Semantic medical care in smart cities

Medical care is a vitally important part of successful smart cities further development. High quality medical treatment has always been a challenging task for administrative departments of cities government. The key reason is that the treatment of patients significantly depends on the skills of medical stuff that can hardly be controlled and estimated. Semantic technologies by now have showed capabilities to solve highly complicated badly formalized problems in conditions of uncertainty. It makes reasonable to apply them in medical domain. In the paper a real example of information system for semantic medical care is presented. The system is being developed for Federal Almazov North-West Medical Research Centre in St-Petersburg, Russia (http://www.almazovcentre.ru/?lang=en). The main attention is paid to the proposed solution for the problem of medical treatment estimation in administrative and managerial departments. We focus on medical treatment examinations matching, trend analysis and administrative analytical and prediction task solving making use of semantic technologies, statistical analysis and deep learning applied to huge amounts of diverse data. Semantic medical data analysis project is an attempt to proceed to semantic medicine - an interoperable approach to medical domain area

Utilizing artificial intelligence in perioperative patient flow:systematic literature review

Abstract. The purpose of this thesis was to map the existing landscape of artificial intelligence (AI) applications used in secondary healthcare, with a focus on perioperative care. The goal was to find out what systems have been developed, and how capable they are at controlling perioperative patient flow. The review was guided by the following research question: How is AI currently utilized in patient flow management in the context of perioperative care? This systematic literature review examined the current evidence regarding the use of AI in perioperative patient flow. A comprehensive search was conducted in four databases, resulting in 33 articles meeting the inclusion criteria. Findings demonstrated that AI technologies, such as machine learning (ML) algorithms and predictive analytics tools, have shown somewhat promising outcomes in optimizing perioperative patient flow. Specifically, AI systems have proven effective in predicting surgical case durations, assessing risks, planning treatments, supporting diagnosis, improving bed utilization, reducing cancellations and delays, and enhancing communication and collaboration among healthcare providers. However, several challenges were identified, including the need for accurate and reliable data sources, ethical considerations, and the potential for biased algorithms. Further research is needed to validate and optimize the application of AI in perioperative patient flow. The contribution of this thesis is summarizing the current state of the characteristics of AI application in perioperative patient flow. This systematic literature review provides information about the features of perioperative patient flow and the clinical tasks of AI applications previously identified

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media

The Corpus Expansion Toolkit: finding what we want on the web

This thesis presents the Corpus Expansion Toolkit (CET), a generally applicable toolkit that allows researchers to build domain-specific corpora from the web. The main purpose of the work presented in this thesis and the development of the CET is to provide a solution to discovering desired content on the web from possibly unknown locations or a poorly defined domain. Using an iterative process, the CET is able to solve the problem of discovering domain-specific online content and expand a corpus using only a very small number of example documents or characteristic phrases taken from the target domain. Using a human-in-the-loop strategy and a chain of discrete software components the CET also allows the concept of a domain to be iteratively defined using the very online resources used to expand the original corpus. The CET combines feature extraction, search, web crawling and machine learning methods to collected, store, filter and perform information extraction on collected documents. Using a small number of example ‘seed’ documents the CET is able to expand the original corpus by finding more relevant documents from the web and provide a number of tools to support their analysis. This thesis presents a case study-based methodology that introduces the various contributions and components of the CET through the discussion of five case studies covering a wide variety of domains and requirements that the CET has been applied. These case studies hope to illustrate three main use cases, listed below, where the CET is applicable: 1. Domain known – source known 2. Domain known – source unknown 3. Domain unknown – source unknown First, use cases where the sites for document collection are known and the topic of research is clearly defined. Second, instances where the topic of research is clearly defined but where to find relevant documents on the web is unknown. Third, the most extreme use case, where the domain is poorly defined or unknown to the researcher and the location of the information is also unknown. This thesis presents a solution that allows researchers to begin with very little information on a specific topic and iteratively build a clear conception of a domain and translate that to a computational system

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Applied Mathematics for Pharmaceutical Problems Using Robotics as Assistive Tools for Learning: A Comprehensive Review

Smart machine endures getting smarter as they are going to access more about the facts and pieces of evidence that make our work even more authentic than before. The term “robot” was created in 1920 by Czechoslovakian playwright Karel Capek and has been a principal point in science fiction ever since. Pharmacy automation involves machine-driven or mechanical processes of distributing, dispensing and managing medications. Pharmaceutical organizations take advantage of robotics to manoeuvre biological or chemical samples around to integrate novel chemical structure or to test the pharmaceutical value of remaining organic material. Pharmaceutical applications with aid of robotic systems are progressively accepted for enhanced throughput and proficiency to satisfy this growing demand, within a rapidly ageing population that directly requires sophisticated medical devices and newer drugs. According to Robot IQ, mathematics is one of the few main robotics attributes that cannot be learned along the way. A good background in many fields of mathematics and science is needed for robotics at the very least. Several studies have shown that robotics is an effective medium for teaching STEM (Science, Technology, Engineering, and Mathematics) skills to students. Thus, Novel methods are under development in machine learning, symbolic reasoning and signal processing which may be utilized in production and packaging concerned to the pharmaceuticals. The target is to review the Planning, Safety, Reliability, Accuracy, Quality, Flexibility, Redeployment, Efficiency and other vital applications of Robotics in Pharmacy

Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards