Download or read book A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care written by Kamran Farooq and published by GRIN Verlag. This book was released on 2016-06-16 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2015 in the subject Computer Sciences - Artificial Intelligence, grade: -, University of Stirling (Computing Science and Mathematics), language: English, abstract: Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases.

Download or read book Hybrid Recommender Framework for the Design of Intelligent Clinical Decision Support Tools written by Luis M. Ahumada and published by . This book was released on 2016 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Physicians are constantly faced with making decisions under uncertainty, and despite the extraordinary advancements in the field of clinical informatics, there is a significant void about how to build simple and trustworthy clinical decision support systems. This dissertation focusses on investigating whether a hybrid recommender framework approach can exceed conventional data analysis techniques in order to provide physicians with accurate insights. The research questions explored a novel hybrid recommender framework that improves upon common clinical recommendation practices such as data driven, case base reasoning and machine learning techniques by integrating them into a unified data model. Conceptually this study was framed within theories of probability, numerical analysis, case base reasoning, machine learning, clinical decision support and recommendation systems. Experiments demonstrate that the proposed hybrid recommender framework is more accurate and effective than common baseline techniques. We evaluate the framework by implementing a prototype and experimenting with an outstanding clinical problem: how to reduce the number of unnecessary pre-operative blood tests for pediatric neurosurgical patients. We analyze heterogeneous databases containing 359,475 patient encounters at The Children's Hospital of Philadelphia from 2001 to 2014. Experimental analysis shows that our hybrid approach has a sensitivity of 0.80, a specificity of 0.85 and a mean absolute error of 0.875. Finally, we demonstrate preliminary results of a real-world implementation by embedding the recommendations into the physician's workflow in the production environment of the hospital's electronic health record. The application shows a reduction of ordering unnecessary tests by ~ 25% in the first quarter of 2016 and a 100% adoption rate by the user base. This result suggests that our approach helps in improving the quality of physician's decisions with a positive impact on outcomes.

Download or read book Decision Support Framework for Cardiovascular Disease Prediction Using Machine Learning written by Nitten Singh Rajliwall and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinical decision making is an important and frequent task, which physicians make in their daily clinical practice. Conventionally, physicians adopt a cognitive predictive modelling process (i.e., knowledge and experience learnt from experience, their research, related literature, patient cases, etc.) for anticipating or ascertaining health problems based on clinical risk factors, that deem to be the most salient. However, with the inundation of health data, from EHR system, wearable devices, and other systems for monitoring vital parameters, it has become difficult for physicians to make sense of this massive data, particularly, due to confounding and complex characteristics of chronic diseases, and there is a need for more effective clinical prediction approaches to address these challenges. Given the paramount importance of predictive models for managing chronic disease, cardiovascular diseases in particular, this thesis proposes a novel computational predictive modelling framework, based on innovative machine learning and data science approaches that can aid in clinical decision support. The focus of the proposed predictive modelling framework is on interpretable machine learning approaches that consist of interpretable models based on shallow machine learning techniques, such as those based on linear regression and decision trees and their variants, and model-agnostic approaches based on neural networks and deep learning methods but enhanced with appropriate feature engineering and post-hoc explainability. These approaches allow disease prediction models to be deployed in complex clinical settings, including under remote, extreme, and low-resource environments, where data could be small, big, or massive and has several inadequacies in terms of data quality, noise, or missing data. The availability of interpretable models, and model-agnostic approaches enhanced with explainable aspects are important for physicians and medical professionals, as it will increase transparency, trust and confidence in the decision support provided by computer based algorithmic models. This thesis aims to address the research gap that exists in the current ML/AI based disease detection models, particularly, the lack of robust, objective, explainable, interpretable and trustworthy inference available from the computer based decision support tools, with a majority of the performance metrics reported from computer based tools have been limited to quantitative measures such as accuracy, precision, recall, F-measure, AUC, ROC, without any detailed qualitative metrics, that provide insight into how the computer has arrived at a decision, and ability to explain the decision making logic, eliciting trust from the stakeholders using the system. This could be due to the problem that most of the current ML/AI tools were built using mathematically rigorous constructs, designed around black box approaches, which are hard to interpret and explain, and hence the decisions provided by them appear to be coming from a black box, offering little explanation on decision arrived. The research proposed in this thesis is aimed at the development of a breakthrough explainable predictive modelling framework, based on innovative ML/AI algorithms for building CVD disease detection models. The proposed computation framework provides an intelligent and interpretable holistic analytics platform with improved prediction accuracy, and improved interpretability and explainability. The proposed innovation and development can help drive the healthcare system to one that is more patient-centred, and trustworthy, with potential to be tailored for several diseases such as cancer, cardiovascular disease, asthma, traumatic brain injury, dementia, and diabetes. The outcomes of this research based on innovative findings can serve as an example - that the availability of better computer-based decision support tools, with novel computational strategies, which can address a patient's unique clinical/genetic characteristics, can result in better characterization of diseases and at the same time redefine therapeutic strategies. Some of the key contributions from this research include:• Novel disease detection models based on traditional shallow machine learning algorithms, particularly those based on decision trees and their variants. These algorithms have shown to be inherently interpretable and accurate white box models and can serve as the baseline for comparing with previous models proposed in the literature.• Innovative disease detection models based on model agnostic algorithms, such as deep learning networks, but augmented with appropriate pre- processing and post-processing stages to provide better interpretability and explainability and eventually make them an efficient white box model. For an objective comparison of the methods proposed in each of the above stages, several publicly available benchmark clinical datasets, including Cleveland dataset, NHANES dataset and Framingham Heart Study/CHS dataset were used for model building and experimental validation. Although Cardiovascular disease has been selected as the use case and disease under investigation, since it has led to an alarming increase in the burden of disease, almost at the epidemic levels, and is a major health concern in today's world, the findings from this research can lead to meaningful and significant impact towards improved self-management of chronic non-communicable diseases and make a significant contribution towards better public health management.

Download or read book Diverse Perspectives and State of the Art Approaches to the Utilization of Data Driven Clinical Decision Support Systems written by Connolly, Thomas M. and published by IGI Global. This book was released on 2022-11-11 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems critically reflects on the challenges that data-driven CDSSs must address to become mainstream healthcare systems rather than a small set of exemplars of what might be possible. It further identifies evidence-based, successful data-driven CDSSs. Covering topics such as automated planning, diagnostic systems, and explainable artificial intelligence, this premier reference source is an excellent resource for medical professionals, healthcare administrators, IT managers, pharmacists, students and faculty of higher education, librarians, researchers, and academicians.

Download or read book Discovering Data Driven Actionable Intelligence for Clinical Decision Support written by Ahmed Mohamed Alaa Ibrahim and published by . This book was released on 2019 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid digitization of healthcare has led to a proliferation of clinical data, manifesting through electronic health records, biorepositories, and disease registries. This dissertation addresses the question of how machine learning (ML) techniques can capitalize on these data resources to assist clinicians in predicting, preventing and treating illness. To this end, we develop a set of MLbased, data-driven models of patient outcomes that we envision to be embedded within systems of decision support deployed at different stages of patient care. We focus on two broad setups for analyzing clinical data: (1) the cross-sectional setup wherein data is collected by observing many patients at a particular point of time, and (2) the longitudinal setup in which repeated observations of the same patient are collected over time. In both setups, we develop models that are: (a) capable of answering counter-factual questions, i.e., can predict outcomes under alternative treatment scenarios, (b) interpretable in the sense that clinicians can understand how the model predictions for individual patients are issued, and (c) automated in the sense that they adaptively tune their modeling choices for the dataset at hand, with little or no need for expert intervention. Models satisfying these three requirements would enable the realization of actionable, transparent and automated decision support systems that operate symbiotically within existing clinical workflows. Our technical contributions are multi-faceted. In the cross-sectional data setup, we develop ML models that fulfill the aforementioned requirements (a)-(c) as follows. We start by developing a comprehensive theoretical framework for causal inference, whereby we quantify the limits to how well ML models can recover the causal effects of counter-factual treatment decisions on individual patients using observational (retrospective) data, and we build ML models -- based on Gaussian processes -- that achieve these limits. Next, we develop a novel symbolic meta-modeling approach for interpreting the predictions of any ML-based prognostic model by converting the "black-box" model into an understandable symbolic equation that relates patients' features to their predicted outcomes. Finally, we develop a model selection approach based on Bayesian optimization that enables the automation of predictive and causal modeling. In the longitudinal data setup, we develop a novel deep probabilistic model for sequential clinical data that satisfies requirements (a)- (c) by capitalizing on the strengths of both state-space models and deep recurrent neural networks. To demonstrate the utility of our models, we evaluate their performance on various real-world datasets for cohorts of breast cancer, cardiovascular disease and cystic fibrosis patients. We show that, compared to existing clinical scorers, our ML-based models can improve the accuracy of predicting individual-level prognoses, guide treatment decisions for individual patients, and provide insights into underlying disease mechanisms.

Download or read book Personalized Health Systems for Cardiovascular Disease written by Anna Maria Bianchi and published by Academic Press. This book was released on 2022-01-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Personalized Health Systems for Cardiovascular Disease is intended for researchers, developers, and designers in the field of p-health, with a specific focus on management of cardiovascular diseases. Biomedical engineers will benefit from coverage of sensors, data transmission, signal processing, data analysis, home and mobile applications, standards, and all other subject matters developed in this book in order to provide an integrated view of the different and multidisciplinary problems related to p-health systems. However, many chapters will also be interesting to physicians and other professionals who operate in the health domain. Students, MS and PhD level, mainly in technical universities, but also in medical schools, will find in this book a complete view of the manifold aspects of p-health, including technical problems related to sensors and software, to automatic evaluation and correct interpretation of the data, and also some legal and regulatory aspects. This book mainly focuses on the development of technology used by people and patients in the management of their own health. New wearable and implantable devices allow a continuous monitoring of chronic patients, with a direct involvement of clinical centers and physicians. Also, healthy people are more and more interested in keeping their own wellness under control, by adopting healthy lifestyles and identifying any early sign of risk. This is leading to personalized solutions via systems which are tailored to a specific patient/person and her/ his needs. However, many problems are still open when it comes to p-health systems. Which sensors and parameters should be used? Which software and analysis? When and how? How do you design an effective management plan for chronic pathologies such as cardiovascular diseases? What is useful feedback for the patient or for the clinician? And finally, what are the limits of this approach? What is the view of physicians? The purpose of this book is to provide, from a technical point of view, a complete description of most of the elements which are part of such systems, including the sensors and the hardware, the signal processing and data management procedures, the classification and stratification models, the standards and the regulations, focusing on the state of the art and identifying the new directions for innovative solutions. In this book, readers will find the fundamental elements that must be taken into account when developing devices and systems in the field of p-health. Provides an integrated approach to design and development of p-health systems which involves sensors, analysis software, user interfaces, data modeling, and interpretation. Covers standards and regulations on data privacy and security, plus safe design of devices. Supported by case studies discussing development of actual solutions in the biomedical engineering field.

Download or read book Machine Learning Analytics for Data driven Decision Support in Healthcare written by Andrew Thomas Ward and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has the potential to revolutionize the field of healthcare. With the increasing availability of electronic healthcare data, machine learning algorithms and techniques are able to offer novel data-driven insights in the form of descriptive, predictive, and prescriptive analytics. Research efforts in machine learning-driven clinical decision support systems have demonstrated performance comparable to, or surpassing, that of doctors across a wide range of disciplines. However, very few of these solutions are implemented and used. This may be due to the solution being too specialized, too difficult to operationalize, or both. My research in machine learning for clinical decision support has focused on delivering broadly applicable and clinically actionable predictions for heart disease and opioid use and misuse. As some of the leading causes of death in the US and worldwide, these are important public health concerns. A less-explored facet of decision support in healthcare lies on operational delivery of care: improving hospital efficiency, modeling patient admissions and discharges, and preventing medical errors. While these research topics are not as popular as their clinical counterparts, the potential for real-world improvement through the study of these issues is far greater in the near-term. In this dissertation, I present novel contributions spanning both the clinical and operational delivery of care. I focus on four lines of data-driven research which have the potential to deliver widespread impact: heart disease prediction, opioid use prediction in pediatric patients, medical error reduction, and hospital discharge planning and resource allocation.

Download or read book Text and Ontology Driven Clinical Decision Support System written by Deepal Susil Dhariwal and published by . This book was released on 2013 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vast amounts of information are present in unstructured format in physicians' notes. Text processing techniques can be used to extract clinically relevant entities from such data. The extracted entities can then be mapped to concepts from medical ontologies to generate a structured Knowledge Base (KB) of patient facts. Clinical Rules written over this KB could then be used to develop systems that can help with a variety of clinical tasks such as decision support alerts in diagnostic process. We propose a generic text and ontology driven information extraction framework. In the first phase, preprocessing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data. The clinical records are parsed using Clinical Text Analysis and Knowledge Extraction System, to extract prior medical history, medications, observations, laboratory results etc. For every concept we consider its polarity, section in which the concept occurs, the associated numerical value, synonyms etc. In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a KB that stores medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB which can then be queried for a variety of clinical tasks. To demonstrate a proof of concept, we use discharge summaries from the cardiovascular domain to determine the TIMI Risk Score and San Francisco Syncope score for a patient.

Download or read book Sensing Modeling and Optimization of Cardiac Systems written by Hui Yang and published by Springer Nature. This book was released on 2023-09-19 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients’ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.

Download or read book New Frontiers of Cardiovascular Screening using Unobtrusive Sensors AI and IoT written by Anirban Dutta Choudhury and published by Academic Press. This book was released on 2022-07-09 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT provides insights into real-world problems in cardiovascular disease screening that can be addressed via AI, IoT and wearable based sensing. Non-Communicable Diseases (NCD) are surpassing CDS and emerging as the foremost cause of death. Hence, early screening of CVDs using wearable and other similar sensors is an extremely important global problem to solve. The digital health field is constantly changing, and this book provides a review of recent technology developments, offering unique coverage of processing time series physiological sensor data. The authors have developed this book with graduate and post graduate students in mind, making sure they provide an accessible entry point into the field. This book is particularly useful for engineers and computer scientists who want to build technologies that work in real world scenarios as it provides a practitioner’s view/insights /tricks of the trade. Finally, this book helps researchers working on this important problem to quickly ramp up their knowledge and research to the state-of-the-art. Maps digital health technology to real diseases that are relevant to the medical community Supported with patient data and case studies Gives practitioners insights into the real-world implementation of signal conditioning, signal processing and machine learning

Download or read book Machine Learning Based Heart Disease Diagnosis written by Pooja Rani and published by . This book was released on 2023-03-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Ontology Driven Clinical Decision Support for Early Diagnostic Recommendations written by Gopikrishnan Mannamparambil Chandrasekharan and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Clinical Decision Support Systems written by Eta S. Berner and published by Springer Verlag. This book was released on 1999 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to be 1) a resource book on diagnostic systems for informatics specialists; 2) a textbook for teachers or students in health or medical informatics training programs; and 3) a comprehensive introduction for clinicians, with or without expertise in the applications of computers in medicine, who are interested in learning about current developments in computer-based diagnostic systems. In recent years, it has become obvious that other health professionals, in addition to physicians, have needs for decision support and that the issues raised in this book apply to a broad range of clinicians. The book includes chapters by nationally and internationally recognized experts on the design, evaluation and application of these systems who examine the impact of practitioner and patient use of computer-based diagnostic tools.

Download or read book Interoperability and Machine Learning in Primary Care written by Wendy Oude Nijeweme-d'Hollosy and published by . This book was released on 2017 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Driven Approaches for Healthcare written by Chengliang Yang and published by CRC Press. This book was released on 2021-06-30 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Health care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics

Download or read book Automated Disease drug Ontology Generation Framework Powered by Linked Biomedical Ontologies written by and published by . This book was released on 2019 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The exponential growth of unstructured data available in the biomedical literature and electronic health records requires powerful technologies and novel architectures. The success of smart healthcare applications in clinical decision support, disease diagnosis, and healthcare management depends on knowledge representation that is interpretable by machines in order to infer new knowledge from. To this end, ontological data models are expected to play a pivotal role in organizing, integrating, and representing knowledge machines can understand and act upon. Unfortunately, constructing such models using non-automated means can be prohivitively time-consuming for both domain experts and ontology engineers, thereby limiting the scale and/or the scope of the required ontological models. This thesis proposes a novel automated ontology generation framework and presents an implementation that automatically builds an ontology from published PubMed abstracts. Empowered by linked biomedical ontologies, the proposed framework consists of five major steps: Text Processing using a compute-on-demand approach; medical semantic annotation using n-gram, ontology linking and classification algorithms; Relation Extraction using graph algorithms and syntactic patterns; semantic enrichment using RDF mining; and Domain Inference Engine to build the formal ontology. Quantitative evaluation of the ontology produced show 84.78% recall, 53.35% precision, and 67.70% F-measure in terms of concepts identification; 85.51% recall, 69.61% precision, and 76.74% F-measure with respect to taxonomic relation extraction; and 77.20% recall, 40.10% precision, and 52.78% F-measure for biomedical non-taxonomic relation extraction. The dissertation concludes that the proposed natural language processing, semantic enrichment, syntactic pattern, and graph-based algorithm techniques, along with the use of linked biomedical ontologies, combine to provide a promising solution to the problem of automating the process of disease-drug ontology generation.

Download or read book Clinical Decision Support The Road Ahead written by Robert A. Greenes and published by Academic Press. This book was released on 2006-12 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the nature of medical knowledge, how it is obtained, and how it can be used for decision support. It provides complete coverage of computational approaches to clinical decision-making. Chapters discuss data integration into healthcare information systems and delivery to point of care for providers, as well as facilitation of direct to consumer access. A case study section highlights critical lessons learned, while another portion of the work examines biostatistical methods including data mining, predictive modelling, and analysis. This book additionally addresses organizational, technical, and business challenges in order to successfully implement a computer-aided decision-making support system in healthcare delivery.