Download or read book Extracting Clinical Event Sequence by Using Association Rule Mining to Predict Clinical Events from Health Records written by Aashara Shrestha and published by . This book was released on 2022 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining is the process of extracting useful information from large amounts of data. Data mining has been around for a long time, and there are many multiple methods of performing data mining. However, the abundance of data that has become available in the last decade has made it possible to mine through this data to uncover important patterns and sequences. The relationship between variables and the way in which they can lead to a specific outcome is an interesting area of research. Today's healthcare industry faces a number of challenges. Providers must reduce costs, improve transparency, and improve the overall user experience. As a result of the rise of medical data, providers must leverage analytics to maximize customer data access. Additionally, patient data security is critical for regulatory compliance. Using clinical decision making with the help of data mining, analysts may now assist physicians in identifying patient concerns more effectively and in a timely manner. A physician can use data mining insights to make a more educated clinical decision and prevent patients from further clinical risks. Many data mining and machine learning techniques have been applied to several aspects of healthcare. Clinical event recognition is one of the several subfields of clinical decision making. Clinical data sequences can be used to aid in better decision making and the identification of scenarios involving patients who are at high risk of experiencing negative hospital outcomes of care. Among the negative outcomes of care include increased length of stay (LOS), negative discharge status, high mortality rate, and high cost of treatment, just to name a few instances. Our research is focused on the recognition of clinical events. We begin with some preliminary work to gain an understanding of how to use clinical data, and we then produced some statistical analyses of seasonal variations in respiratory diseases in hospital admissions, as well as demonstrated the negative impact on clinical care that occurs when a discrepancy between admission and discharge diagnosis is observed in our study. With all of the preparation work completed, our primary focus became the recognition of clinical events. In the beginning, we used an approach in which the user annotated the clinical sequence, and then we developed an Apriori-Plugin algorithm that assists in viewing the sequence of clinical events that contribute to the development of adverse clinical outcomes. Later, in order to eliminate the need for manual annotation of sequence order, we developed a Bayes-based automated extraction of clinical sequences that utilized the principles of association rule mining in conjunction with metrics such as confidence and certainty factor to extract clinical sequences. Afterward, this approach is incorporated to replace the annotation step in our prior work, which aided in the process of generating clinical sequence orders that did not require user annotation.

Download or read book Healthcare Data Analytics written by Chandan K. Reddy and published by CRC Press. This book was released on 2015-06-23 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available

Download or read book Towards More Generalizable Machine Learning written by Tianran Zhang and published by . This book was released on 2022 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven models for diagnostic and other clinical prediction tasks have been enabled by the increasing availability of electronic health records (EHRs) and recent developments in machine learning (ML). Notably, the clinical event sequences extracted from EHR data provide important insights into how a patient's illness progresses. However, many of the models developed thus far are trained and validated using data from the same distribution (e.g., a single institutional dataset). When externally validated on distributions other than those used for training, these models exhibit generalizability issues despite their reported improvement. The variation in distributions between the training and deployment environment is called dataset shift, which can be attributed to many factors during the data generation process (e.g., patient demographics, site-specific healthcare delivery patterns, policy changes), and data processing approaches (e.g., concurrent event ordering, feature mapping). This problem and subsequent model generalization is exemplified by current approaches involving EHR data and clinical event sequences. This dissertation seeks to assess and reduce the impact of dataset shift on the stability of clinical event sequence models, addressing two facets of the problem. First, the research explores a method to learn perturbation-invariant representations of event sequences involving concurrent events by modeling them as a sequence-of-sets, ameliorating the impact of dataset shift caused by inconsistent ordering schemes imposed during pre-processing. With a permutation-sampling-based framework, we enforce perturbation-invariance on a clinical dataset using an additional L1 loss. The proposed framework is tested on a next-visit diagnostic prediction task and shows improved robustness over perturbations in concurrent event ordering shifts. Second, this research develops a domain-invariant representation learning framework using unsupervised adversarial domain adaptation techniques, reducing the impact of dataset shift on a model's target domain performance without requiring any target labels. To improve transfer performance in the unlabelled target domain, the pre-trained Transformer-based framework adversarially learns domain-invariant features that are also beneficial to the discriminative task of next-visit diagnostic prediction. The proposed framework is evaluated for both transfer directions on event sequence datasets from two different healthcare systems and demonstrates superior zero-shot predictive performance on the target data over the non-adversarial baselines. This dissertation advances our understanding of how dataset shift affects the generalization and stability of clinical event sequence diagnostic prediction models, and offers solutions to reduce its impact in both single-source perturbation and cross-dataset unsupervised transfer learning settings.

Download or read book Extracting Clinical Event Timelines written by Julien Tourille and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Important information for public health is contained within Electronic Health Records (EHRs). The vast majority of clinical data available in these records takes the form of narratives written in natural language. Although free text is convenient to describe complex medical concepts, it is difficult to use for medical decision support, clinical research or statistical analysis.Among all the clinical aspects that are of interest in these records, the patient timeline is one of the most important. Being able to retrieve clinical timelines would allow for a better understanding of some clinical phenomena such as disease progression and longitudinal effects of medications. It would also allow to improve medical question answering and clinical outcome prediction systems. Accessing the clinical timeline is needed to evaluate the quality of the healthcare pathway by comparing it to clinical guidelines, and to highlight the steps of the pathway where specific care should be provided.In this thesis, we focus on building such timelines by addressing two related natural language processing topics which are temporal information extraction and clinical event coreference resolution.Our main contributions include a generic feature-based approach for temporal relation extraction that can be applied to documents written in English and in French. We devise a neural based approach for temporal information extraction which includes categorical features.We present a neural entity-based approach for coreference resolution in clinical narratives. We perform an empirical study to evaluate how categorical features and neural network components such as attention mechanisms and token character-level representations influence the performance of our coreference resolution approach.

Download or read book Statistics and Machine Learning Methods for EHR Data written by Hulin Wu and published by CRC Press. This book was released on 2020-12-10 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

Download or read book A Hierarchical Model for Association Rule Mining of Sequential Events written by Tyler McCormick and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many healthcare settings, patients visit healthcare professionals periodically and report multiple medical conditions, or symptoms, at each encounter. We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient's possible future symptoms given the patient's current and past history of reported symptoms. The core of our technique is a Bayesian hierarchical model for selecting predictive association rules (such as "symptom 1 and symptom 2 → symptom 3") from a large set of candidate rules. Because this method "borrows strength" using the symptoms of many similar patients, it is able to provide predictions specialized to any given patient, even when little information about the patient's history of symptoms is available.

Download or read book Cases on Health Outcomes and Clinical Data Mining Studies and Frameworks written by Cerrito, Patricia and published by IGI Global. This book was released on 2010-02-28 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Because so much data is now becoming readily available to investigate health outcomes, it is important to examine just how statistical models are used to do this. This book studies health outcomes research using data mining techniques"--Provided by publisher.

Download or read book R and Data Mining written by Yanchang Zhao and published by Academic Press. This book was released on 2012-12-31 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work

Download or read book Clinical Research Informatics written by Rachel L. Richesson and published by Springer Nature. This book was released on 2023-06-14 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: This extensively revised new edition comprehensively reviews the rise of clinical research informatics (CRI). It enables the reader to develop a thorough understanding of how CRI has developed and the evolving challenges facing the biomedical informatics professional in the modern clinical research environment. Emphasis is placed on the changing role of the consumer and the need to merge clinical care delivery and research as part of a changing paradigm in global healthcare delivery. Clinical Research Informatics presents a detailed review of using informatics in the continually evolving clinical research environment. It represents a valuable textbook reference for all students and practising healthcare informatics professional looking to learn and expand their understanding of this fast-moving and increasingly important discipline.

Download or read book A Sequence Based Approach for Predicting Clinical Events written by Anam Zahid and published by . This book was released on 2016 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: The data associated to each patient increases almost linearly as the patient flows through the continuum of care. Analysis of the data collected during a patient's admission to the hospital reveals that it grows vertically as well as horizontally as a variety of readings are taken for the patient. In general, ma- chine learning techniques are designed and evaluated to predict clinical events at one particular time point during this process (on admission to the hospital, or on discharge). This highlights one of the key challenges of making predictive solutions applicable to the real world setting, as it limits the interventions that can be taken while the patient is at the hospital, to avoid undesirable clini- cal outcomes down the road. To address this challenge, we have proposed a novel framework of at-admit and sequence based models that predict clinical outcomes accurately at different time points of a patient's hospital stay and perform consistently better than a retrospectively designed solution. Hospitalizations account for about half of all healthcare expenses, and it has been estimated that 13% of the inpatients in the United States use more than half of all hospital resources through repeated admissions. Therefore, the clinical outcome chosen for this work is predicting thirty day readmissions for the "all cause" population. We compare our proposed approach to the state of the art readmission modeling approach of retrospective feature creation, and see an average improvement of 7% in the area under the curve as well as significant improvements in precision, accuracy and recall.

Download or read book Clinical Data as the Basic Staple of Health Learning written by Institute of Medicine and published by National Academies Press. This book was released on 2011-01-14 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Successful development of clinical data as an engine for knowledge generation has the potential to transform health and health care in America. As part of its Learning Health System Series, the Roundtable on Value & Science-Driven Health Care hosted a workshop to discuss expanding the access to and use of clinical data as a foundation for care improvement.

Download or read book Clinical Data Mining for Physician Decision Making and Investigating Health Outcomes Methods for Prediction and Analysis written by Cerrito, Patricia and published by IGI Global. This book was released on 2010-06-30 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book shows how the investigation of healthcare databases can be used to examine physician decisions to develop evidence-based treatment guidelines that optimize patient outcomes"--Provided by publisher.

Download or read book Process Mining in Healthcare written by Ronny S. Mans and published by Springer. This book was released on 2015-03-12 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.

Download or read book Introduction to Computational Health Informatics written by Arvind Kumar Bansal and published by CRC Press. This book was released on 2020-01-08 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development

Download or read book Data Mining Techniques written by Arun K. Pujari and published by Universities Press. This book was released on 2001 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing. It Deals With The Latest Algorithms For Discussing Association Rules, Decision Trees, Clustering, Neural Networks And Genetic Algorithms. The Book Also Discusses The Mining Of Web Data, Temporal And Text Data. It Can Serve As A Textbook For Students Of Compuer Science, Mathematical Science And Management Science, And Also Be An Excellent Handbook For Researchers In The Area Of Data Mining And Warehousing.

Download or read book Post Mining of Association Rules Techniques for Effective Knowledge Extraction written by Zhao, Yanchang and published by IGI Global. This book was released on 2009-05-31 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules.

Download or read book Predicting Disease Progression Using Deep Recurrent Neural Networks and Longitudinal Electronic Health Record Data written by Seunghwan Kim (Data scientist) and published by . This book was released on 2020 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially important as it encodes temporal concepts such as event trends, episodes, cycles, and abnormalities. Previously, there have been attempts to utilize temporal neural network models to predict clinical intervention time and mortality in the intensive care unit (ICU) and recurrent neural network (RNN) models to predict multiple types of medical conditions as well as medication use. However, such work has been limited in scope and generalizability beyond the immediate use cases that have been focused upon. In order to extend the relevant knowledge- base, this study demonstrates a predictive modeling pipeline that can extract and integrate clinical information from the EHR, construct a feature set, and apply a deep recurrent neural network (DRNN) to model complex time stamped longitudinal data for monitoring and managing the progression of a disease condition. It utilizes longitudinal data of pediatric patient cohort diagnosed with Neurofibromatosis Type 1 (NF1), which is one of the most common neurogenetic disorders and occurs in 1 of every 3,000 births, without predilection for race, sex, or ethnicity. The prediction pipeline is differentiable from other efforts to-date that have sought to model NF1 progression in that it involves the analysis of multi-dimensional phenotypes wherein the DRNN is able to model complex non-linear relationships between event points in the longitudinal data both temporally and . Such an approach is critical when seeking to transition from traditional evidence-based care models to precision medicine paradigms. Furthermore, our predictive modeling pipeline can be generalized and applied to manage the progression and stratify the risks in other similar complex diseases, as it can predict multiple set of sub-phenotypical features from training on longitudinal event sequences.