Predicting risk of hospital readmission for comorbidity patients through a novel deep learning framework

Hospital readmission is widely recognized as indicator of inpatient quality of care which has significant impact on healthcare cost. Thus, early recognition of readmission risk has been of growing interest in various hospitals. Additionally, there has been growing attention to provide better care to patients with more complications, whose care would impact the quality of care in multiple directions. To this regard, this research specifically targets comorbidity patients i.e., the patients with chronic disease. This research proposes a novel deep learning- framework termed SDAE-GAN. The presented approach consists of three phases. Firstly, various groups of variables from heterogeneous sources are collated. These variables mainly include demographic, socioeconomic, some statistics about patient’s frequent admissions and their diagnosis codes. Then, more processing applies dealing missing values, digitization and data balancing. Afterwards, stacked denoising auto-encoders function to learn underlying representation; and technically to forms a latent space. The latent variables then are used by a Generative Adversarial Neural Networks to evaluate the risk of 30- day readmission. The model is fine-tuned and being compared with state-of-the-arts. Experimental results exhibit competitive performance with higher sensitivity

A closed-loop supply chain operation problem under different recycling modes and patent licensing strategies

A closed-loop supply chain operation is an effective way to improve the dual benefits of economy and environment. Inspired by the practice of closed-loop supply chain coordination, this paper attempts to investigate the supply chain operation strategies of different recycling modes and patent licensing strategies and consider the impact of government subsidies. We construct a multi-player game model of an original manufacturer, a remanufacturer, and a retailer under a waste product recycling mode, patent licensing strategy, and government subsidy system. We provide the operation strategies under different strategy combinations in the closed-loop supply chain, and then analyze their differences and the interests of various subjects. We further analyze the impact of government subsidies on the operation strategies of the supply chain. The results illustrate that when the original manufacturer adopts the fixed fee patent licensing strategy, the recycling price of waste products is higher. When the original manufacturer adopts the unit fee patent licensing strategy and the retailer and the remanufacturer participate in the recycling of waste products simultaneously, the original manufacturer will increase the unit patent licensing fee. When the remanufacturer recycles waste products alone, consumers can obtain greater unit income in the waste product recycling market. Different government subsidies have different effects on the wholesale price and the retail price of new products and remanufactured products

Cooperative advertising strategy selection problem for considering pricing and advertising decisions in a two-period online supply chain

This article studies the cooperative advertising problem of a two-period online supply chain consisting of a manufacturer and an online retail platform. The manufacturer provides national advertising in the first period to build the brand image and increase the awareness of the product. And the online retail platform provides platform advertising for selling the product to consumers on its platform during two periods. The manufacturer and the online retail platform may choose different cooperative advertising strategies for national advertising and platform advertising, which are one-way subsidy strategy, two-way subsidy strategy, and revenue-share strategy. We formulate a Stackelberg game model to study the cooperative advertising problem by taking price and advertising effect into account and analyze how the profit is influenced in different cooperative advertising strategies. We find that under the revenue-share strategy, the manufacturer provides a higher subsidy rate for the online retail platform advertising than that in other cooperative advertising strategies. Interestingly, there are conditions where, while just the manufacturer contributes a percentage of the platform advertising and the online retail platform has no effort on the national advertising, the total profit would be better than that in revenue-share strategy even in revenue-share strategy, the cooperative relationship is closer between the manufacture and the online retail platform

Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

AI3SD Video: Introducing the Future Blood Testing Network

The Future Blood Testing Network+ is a new Network funded by EPSRC. We are aiming to build a multi-disciplinary community to develop digital health technologies for remote, rapid, affordable and inclusive monitoring and personalised analytics. This presentation will introduce our Network, detailing our plans for the next three years, in particular highlighting the funding calls and opportunities that will be relevant to the AI4SD Community

Best practice of "Internet+" hospital: seamless medical services across whole process

Due to the "Internet+" environment represented by cloud computing, big data, mobile applications, the Internet of things, social media and other technologies, and the urgent need of improving the medical experience with new information technology by medical institutions, a large number of mobile medical applications emerge. However, these applications have serious homogeneity, confusing entry, failure to use medical core process and other problems. With poor experience, it is difficult to take advantage of the Internet in improving the medical service level. The hospital "Internet+" practice implementation method with the core of process is proposed. With the mobile Internet information technology, the hospital service flow is integrated, so as to realize efficient medical service. With the example of mobile Internet digital hospital of The Central Hospital of Wuhan, the value and effect of the seamless medical service with the core of the entire process in the hospital process optimization are summarized

Organisational responsiveness through signs

Part 4: Knowledge Management and EngineeringInternational audienceOrganisational Semiotics is a discipline that studies signs of organisations, and how these signs aid an analysis and design of technical information systems. The responsiveness of organisations has been discussed as a key feature to adapt the organisational behaviour in turbulent environments, but there are not studies about the implication of these capabilities on Information Systems. Organisational Semiotics can be used as a good approach to understand relationships between living things and organisations in order to develop their responses to the organisation. The purpose of this paper is the proposition of a preliminary model of organisations as living systems, using the concept of organisations as information systems. This paper articulates the information interactions between the border of the system, the environment and the activities of the organisation that maintain this border by methods of organisational semiotics. The future study will focus on the implementation of the proposed framework

Adaptive requirement-driven architecture for integrated healthcare systems

In order to improve the quality of healthcare services, large-scale medical information systems should be integrated with adaptability in response to the changing medical environment. In this paper, we propose a requirement-driven architecture for healthcare information systems that will be able to respond to new requirements. The system operates through the mapping mechanism between these layers and thus can organize functions dynamically adapting to user’s requirement. Furthermore, we introduce the organizational semiotics methods to capture and analyze user’s requirement through ontology chart and norms. Based on these results, the structure of user’s requirement pattern (URP) is established as the driving factor of our system. Finally, we propose an integration framework for data sharing amongst different hospital organizations and also present the HL7 based virtual database to realize the data integration. Our research makes a contribution to the design and implementation of the architecture of healthcare systems which can adapt to the changing medical environment