rule-based CDSS prototype. The results show that the prototype can provide reliable diagnosis recommendations and the diagnostic performance of the system can be improved significantly after training BRB using accumulated clinical cases.EThOS - Electronic Theses Online ServiceManchester Business SchoolGBUnited Kingdo

Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data

This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were compared, and an end-to-end multivariate time series clustering system called Time2Feat, combined with K-Means, was chosen as the most effective method to cluster patients in the ICU. In clustering analysis, data of 8,080 patients admitted between 2008 and 2016 was used for model development and 2,038 patients admitted between 2017 and 2019 for model validation. By analyzing the differences in clinical mortality prognosis among different categories, varying risks of ICU mortality and hospital mortality were found between different subgroups. Furthermore, the study visualized the trajectory of vital signs changes. The findings of this study provide valuable insights into the potential use of multivariate time-series clustering systems in patient management and monitoring in the ICU setting.Comment: Proceedings of Beijing Health Data Science Summit (HDSS) 202

Differential proteomic profiles and characterizations between hyalinocytes and granulocytes in ivory shell Babylonia areolata

Abstract(#br)The haemocytes of the ivory shell, Babylonia areolata are classified by morphologic observation into the following types: hyalinocytes (H) and granulocytes (G). Haemocytes comprise diverse cell types with morphological and functional heterogene and play indispensable roles in immunological homeostasis of invertebrates. In the present study, two types of haemocytes were morphologically identified and separated as H and G by Percoll density gradient centrifugation. The differentially expressed proteins were investigated between H and G using mass spectrometry. The results showed that total quantitative proteins between H and G samples were 1644, the number of up-regulated proteins in G was 215, and the number of down-regulated proteins in G was 378. Among them, cathepsin, p38 MAPK, toll-interacting protein-like and beta-adrenergic receptor kinase 2-like were up-regulated in G; alpha-2-macroglobulin-like protein, C-type lectin, galectin-2-1, galectin-3, β-1,3-glucan-binding protein, ferritin, mega-hemocyanin, mucin-17-like, mucin-5AC-like and catalytic subunit of protein kinase A were down-regulated in G. The results showed that the most significantly enriched KEGG pathways were the pathways related to ribosome, phagosome, endocytosis, carbon metabolism, protein processing in endoplasmic reticulum and oxidative phosphorylation. For phagosome and endocytosis pathway, the number of down-regulation proteins in G was more than that of up-regulation proteins. For lysosome pathway, the number of up-regulation proteins in G was more than that of down-regulation proteins. These results suggested that two sub-population haemocytes perform the different immune functions in B. areolata

Efficiency Spillovers of Foreign Direct Investment in the Chinese Banking System

This research suggests that the efficiency spillovers of foreign banks in China have played an integral role in transforming the fragile banking system into a relatively robust one. Stochastic frontier analysis is employed to analyze the efficiency of the Chinese banks during 2001-2007 with the data of 126 banks. The study suggests that the presence of foreign banks has helped improve the overall profitability of the Chinese banks and has also effectively constrained the scale of loans. The findings manifest the importance of efficiency spillovers which can be brought to an economy in transition by cautious financial liberalization.Commercial bank, efficiency, spillover, foreign direct investment, stochastic frontier analysis,

Clinical decision support systems: A review on knowledge representation and inference under uncertainties

This paper provides a literature review in clinical decision support systems (CDSSs) with a focus on the way knowledge bases are constructed, and how inference mechanisms and group decision making methods are used in CDSSs. Particular attention is paid to the uncertainty handling capability of the commonly used knowledge representation and inference schemes. The definition of what constitute good CDSSs and how they can be evaluated and validated are also considered. Some future research directions for handling uncertainties in CDSSs are proposed