Biomedical informatics insights最新文献

{"title":"A Software Tool for the Annotation of Embolic Events in Echo Doppler Audio Signals.","authors":"Paola Pierleoni, Lorenzo Maurizi, Lorenzo Palma, Alberto Belli, Simone Valenti, Alessandro Marroni","doi":"10.1177/1178222617745557","DOIUrl":"10.1177/1178222617745557","url":null,"abstract":"<p><p>The use of precordial Doppler monitoring to prevent decompression sickness (DS) is well known by the scientific community as an important instrument for early diagnosis of DS. However, the timely and correct diagnosis of DS without assistance from diving medical specialists is unreliable. Thus, a common protocol for the manual annotation of echo Doppler signals and a tool for their automated recording and annotation are necessary. We have implemented original software for efficient bubble appearance annotation and proposed a unified annotation protocol. The tool auto-sets the response time of human \"bubble examiners,\" performs playback of the Doppler file by rendering it independent of the specific audio player, and enables the annotation of individual bubbles or multiple bubbles known as \"showers.\" The tool provides a report with an optimized data structure and estimates the embolic risk level according to the Extended Spencer Scale. The tool is built in accordance with ISO/IEC 9126 on software quality and has been projected and tested with assistance from the Divers Alert Network (DAN) Europe Foundation, which employs this tool for its diving data acquisition campaigns.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617745557"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f9/dd/10.1177_1178222617745557.PMC5724642.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35658154","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":"Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting.","authors":"Thomas Desautels,&nbsp;Jacob Calvert,&nbsp;Jana Hoffman,&nbsp;Qingqing Mao,&nbsp;Melissa Jay,&nbsp;Grant Fletcher,&nbsp;Chris Barton,&nbsp;Uli Chettipally,&nbsp;Yaniv Kerem,&nbsp;Ritankar Das","doi":"10.1177/1178222617712994","DOIUrl":"https://doi.org/10.1177/1178222617712994","url":null,"abstract":"<p><p>Algorithm-based clinical decision support (CDS) systems associate patient-derived health data with outcomes of interest, such as in-hospital mortality. However, the quality of such associations often depends on the availability of site-specific training data. Without sufficient quantities of data, the underlying statistical apparatus cannot differentiate useful patterns from noise and, as a result, may underperform. This initial training data burden limits the widespread, out-of-the-box, use of machine learning-based risk scoring systems. In this study, we implement a statistical transfer learning technique, which uses a large \"source\" data set to drastically reduce the amount of data needed to perform well on a \"target\" site for which training data are scarce. We test this transfer technique with <i>AutoTriage</i>, a mortality prediction algorithm, on patient charts from the Beth Israel Deaconess Medical Center (the source) and a population of 48 249 adult inpatients from University of California San Francisco Medical Center (the target institution). We find that the amount of training data required to surpass 0.80 area under the receiver operating characteristic (AUROC) on the target set decreases from more than 4000 patients to fewer than 220. This performance is superior to the Modified Early Warning Score (AUROC: 0.76) and corresponds to a decrease in clinical data collection time from approximately 6 months to less than 10 days. Our results highlight the usefulness of transfer learning in the specialization of CDS systems to new hospital sites, without requiring expensive and time-consuming data collection efforts.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617712994"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222617712994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35108355","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":"Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.","authors":"Huaxiu Tang,&nbsp;Imre Solti,&nbsp;Eric Kirkendall,&nbsp;Haijun Zhai,&nbsp;Todd Lingren,&nbsp;Jaroslaw Meller,&nbsp;Yizhao Ni","doi":"10.1177/1178222617713018","DOIUrl":"https://doi.org/10.1177/1178222617713018","url":null,"abstract":"<p><p>The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives. We downloaded all FAERS reports submitted by medical providers and extracted the reported drug-DDSS pairs. For each patient, we aligned the drug-DDSS pairs extracted from their clinical notes with the corresponding drug-DDSS pairs from the FAERS data set to identify Drug-Reaction Pair Sentences (DRPSs). The DRPSs were processed by NLP techniques to identify ADR-related DRPSs. We used clinician annotated, real-world EHR data as reference standard to evaluate the proposed algorithm. During evaluation, the algorithm achieved promising performance and showed great potential in identifying ADRs accurately for pediatric patients.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617713018"},"PeriodicalIF":0.0,"publicationDate":"2017-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222617713018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35105249","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":"Analysis of Individual Differences in Vaccine Pharmacovigilance Using VAERS Data and MedDRA System Organ Classes: A Use Case Study With Trivalent Influenza Vaccine.","authors":"Jingcheng Du,&nbsp;Yi Cai,&nbsp;Yong Chen,&nbsp;Yongqun He,&nbsp;Cui Tao","doi":"10.1177/1178222617700627","DOIUrl":"https://doi.org/10.1177/1178222617700627","url":null,"abstract":"<p><p>Personalized and precision vaccination requires consideration of an individual's sex and age. This article proposed systematic methods to study individual differences in adverse reactions following vaccination and chose trivalent influenza vaccine as a use case. Data were extracted from the Vaccine Adverse Event Reporting System from years 1990 to 2014. We first grouped symptoms into the Medical Dictionary for Regulatory Activities System Organ Classes (SOCs). We then applied zero-truncated Poisson regression and logistic regression to identify reporting differences among different individual groups over the SOCs. After that, we further studied detailed symptoms of 4 selected SOCs. In all, 19 of the 26 SOCs and 17 of the 434 symptoms under the 4 selected SOCs show significant reporting differences based on sex and/or age. In addition to detecting previously reported associations among sex, age group, and symptoms, our approach also enabled the detection of new associations.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617700627"},"PeriodicalIF":0.0,"publicationDate":"2017-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222617700627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34965010","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":"Using Spatial Analysis to Inform Community Immunization Strategies.","authors":"Moises E Maravi,&nbsp;Lauren E Snyder,&nbsp;L Dean McEwen,&nbsp;Kathryn DeYoung,&nbsp;Arthur J Davidson","doi":"10.1177/1178222617700626","DOIUrl":"https://doi.org/10.1177/1178222617700626","url":null,"abstract":"<p><strong>Introduction: </strong>Recent pertussis outbreaks in the United States suggest our response to local disease outbreaks (eg, vaccine-preventable <i>Bordetella pertussis</i>) may benefit from understanding and applying spatial analytical methods that use data from immunization information systems at a subcounty level.</p><p><strong>Methods: </strong>A 2012 study on Denver, CO, residents less than 19 years of age confirmed pertussis cases and immunization information system records were geocoded and aggregated to the census tract (CT) level. An algorithm assessed whether individuals were up-to-date (UTD) for pertussis vaccines. Pearson, Spearman, and Kendall correlations assessed relations between disease incidence and pertussis vaccine coverage. Using spatial analysis software, disease incidence and UTD rates were spatially weighted, and smoothed. Global and local autocorrelations based on univariate Moran's I spatial autocorrelation statistics evaluated whether a CT's rate belong to a cluster based on incidence or UTD measures.</p><p><strong>Results: </strong>Overall disease incidence rate was 116.8/100 000. Assessment of pertussis vaccination coverage was available for 90% of the population. Among 134 672 Denver residents less than 19 years old, 103 496 (77%) were UTD for pertussis vaccines. Raw correlation coefficients showed weak relationships between incidence and immunization rates due to the presence of outliers. With geospatial and clustering analysis, estimates and correlation coefficients were improved with statistically significant Moran's I values for global and local autocorrelations rejecting the null hypothesis that incidence or UTD rates were randomly distributed. With evidence indicating the presence of clusters, smoothed and weighted disease incidence and UTD rates in 144 CTs identified 21 CTs (15%) for potential public health intervention.</p><p><strong>Conclusions: </strong>Correlation of raw disease incidence and vaccine UTD rates in subcounty regions showed limited association, providing limited information for decision making. By assessing for clusters using spatial analysis methods, we identified CTs with higher incidence and lower immunization coverage for targeted public health interventions.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617700626"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222617700626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964509","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":"Systematic Review of Medical Informatics-Supported Medication Decision Making.","authors":"Brittany L Melton","doi":"10.1177/1178222617697975","DOIUrl":"https://doi.org/10.1177/1178222617697975","url":null,"abstract":"<p><p>This systematic review sought to assess the applications and implications of current medical informatics-based decision support systems related to medication prescribing and use. Studies published between January 2006 and July 2016 which were indexed in PubMed and written in English were reviewed, and 39 studies were ultimately included. Most of the studies looked at computerized provider order entry or clinical decision support systems. Most studies examined decision support systems as a means of reducing errors or risk, particularly associated with medication prescribing, whereas a few studies evaluated the impact medical informatics-based decision support systems have on workflow or operations efficiency. Most studies identified benefits associated with decision support systems, but some indicate there is room for improvement.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617697975"},"PeriodicalIF":0.0,"publicationDate":"2017-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222617697975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964508","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":"Opportunities and Challenges of Adolescent and Adult Vaccination Administration Within Pharmacies in the United States.","authors":"Jessica Y Islam, Joann F Gruber, Alexandre Lockhart, Manju Kunwar, Spencer Wilson, Sara B Smith, Noel T Brewer, Jennifer S Smith","doi":"10.1177/1178222617692538","DOIUrl":"10.1177/1178222617692538","url":null,"abstract":"<p><p>Pharmacies have been endorsed as alternative vaccine delivery sites to improve vaccination rates through increased access to services. Our objective was to identify challenges and facilitators to adolescent and adult vaccination provision in pharmacy settings in the United States. We recruited 40 licensed pharmacists in states with different pharmacy vaccination laws. Eligible pharmacists previously administered or were currently administering human papillomavirus (HPV); tetanus, diphtheria, and pertussis (TDAP); or meningitis (meningococcal conjugate vaccine [MCV4]) vaccines to adolescents aged 9 to 17 years. Pharmacists participated in a semistructured survey on in-pharmacy vaccine provision. Pharmacists commonly administered vaccinations to age-eligible adolescents and adults: influenza (100%, 100%), pneumococcal (35%, 98%), TDAP (80%, 98%), MCV4 (60%, 78%), and HPV (45%, 53%). Common challenges included reimbursement/insurance coverage (28%, 78%), education of patients/parents (30%, 40%), and pharmacists' time constraints (28%, 35%). Three-quarters of pharmacists reported that vaccination rates could be increased. National efforts should expand insurance coverage for vaccine administration reimbursement and improve data information systems to optimize provision within pharmacies.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222617692538"},"PeriodicalIF":0.0,"publicationDate":"2017-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1a/8b/10.1177_1178222617692538.PMC5345946.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964507","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":"Using Electronic Medical Record to Identify Patients With Dyslipidemia in Primary Care Settings: International Classification of Disease Code Matters From One Region to a National Database.","authors":"Justin Oake,&nbsp;Erfan Aref-Eshghi,&nbsp;Marshall Godwin,&nbsp;Kayla Collins,&nbsp;Kris Aubrey-Bassler,&nbsp;Pauline Duke,&nbsp;Masoud Mahdavian,&nbsp;Shabnam Asghari","doi":"10.1177/1178222616685880","DOIUrl":"https://doi.org/10.1177/1178222616685880","url":null,"abstract":"<p><strong>Objective: </strong>To assess the validity of the International Classification of Disease (ICD) codes for identifying patients with dyslipidemia in electronic medical record (EMR) data.</p><p><strong>Methods: </strong>The EMRs of patients receiving primary care in St. John's, Newfoundland and Labrador (NL), Canada, were retrieved from the Canadian Primary Care Sentinel Surveillance Network database. International Classification of Disease codes were first compared with laboratory lipid data as an independent criterion standard, and next with a \"comprehensive criterion standard,\" defined as any existence of abnormal lipid test, lipid-lowering medication record, or dyslipidemia ICD codes. The ability of ICD coding alone or combined with other components was evaluated against the two criterion standards using receiver operating characteristic (ROC) analysis, sensitivity, specificity, negative predictive value (NPV) and Kappa agreement. (No specificity was reported for the comparison of ICD codes against the comprehensive criterion standard as this naturally leads to 100% specificity.).</p><p><strong>Results: </strong>The ICD codes led to a poor outcome when compared with the serum lipid levels (sensitivity, 27%; specificity, 76%; PPV, 71%; NPV, 33%; Kappa, 0.02; area under the receiver operating characteristic curve (AUC), 0.51) or with the comprehensive criterion standard (sensitivity, 32%; NPV, 25%; Kappa, 0.15; AUC, 66%). International Classification of Disease codes combined with lipid-lowering medication data also resulted in low sensitivity (51.2%), NPV (32%), Kappa (0.28), and AUC (75%). The addition of laboratory lipid levels to ICD coding marginally improved the algorithm (sensitivity, 94%; NPV, 79%; Kappa, 0.85; AUC, 97%).</p><p><strong>Conclusions: </strong>The use of ICD coding, either alone or in combination with laboratory data or lipid-lowering medication records, was not an accurate indicator in identifying dyslipidemia.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222616685880"},"PeriodicalIF":0.0,"publicationDate":"2017-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222616685880","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964504","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":"Immunization Information System and Informatics to Promote Immunizations: Perspective From Minnesota Immunization Information Connection.","authors":"Miriam Halstead Muscoplat,&nbsp;Sripriya Rajamani","doi":"10.1177/1178222616688893","DOIUrl":"https://doi.org/10.1177/1178222616688893","url":null,"abstract":"<p><p>The vision for management of immunization information is availability of real-time consolidated data and services for all ages, to clinical, public health, and other stakeholders. This is being executed through Immunization Information Systems (IISs), which are population-based and confidential computerized systems present in most US states and territories. Immunization Information Systems offer many functionalities, such as immunization assessment reports, client follow-up, reminder/recall feature, vaccine management tools, state-supplied vaccine ordering, comprehensive immunization history, clinical decision support/vaccine forecasting and recommendations, data processing, and data exchange. This perspective article will present various informatics tools in an IIS, in the context of the Minnesota Immunization Information Connection.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222616688893"},"PeriodicalIF":0.0,"publicationDate":"2017-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222616688893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964506","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":"A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.","authors":"Joshua Glauser, Brian Connolly, Paul Nash, Daniel H Grossoehme","doi":"10.1177/1178222616686067","DOIUrl":"10.1177/1178222616686067","url":null,"abstract":"<p><p>Religious or spiritual struggles are clinically important to health care chaplains because they are related to poorer health outcomes, involving both mental and physical health problems. Identifying persons experiencing religious struggle poses a challenge for chaplains. One potentially underappreciated means of triaging chaplaincy effort are prayers written in chapel notebooks. We show that religious struggle can be identified in these notebooks through instances of negative religious coping, such as feeling anger or abandonment toward God. We built a data set of entries in chapel notebooks and classified them as showing religious struggle, or not. We show that natural language processing techniques can be used to automatically classify the entries with respect to whether or not they reflect religious struggle with as much accuracy as humans. The work has potential applications to triaging chapel notebook entries for further attention from pastoral care staff.</p>","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"9 ","pages":"1178222616686067"},"PeriodicalIF":0.0,"publicationDate":"2017-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/6f/10.1177_1178222616686067.PMC5391196.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34964505","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}