Journal of healthcare informatics research最新文献

{"title":"Discovering Oculometric Patterns to Detect Cognitive Performance Changes in Healthy Youth Football Athletes.","authors":"Gaurav N Pradhan, Jamie M Bogle, Michael J Cevette, Jan Stepanek","doi":"10.1007/s41666-019-00045-4","DOIUrl":"https://doi.org/10.1007/s41666-019-00045-4","url":null,"abstract":"<p><p>In this paper, we focus on the application of oculometric patterns extracted from raw eye movements during a mental workload task to assess changes in cognitive performance in healthy youth athletes over the course of a typical sport season. Oculometric features pertaining to fixations and saccades were measured on 116 athletes in pre- and post-season testing. Participants were between 7 and 14 years of age at pre-season testing. Due to varied developmental rates, there were large interindividual performance differences during a mental workload task consisting of reading numbers. Based on different reading speeds, we classified three profiles (slow, moderate, and fast) and established their corresponding baselines for oculometric data. Within each profile, we describe changes in oculomotor function based on changes in cognitive performance during the season. To visualize these changes in multidimensional oculometric data, we also present a multidimensional visualization tool named DiViTo (diagnostic visualization tool). These experimental, computational informatics and visualization methodologies may serve to utilize oculometric information to detect changes in cognitive performance due to mild or severe cognitive impairment such as concussion/mild traumatic brain injury, as well as possibly other disorders such as attention deficit hyperactivity disorders, learning/reading disabilities, impairment of alertness, and neurocognitive function.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2019-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients.","authors":"Sungrim Moon, Sijia Liu, David Chen, Yanshan Wang, Douglas L Wood, Rajeev Chaudhry, Hongfang Liu, Paul Kingsbury","doi":"10.1007/s41666-019-00044-5","DOIUrl":"https://doi.org/10.1007/s41666-019-00044-5","url":null,"abstract":"<p><p>Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140913664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual Mobility and Uncertain Geographic Context: Real-time Versus Neighborhood Approximated Exposure to Retail Tobacco Outlets Across the US.","authors":"Thomas R Kirchner, Hong Gao, Daniel J Lewis, Andrew Anesetti-Rothermel, Heather A Carlos, Brian House","doi":"10.1007/s41666-018-0035-8","DOIUrl":"https://doi.org/10.1007/s41666-018-0035-8","url":null,"abstract":"<p><p>There is growing interest in the way exposure to neighborhood risk and protective factors affects the health of residents. Although multiple approaches have been reported, empirical methods for contrasting the spatial uncertainty of exposure estimates are not well established. The objective of this paper was to contrast real-time versus neighborhood approximated exposure to the landscape of tobacco outlets across the contiguous US. A nationwide density surface of tobacco retail outlet locations was generated using kernel density estimation (KDE). This surface was linked to participants' (<i>N</i> _<i>p</i>  = 363) inferred residential location, as well as to their real-time geographic locations, recorded every 10 min over 180 days. Real-time exposure was estimated as the hourly product of radius of gyration and average tobacco outlet density (<i>N</i> _hour = 304, 164 h). Ordinal logit modeling was used to assess the distribution of real-time exposure estimates as a function of each participant's residential exposure. Overall, 61.3% of real-time, hourly exposures were of relatively low intensity, and after controlling for temporal and seasonal variation, 72.8% of the variance among these low-level exposures was accounted for by residence in one of the two lowest residential exposure quintiles. Most moderate to high intensity exposures (38.7% of all real-time, hourly exposures) were no more likely to have been contributed by subjects from any single residential exposure cluster than another. Altogether, 55.2% of the variance in real-time exposures was <i>not</i> explained by participants' residential exposure cluster. Calculating hourly exposure estimates made it possible to directly contrast real-time observations with static residential exposure estimates. Results document the substantial degree that real-time exposures can be misclassified by residential approximations, especially in residential areas characterized by moderate to high retail density levels.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bi-submodular Optimization (BSMO) for Detecting Drug-Drug Interactions (DDIs) from On-line Health Forums.","authors":"Yan Hu, Rui Wang, Feng Chen","doi":"10.1007/s41666-018-0032-y","DOIUrl":"https://doi.org/10.1007/s41666-018-0032-y","url":null,"abstract":"<p><p>Online health discussion forums as information exchange repository are used by different patient groups for sharing experience and seeking advice. Their accessibility is tremendously expanded in the last decade with the rapid growth of mobile internet. Among many popular topics, \"drug-drug interactions\" (DDIs) forum embeds a large number of DDIs hazards patient experienced however not published. In this paper, we intend to uncover the potential DDIs from the online forums and formulate the task as a sub-graph detection problem, such that co-mentioned drugs and symptoms are modeled as vertices, along with the occurrences are modeled as weighted edges. Therefore, a connected sub-graph consisting of both symptoms and drug vertices reveals DDIs occurrence. We then propose a novel bi-submodular function to characterize the likelihood of DDI occurrence within a connected sub-graph and apply an approximated algorithm to resolve the bi-submodular optimization (BSMO). The complexity of the algorithm is nearly linear. Our extensive experiments demonstrate the effectiveness and efficiency of the proposed approach.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2018-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The State of Data in Healthcare: Path Towards Standardization.","authors":"Keith Feldman, Reid A Johnson, Nitesh V Chawla","doi":"10.1007/s41666-018-0019-8","DOIUrl":"https://doi.org/10.1007/s41666-018-0019-8","url":null,"abstract":"<p><p>Coupled with the rise of data science and machine learning, the increasing availability of digitized health and wellness data has provided an exciting opportunity for complex analyses of problems throughout the healthcare domain. Whereas many early works focused on a particular aspect of patient care, often drawing on data from a specific clinical or administrative source, it has become clear such a single-source approach is insufficient to capture the complexity of the human condition. Instead, adequately modeling health and wellness problems requires the ability to draw upon data spanning multiple facets of an individual's biology, their care, and the social aspects of their life. Although such an awareness has greatly expanded the breadth of health and wellness data collected, the diverse array of data sources and intended uses often leave researchers and practitioners with a scattered and fragmented view of any particular patient. As a result, there exists a clear need to catalogue and organize the range of healthcare data available for analysis. This work represents an effort at developing such an organization, presenting a patient-centric framework deemed the Healthcare Data Spectrum (HDS). Comprised of six layers, the HDS begins with the innermost micro-level omics and macro-level demographic data that directly characterize a patient, and extends at its outermost to aggregate population-level data derived from attributes of care for each individual patient. For each level of the HDS, this manuscript will examine the specific types of constituent data, provide examples of how the data aid in a broad set of research problems, and identify the primary terminology and standards used to describe the data.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design Factors of Longitudinal Smartphone-based Health Surveys.","authors":"Sudip Vhaduri, Christian Poellabauer","doi":"10.1007/s41666-017-0003-8","DOIUrl":"https://doi.org/10.1007/s41666-017-0003-8","url":null,"abstract":"<p><p>Phone-based surveys are increasingly being used in healthcare settings to collect data from potentially large numbers of subjects, e.g., to evaluate their levels of satisfaction with medical providers, to study behaviors and trends of specific populations, and to track their health and wellness. Often, subjects respond to such surveys once, but it has become increasingly important to capture their responses multiple times over an extended period to accurately and quickly detect and track changes. With the help of smartphones, it is now possible to automate such longitudinal data collections, e.g., push notifications can be used to alert a subject whenever a new survey is available. This paper investigates various design factors of a longitudinal smartphone-based health survey data collection that contribute to user compliance and quality of collected data. This work presents the design recommendations based on analysis of data collected from 17 subjects over a 1-month period.</p>","PeriodicalId":101413,"journal":{"name":"Journal of healthcare informatics research","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2017-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}