Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Distributional Semantic Models for Clinical Text Applied to Health Record Summarization

As information systems in the health sector are becoming increasingly computerized, large amounts of care-related information are being stored electronically. In hospitals clinicians continuously document treatment and care given to patients in electronic health record (EHR) systems. Much of the information being documented is in the form of clinical notes, or narratives, containing primarily unstructured free-text information. For each care episode, clinical notes are written on a regular basis, ending with a discharge summary that basically summarizes the care episode. Although EHR systems are helpful for storing and managing such information, there is an unrealized potential in utilizing this information for smarter care assistance, as well as for secondary purposes such as research and education. Advances in clinical language processing are enabling computers to assist clinicians in their interaction with the free-text information documented in EHR systems. This includes assisting in tasks like query-based search, terminology development, knowledge extraction, translation, and summarization. This thesis explores various computerized approaches and methods aimed at enabling automated semantic textual similarity assessment and information extraction based on the free-text information in EHR systems. The focus is placed on the task of (semi-)automated summarization of the clinical notes written during individual care episodes. The overall theme of the presented work is to utilize resource-light approaches and methods, circumventing the need to manually develop knowledge resources or training data. Thus, to enable computational semantic textual similarity assessment, word distribution statistics are derived from large training corpora of clinical free text and stored as vector-based representations referred to as distributional semantic models. Also resource-light methods are explored in the task of performing automatic summarization of clinical freetext information, relying on semantic textual similarity assessment. Novel and experimental methods are presented and evaluated that focus on: a) distributional semantic models trained in an unsupervised manner from statistical information derived from large unannotated clinical free-text corpora; b) representing and computing semantic similarities between linguistic items of different granularity, primarily words, sentences and clinical notes; and c) summarizing clinical free-text information from individual care episodes. Results are evaluated against gold standards that reflect human judgements. The results indicate that the use of distributional semantics is promising as a resource-light approach to automated capturing of semantic textual similarity relations from unannotated clinical text corpora. Here it is important that the semantics correlate with the clinical terminology, and with various semantic similarity assessment tasks. Improvements over classical approaches are achieved when the underlying vector-based representations allow for a broader range of semantic features to be captured and represented. These are either distributed over multiple semantic models trained with different features and training corpora, or use models that store multiple sense-vectors per word. Further, the use of structured meta-level information accompanying care episodes is explored as training features for distributional semantic models, with the aim of capturing semantic relations suitable for care episode-level information retrieval. Results indicate that such models performs well in clinical information retrieval. It is shown that a method called Random Indexing can be modified to construct distributional semantic models that capture multiple sense-vectors for each word in the training corpus. This is done in a way that retains the original training properties of the Random Indexing method, by being incremental, scalable and distributional. Distributional semantic models trained with a framework called Word2vec, which relies on the use of neural networks, outperform those trained using the classic Random Indexing method in several semantic similarity assessment tasks, when training is done using comparable parameters and the same training corpora. Finally, several statistical features in clinical text are explored in terms of their ability to indicate sentence significance in a text summary generated from the clinical notes. This includes the use of distributional semantics to enable case-based similarity assessment, where cases are other care episodes and their “solutions”, i.e., discharge summaries. A type of manual evaluation is performed, where human experts rates the different aspects of the summaries using a evaluation scheme/tool. In addition, the original clinician-written discharge summaries are explored as gold standard for the purpose of automated evaluation. Evaluation shows a high correlation between manual and automated evaluation, suggesting that such a gold standard can function as a proxy for human evaluations. --- This thesis has been published jointly with Norwegian University of Science and Technology, Norway and University of Turku, Finland.This thesis has beenpublished jointly with Norwegian University of Science and Technology, Norway.Siirretty Doriast

Semantic concept extraction from electronic medical records for enhancing information retrieval performance

With the healthcare industry increasingly using EMRs, there emerges an opportunity for knowledge discovery within the healthcare domain that was not possible with paper-based medical records. One such opportunity is to discover UMLS concepts from EMRs. However, with opportunities come challenges that need to be addressed. Medical verbiage is very different from common English verbiage and it is reasonable to assume extracting any information from medical text requires different protocols than what is currently used in common English text. This thesis proposes two new semantic matching models: Term-Based Matching and CUI-Based Matching. These two models use specialized biomedical text mining tools that extract medical concepts from EMRs. Extensive experiments to rank the extracted concepts are conducted on the University of Pittsburgh BLULab NLP Repository for the TREC 2011 Medical Records track dataset that consists of 101,711 EMRs that contain concepts in 34 predefined topics. This thesis compares the proposed semantic matching models against the traditional weighting equations and information retrieval tools used in the academic world today

Methodologies of Legacy Clinical Decision Support System -A Review

Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

How to Undertake a Clinically Relevant Systematic Review in a Rapidly Evolving Field: Magnetic Resonance Angiography

Objectives: The aim was to determine which generations of the evolving technology of magnetic resonance angiography (MRA) are currently of clinical relevance in two clinical applications. Our purpose was to plan a systematic review that would be valuable both to purchasers driven by cost-effectiveness and to practicing clinicians. Methods: Information was gathered from a search of major bibliographic databases, from a short questionnaire sent to 500 U.K. vascular radiologists and vascular surgeons, and from local clinical The authors thank A. Jackson and all those who completed a questionnaire. This work was carried out with the financial support of the Secretary of State for Health under the NHS Health Technology Assessment Programme, project 97/13/04. The views and opinions expressed do not necessarily reflect those of the Secretary of State for Health. In part, this work was undertaken by the Leeds Teaching Hospitals NHS Trust, which received funding from the NHS Executive. The views expressed in this publication are those of the authors and not necessarily those of the NHS Executive.experts. We asked which of the MRA techniques were currently used and, assuming availability, what would be their technique of choice. Results: There were 206 published articles that satisfied preliminary inclusion criteria: 69 discussed 2D time of flight (TOF); 47, 3D TOF; and 38, contrast-enhanced techniques. There were 162 questionnaires returned (60 radiologists, 102 surgeons). Of the total respondents, 77/162 (48%) used MRA in the assessment of carotid artery stenosis; 47/77 (61%) used 2D TOF; 32/77 (42%), 3D TOF; and 26/77 (34%), contrast-enhanced techniques. Thirty-five of 162 (22%) respondents used MRA in the assessment of peripheral vascular disease (PVD); 15/35 (43%) used 2D TOF, 4/35 (11%) used 3D TOF, and 22/35 (63%) used contrast-enhanced techniques. For those wishing to use MRA, contrast-enhanced techniques were the method of choice. Conclusions: The TOF methods that represent earlier generations of the technology remain clinically relevant, and will therefore be included in our systematic review. To ensure complete and relevant coverage in reviews of other evolving technologies, it would be advisable to obtain data for guidance in a similar way

A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department

Objectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs

Information Technology for Evidence Based Medicine: Status and Future Direction

Evidence based medicine (EBM) refers to the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. This article presents systematic review of the use of information technology (IT) to support EBM with a particular emphasis on status and opportunities. Out of 2,490 papers initially scanned, 585 articles were included at the title level review. This was followed by an abstract review, which resulted in 196 articles. On full text scanning of the 196 articles, 69 articles met the inclusion criteria and were included in the final analyses. The key issues and potential for IT support for the practice of EBM are insufficient techniques to produce evidence in a computer interpretable format, insufficient research to combine the evidence from the multiple sources, inadequate techniques that automatically rate the literature and practice-based evidence, and integration of evidence at the clinician’s workflow