61 research outputs found

    Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

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    Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research

    PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data

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    With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends, and the detection of outliers. This allows policies to be rapidly adapted to changing circumstances. We present an efficient outlier detection technique, termed PIKS (Pruned iterative-k means searchlight), which combines an iterative k-means algorithm with a pruned searchlight based scan. We apply this technique to identify outliers in two publicly available healthcare datasets from the New York Statewide Planning and Research Cooperative System, and California's Office of Statewide Health Planning and Development. We provide a comparison of our technique with three other existing outlier detection techniques, consisting of auto-encoders, isolation forests and feature bagging. We identified outliers in conditions including suicide rates, immunity disorders, social admissions, cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the PIKS technique produces results consistent with other techniques such as the auto-encoder. However, the auto-encoder needs to be trained, which requires several parameters to be tuned. In comparison, the PIKS technique has far fewer parameters to tune. This makes it advantageous for fast, "out-of-the-box" data exploration. The PIKS technique is scalable and can readily ingest new datasets. Hence, it can provide valuable, up-to-date insights to citizens, patients and policy-makers. We have made our code open source, and with the availability of open data, other researchers can easily reproduce and extend our work. This will help promote a deeper understanding of healthcare policies and public health issues

    Pair Analytics in a Visual Analytics Context

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    This case study details the development of “pair analytics” as practical approach to applied analysis and as a scientific research method. The hybrid research project itself was part of a larger research program approved for the Canadian government for their offset program and supported by Federal and Provincial research internships. As a real-world analysis approach, the pair analysis sessions conduced actionable causal chain analysis of aircraft safety. As a scientific method, pair analytics advanced our knowledge of the cognitive science of interpersonal communication in Joint Activities. The paper describes how aerospace researchers and cognitive scientists were able to design a research approach that met constraints from both areas. It concludes with discussion of the implications of this work for highly integrated basic and responsive research in other areas of visualization and analytics

    EHR STAR: The State‐Of‐the‐Art in Interactive EHR Visualization

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    Since the inception of electronic health records (EHR) and population health records (PopHR), the volume of archived digital health records is growing rapidly. Large volumes of heterogeneous health records require advanced visualization and visual analytics systems to uncover valuable insight buried in complex databases. As a vibrant sub-field of information visualization and visual analytics, many interactive EHR and PopHR visualization (EHR Vis) systems have been proposed, developed, and evaluated by clinicians to support effective clinical analysis and decision making. We present the state-of-the-art (STAR) of EHR Vis literature and open access healthcare data sources and provide an up-to-date overview on this important topic. We identify trends and challenges in the field, introduce novel literature and data classifications, and incorporate a popular medical terminology standard called the Unified Medical Language System (UMLS). We provide a curated list of electronic and population healthcare data sources and open access datasets as a resource for potential researchers, in order to address one of the main challenges in this field. We classify the literature based on multidisciplinary research themes stemming from reoccurring topics. The survey provides a valuable overview of EHR Vis revealing both mature areas and potential future multidisciplinary research directions

    Translating Predictive Models for Alzheimer’s Disease to Clinical Practice: User Research, Adoption Opportunities, and Conceptual Design of a Decision Support Tool

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    Alzheimer’s Disease (AD) is a common form of Dementia with terrible impact on patients, families, and the healthcare sector. Recent computational advances, such as predictive models, have improved AD data collection and analysis, disclosing the progression pattern of the disease. Whilst clinicians currently rely on a qualitative, experience-led approach to make decisions on patients’ care, the Event-Based Model (EBM) has shown promising results for familial and sporadic AD, making it well positioned to inform clinical decision-making. What proves to be challenging is the translation of computational implementations to clinical applications, due to lack of human factors considerations. The aim of this Ph.D. thesis is to (1) explore barriers and opportunities to the adoption of predictive models for AD in clinical practice; and (2) develop and test the design concept of a tool to enable EBM exploitation by AD clinicians. Following a user-centred design approach, I explored current clinical needs and practices, by means of field observations, interviews, and surveys. I framed the technical-clinical gap, identifying the technical features that were better suited for clinical use, and research-oriented clinicians as the best placed to initially adopt the technology. I designed and tested with clinicians a prototype, icompass, and reviewed it with the technical teams through a series of workshops. This approach fostered a thorough understanding of clinical users’ context and perceptions of the tool’s potential. Furthermore, it provided recommendations to computer scientists pushing forward the models and tool’s development, to enhance user relevance in the future. This thesis is one of the few works addressing a lack of consensus on successful adoption and integration of such innovations to the healthcare environment, from a human factors’ perspective. Future developments should improve prototype fidelity, with interleaved clinical testing, refining design, algorithm, and strategies to facilitate the tool’s integration within clinical practice

    Visual Analytics for Performing Complex Tasks with Electronic Health Records

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    Electronic health record systems (EHRs) facilitate the storage, retrieval, and sharing of patient health data; however, the availability of data does not directly translate to support for tasks that healthcare providers encounter every day. In recent years, healthcare providers employ a large volume of clinical data stored in EHRs to perform various complex data-intensive tasks. The overwhelming volume of clinical data stored in EHRs and a lack of support for the execution of EHR-driven tasks are, but a few problems healthcare providers face while working with EHR-based systems. Thus, there is a demand for computational systems that can facilitate the performance of complex tasks that involve the use and working with the vast amount of data stored in EHRs. Visual analytics (VA) offers great promise in handling such information overload challenges by integrating advanced analytics techniques with interactive visualizations. The user-controlled environment that VA systems provide allows healthcare providers to guide the analytics techniques on analyzing and managing EHR data through interactive visualizations. The goal of this research is to demonstrate how VA systems can be designed systematically to support the performance of complex EHR-driven tasks. In light of this, we present an activity and task analysis framework to analyze EHR-driven tasks in the context of interactive visualization systems. We also conduct a systematic literature review of EHR-based VA systems and identify the primary dimensions of the VA design space to evaluate these systems and identify the gaps. Two novel EHR-based VA systems (SUNRISE and VERONICA) are then designed to bridge the gaps. SUNRISE incorporates frequent itemset mining, extreme gradient boosting, and interactive visualizations to allow users to interactively explore the relationships between laboratory test results and a disease outcome. The other proposed system, VERONICA, uses a representative set of supervised machine learning techniques to find the group of features with the strongest predictive power and make the analytic results accessible through an interactive visual interface. We demonstrate the usefulness of these systems through a usage scenario with acute kidney injury using large provincial healthcare databases from Ontario, Canada, stored at ICES

    Characterizing the Quality of Insight by Interactions: A Case Study

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    Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool—MediSyn—for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study’s implications, lessons learned, and future research opportunities.Peer reviewe

    Visual Analytics of Temporal Event Sequences

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    Temporal event sequence data (such as event logs) is collected in a wide variety of domains ranging from healthcare to cyber security, vehicle fault diagnosis, population living activities, and web clickstream records. Visual analytics aims to obtain a summary or overview of the data to allow knowledge discovery and support the improvement of the process being studied. Despite the great advances in visual analytics of event data, two main gaps were found in the literature. First, existing visualisations provide an overview of event sequences where its level-of-detail can be transformed by drilling down certain elements, but do not provide dynamic levels of detail simultaneously across sequences and longitudinally. Second, current overviews of event data focus on the visual encoding of sequential patterns but present limitations when representing temporal and multivariate attributes: the attributes are not encoded in the overview or if present, these are oversimplified (e.g. using average values). This thesis tackles both gaps by proposing a technique to build a multilevel and multivariate overview of temporal event sequences. The overview is multilevel as its level of granularity can be transformed across sequences (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric – then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarisation applied to each cluster representation. The overview is multivariate as it allows to visualise event types in the overview using an EventBox, a novel visual encoding that aggregates temporal and multivariate attributes for a set of event occurrences of the same type. The overview allows the identification of trends and outliers involving multivariate attributes within and across clusters. The proposed technique has been implemented into a visualisation system called Sequence Cluster Explorer (Sequen-C) that allows detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. The technique is demonstrated through four case studies using three different types of real-world datasets in the healthcare domain: patient flow, hospital admissions and prescription history, and calls made to the emergency services. The case studies show how the technique can aid experts in exploring and defining a set of pathways that best summarise the dataset, while exploring data attributes for selected patterns. Moreover, Sequen-C was evaluated with 13 non-expert users. The results indicate that the system Sequen-C can allow novice users to quickly familiarise with the proposed visualisations and successfully obtain insights from the data according to the objective analytic tasks. Furthermore, the results of the System Usability Scale questionnaire indicate that Sequen-C has a good usability level
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