Narrow Results

This paper presents a software tool called AVID (A VIsualization and Design) which is particularly useful for data mining with an artificial neural network known as the self-organising feature map (SOM). AVID supports network training in both the i) selection of network inputs and ii) visualisation of the trained SOM. Both these features are novel aids to SOM network training and are particularly important when consideration is given to using the SOM for data mining. Once trained the SOM produces a 2-dimensional topological ordering of the input training data and it is particularly useful for representing the relationships within multi-dimensional data. The main classes within the data can be identified from the output map. AVID is an important software tool which enables data mining with the SOM by the selection of network inputs and the subsequent visualisation of the classes within these input vectors.

Innovation projects are prone to “escalation of commitment” (the tendency to continue projects even if it is clear that they will be unsuccessful). In this study, we introduce a construct measuring the escalation prevention potential (EPP) of innovation projects as perceived by individuals in the organization. EPP consists of three components: (i) goals, (ii) process, and (iii) ability, to shield projects from escalation to commitment. A survey was conducted among 1062 clinicians working in hospitals implementing Electronic Medical Records. The empirical results show that the three theoretical components of EPP sum up to a single measure. Four organizational characteristics of organizations (organizational routines, authentic leadership, employee involvement and support staff quality) explain a large share of the variation in EPP.

Innovation projects are prone to “escalation of commitment” (the tendency to continue projects even if it is clear that they will be unsuccessful). In this study, we introduce a construct measuring the escalation prevention potential (EPP) of innovation projects as perceived by individuals in the organization. EPP consists of three components: (1) goals, (2) process, and (3) ability, to shield projects from escalation to commitment. A survey was conducted among 1,062 clinicians working in hospitals implementing electronic medical records (EMRs). The empirical results show that the three theoretical components of EPP sum up to a single measure. Four organizational characteristics of organizations (organizational routines, leadership reflexivity, employee involvement, and support staff quality) explain a large share of the variation in EPP.

Identification and subsequent intervention of patients at risk of becoming High Cost Users (HCUs) presents the opportunity to improve outcomes while also providing significant savings for the healthcare system. In this paper, the 2016 HCU status of patients was predicted using free-form text data from the 2015 cumulative patient profiles within the electronic medical records of family care practices in Ontario. These unstructured notes make substantial use of domain-specific spellings and abbreviations; we show that word embeddings derived from the same context provide more informative features than pre-trained ones based on Wikipedia, MIMIC, and Pubmed. We further demonstrate that a model using features derived from aggregated word embeddings (EmbEncode) provides a significant performance improvement over the bag-of-words representation (82.48±0.35% versus 81.85±0.36% held-out AUROC, p = 3.2 × 10⁻⁴), using far fewer input features (5,492 versus 214,750) and fewer non-zero coefficients (1,177 versus 4,284). The future HCUs of greatest interest are the transitional ones who are not already HCUs, because they provide the greatest scope for interventions. Predicting these new HCU is challenging because most HCUs recur. We show that removing recurrent HCUs from the training set improves the ability of EmbEncode to predict new HCUs, while only slightly decreasing its ability to predict recurrent ones.

Blockchain technology has attained importance in the field of health as it overcomes the challenges in securing EHR (Electronic Health Records) as well as EMR (Electronic Medical Records) in eHealth systems. Distributed nature of the technology produces a single ecosystem of patient information that can be monitored more efficiently and quickly by doctors, pharmacists, and hospitals or anyone who diagnosis or gives treatment. Thus, blockchain provides faster diagnoses and plans to care personally. Blockchain technology is used to securely store digital health records and maintain the source record to protect and preserve the identity of patients. This chapter aims to integrate secure data from IoT devices to clearly understand the effects of blockchain in the real environment field. A novel blockchain approach is designed for eHealth and is employed to discover different ways of sharing decentralized view of health information and improve medical accuracy, health, and prevent health disorders.

Catering to the healthcare needs of seven billion people poses unique challenges and opportunities globally. The provision of the basic right to healthcare is the moral responsibility of the government toward its citizens. Although access to health information is crucial, the emphasis must be on the quality of the data being collected that will guide the decisions of our policymakers. We must and should adopt the best technology frameworks that are relevant to our geography without compromising the volume of care that is currently being delivered. With the changing landscape of both lifestyle and the trend of diseases over the years, it is imperative that we need to have the right information at the right time to make the right decision for the right individual. The implementation of digital health is a key step in the direction that ensures the availability, accessibility, affordability, and acceptability of healthcare for the masses. There has never been a more opportune time such as this to lay down a strong foundation to enable the collection of “good quality” digital health data points to guide us in the future to prepare for any challenge, such as the COVID-19 pandemic.

In this paper, we present VisAGE, a method that visualizes electronic medical records (EMRs) in a low-dimensional space. Effective visualization of new patients allows doctors to view similar, previously treated patients and to identify the new patients’ disease subtypes, reducing the chance of misdiagnosis. However, EMRs are typically incomplete or fragmented, resulting in patients who are missing many available features being placed near unrelated patients in the visualized space. VisAGE integrates several external data sources to enrich EMR databases to solve this issue. We evaluated VisAGE on a dataset of Parkinson’s disease patients. We qualitatively and quantitatively show that VisAGE can more effectively cluster patients, which allows doctors to better discover patient subtypes and thus improve patient care.