81 results match your criteria Biomedical Informatics Insights [Journal]


Incorporating Observed Physiological Data to Personalize Pediatric Vital Sign Alarm Thresholds.

Biomed Inform Insights 2019 10;11:1178222618818478. Epub 2019 Jan 10.

Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

Bedside monitors are intended as a safety net in patient care, but their management in the inpatient setting is a significant patient safety concern. The low precision of vital sign alarm systems leads to clinical staff becoming desensitized to the sound of the alarm, a phenomenon known as alarm fatigue. Alarm fatigue has been shown to increase response time to alarms or result in alarms being ignored altogether and has negative consequences for patient safety. Read More

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http://dx.doi.org/10.1177/1178222618818478DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6330722PMC
January 2019

Views From the Street Pilot Study: Constraints and Difficulties of Using Photographs in Research of a Complex Nature Such as Homelessness.

Biomed Inform Insights 2018 13;10:1178222618816097. Epub 2018 Dec 13.

The Old Schools, Department of Psychiatry, University of Cambridge, Cambridge, UK.

Homeless people experience a unique set of challenges leading to pervasive health and social problems. An increasing number of researchers have harnessed photographic data to gain a unique perspective of marginalised groups. The aim of the study is to explore the feasibility of using photographs in research to understand the complex environment experienced by homeless people, with a special interest in mental health. Read More

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http://dx.doi.org/10.1177/1178222618816097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295676PMC
December 2018
2 Reads

Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images.

Biomed Inform Insights 2018 31;10:1178222618807481. Epub 2018 Oct 31.

Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.

Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). Read More

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http://dx.doi.org/10.1177/1178222618807481DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6236488PMC
October 2018
9 Reads

Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder.

Biomed Inform Insights 2018 2;10:1178222618803076. Epub 2018 Oct 2.

Personal Health Informatics, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.

Non-adherence with pharmacologic treatment is associated with increased rates of relapse and rehospitalisation among patients with schizophrenia and bipolar disorder. To improve treatment response, remission, and recovery, research efforts are still needed to elucidate how to effectively map patient's response to medication treatment including both therapeutic and adverse effects, compliance, and satisfaction in the prodromal phase of illness (ie, the time period in between direct clinical consultation and relapse). The Actionable Intime Insights (AI) application draws information from Australian Medicare administrative claims records in real time when compliance with treatment does not meet best practice guidelines for managing chronic severe mental illness. Read More

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http://dx.doi.org/10.1177/1178222618803076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170953PMC
October 2018
4 Reads

Humanizing Digital Mental Health through Social Media: Centering Experiences of Gang-Involved Youth Exposed to High Rates of Violence.

Authors:
William R Frey

Biomed Inform Insights 2018 27;10:1178222618797076. Epub 2018 Aug 27.

School of Social Work, Columbia University, New York, NY, USA.

As the lives of young people expand further into digital spaces, our understandings of their expressions and language on social media become more consequential for providing individualized and applicable mental health resources. This holds true for young people exposed to high rates of community violence who may also lack access to health resources offline. Social media may provide insights into the impacts of community violence exposure on mental health. Read More

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http://dx.doi.org/10.1177/1178222618797076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111393PMC
August 2018
11 Reads

Experiences of Donating Personal Data to Mental Health Research: An Explorative Anthropological Study.

Authors:
Joanna Sleigh

Biomed Inform Insights 2018 27;10:1178222618785131. Epub 2018 Jun 27.

Department of Political and Social Sciences, Research Area Visual and Media Anthropology, Freie Universität, Berlin, Germany.

Technological developments, such as the advent of social networking sites, apps, and tracking 'cookies', enable the generation and collection of unprecedented quantities of rich personal and behavioural data, opening up a vast new resource for mental health research. Despite these non-traditional health-related data already forming a vital foundation of many new research avenues, little analysis has been done focusing on the experiences, motivations, and concerns of the individuals already engaged in data sharing and donation practices. This explorative study aims to investigate the experiences of individuals voluntarily donating their data to mental health research, specifically through the open data initiative OurDataHelps. Read More

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http://dx.doi.org/10.1177/1178222618785131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043936PMC
June 2018
3 Reads

Tech Has It's Place.

Authors:
Telixia Inico

Biomed Inform Insights 2018 9;10:1178222618785122. Epub 2018 Jul 9.

London, UK.

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http://dx.doi.org/10.1177/1178222618785122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6043930PMC
July 2018
4 Reads

Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform.

Biomed Inform Insights 2018 4;10:1178222618777749. Epub 2018 Jun 4.

Massachusetts General Hospital, Boston, MA, USA.

Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at more than 150 institutions for querying patient data. An i2b2 installation (called hive) comprises several i2b2 cells that provide different functionalities. Given the complex architecture of i2b2 installation, creating a working installation of the platform is challenging for new users. Read More

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http://dx.doi.org/10.1177/1178222618777749DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989048PMC
June 2018
6 Reads

A Method for Deriving Quasi-healthy Cohorts From Clinical Data.

Biomed Inform Insights 2018 29;10:1178222618777758. Epub 2018 May 29.

Nursing Course of Kochi Medical School, Nankoku, Japan.

We evaluated quasi-healthy cohorts (model cohorts), derived from clinical data, to determine how well they simulated control cohorts. Control cohorts comprised individuals extracted from a public checkup database in Japan, under the condition that their values for 3 basic laboratory tests fall within specific reference ranges (3Ts condition). Model cohorts comprised outpatients, extracted from a clinical database at a hospital, under the 3Ts condition or under the condition that their values for 4 laboratory tests fall within specific reference ranges (4Ts condition). Read More

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http://dx.doi.org/10.1177/1178222618777758DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977427PMC
May 2018
4 Reads

Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing.

Biomed Inform Insights 2018 3;10:1178222618763155. Epub 2018 Apr 3.

Computer Science, Columbia University, New York, NY, USA.

There is a dearth of research investigating youths' experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. Read More

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http://dx.doi.org/10.1177/1178222618763155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5888812PMC
April 2018
9 Reads

Proceedings from the Digital Innovation in Mental Health Conference, London, 2017.

Authors:
Becky Inkster

Biomed Inform Insights 2018 28;10:1178222618764732. Epub 2018 Mar 28.

Department of Psychiatry, University of Cambridge, Cambridge, UK.

The conference aims were two-fold: (1) to explore how digital technology is implemented into personalized and/or group mental health interventions and (2) to promote digital equality through developing culturally sensitive ways of bringing technological innovation to disadvantaged groups. A broad scope of perspectives were welcomed and encouraged, from lived experience, academic, clinical, media, the arts, policy-making, tech innovation, and other perspectives. Read More

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http://dx.doi.org/10.1177/1178222618764732DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882037PMC
March 2018
5 Reads

A Software Tool for the Annotation of Embolic Events in Echo Doppler Audio Signals.

Biomed Inform Insights 2017 7;9:1178222617745557. Epub 2017 Dec 7.

DAN Europe Foundation, Ta'Xbiex, Malta.

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. Read More

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http://dx.doi.org/10.1177/1178222617745557DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5724642PMC
December 2017
10 Reads

Using Transfer Learning for Improved Mortality Prediction in a Data-Scarce Hospital Setting.

Biomed Inform Insights 2017 12;9:1178222617712994. Epub 2017 Jun 12.

Department of Research, Dascena, Inc, Hayward, CA, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222617712994DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5470861PMC
June 2017
16 Reads

Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.

Biomed Inform Insights 2017 8;9:1178222617713018. Epub 2017 Jun 8.

Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222617713018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467704PMC
June 2017
18 Reads

Analysis of Individual Differences in Vaccine Pharmacovigilance Using VAERS Data and MedDRA System Organ Classes: A Use Case Study With Trivalent Influenza Vaccine.

Biomed Inform Insights 2017 11;9:1178222617700627. Epub 2017 Apr 11.

School of Biomedical Informatics, The University of Texas, Houston, TX, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222617700627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391193PMC
April 2017
26 Reads

Using Spatial Analysis to Inform Community Immunization Strategies.

Biomed Inform Insights 2017 30;9:1178222617700626. Epub 2017 Mar 30.

Denver Public Health, Denver, CO, USA.

Introduction: Recent pertussis outbreaks in the United States suggest our response to local disease outbreaks (eg, vaccine-preventable ) may benefit from understanding and applying spatial analytical methods that use data from immunization information systems at a subcounty level.

Methods: 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. Read More

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http://journals.sagepub.com/doi/10.1177/1178222617700626
Publisher Site
http://dx.doi.org/10.1177/1178222617700626DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391195PMC
March 2017
16 Reads

Systematic Review of Medical Informatics-Supported Medication Decision Making.

Biomed Inform Insights 2017 30;9:1178222617697975. Epub 2017 Mar 30.

Department of Pharmacy Practice, University of Kansas School of Pharmacy, Kansas City, KS, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222617697975DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391194PMC
March 2017
11 Reads

Opportunities and Challenges of Adolescent and Adult Vaccination Administration Within Pharmacies in the United States.

Biomed Inform Insights 2017 16;9:1178222617692538. Epub 2017 Feb 16.

Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222617692538DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345946PMC
February 2017
61 Reads

Immunization Information System and Informatics to Promote Immunizations: Perspective From Minnesota Immunization Information Connection.

Biomed Inform Insights 2017 9;9:1178222616688893. Epub 2017 Feb 9.

Public Health Informatics Program and Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222616688893DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345950PMC
February 2017
19 Reads

A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

Biomed Inform Insights 2017 8;9:1178222616686067. Epub 2017 Feb 8.

Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

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. Read More

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http://dx.doi.org/10.1177/1178222616686067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391196PMC
February 2017
14 Reads

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.

Biomed Inform Insights 2017 10;9:1178222616685880. Epub 2017 Feb 10.

Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland St. John's, NL, Canada.

Objective: To assess the validity of the International Classification of Disease (ICD) codes for identifying patients with dyslipidemia in electronic medical record (EMR) data.

Methods: 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. Read More

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http://dx.doi.org/10.1177/1178222616685880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391192PMC
February 2017
15 Reads

Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System.

Biomed Inform Insights 2016 22;8(Suppl 2):23-29. Epub 2016 Dec 22.

Minnesota Department of Health, St. Paul, MN, USA.

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. Read More

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http://dx.doi.org/10.4137/BII.S40208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5181832PMC
December 2016
16 Reads

Tennessee's 3-Star Report: Using Available Data Systems to Reduce Missed Opportunities to Vaccinate Preteens.

Biomed Inform Insights 2016 4;8(Suppl 2):15-21. Epub 2016 Dec 4.

Department of Medicine, Division of Epidemiology, Vanderbilt University Medical School, Nashville, TN, USA.

All preteens should receive tetanus-diphtheria-pertussis vaccine (Tdap), quadrivalent meningococcal vaccine (Men-ACWY), and the human papillomavirus (HPV) cancer vaccine series. In Tennessee, HPV vaccination rates have stagnated at low levels for a decade. Three fundamental strategies to reduce missed opportunities for immunization include administering all recommended vaccines at the same visit, making strong recommendations for vaccines, and auditing and feedback. Read More

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http://dx.doi.org/10.4137/BII.S40207DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5138065PMC
December 2016
19 Reads

Clinical Decision Support for Immunizations (CDSi): A Comprehensive, Collaborative Strategy.

Authors:
Noam H Arzt

Biomed Inform Insights 2016 19;8(Suppl 2):1-13. Epub 2016 Oct 19.

President, HLN Consulting, LLC, Palm Desert, CA, USA.

This article focuses on the requirements and current developments in clinical decision support technologies for immunizations (CDSi) in both the public health and clinical communities, with an emphasis on shareable solutions. The requirements of the Electronic Health Record Incentive Programs have raised some unique challenges for the clinical community, including vocabulary mapping, update of changing guidelines, single immunization schedule, and scalability. This article discusses new, collaborative approaches whose long-term goal is to make CDSi more sustainable for both the public and private sectors. Read More

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http://dx.doi.org/10.4137/BII.S40204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5072461PMC
October 2016
10 Reads

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.

Biomed Inform Insights 2016 19;8:29-38. Epub 2016 Jul 19.

Professor, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. Read More

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http://dx.doi.org/10.4137/BII.S38916DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4954589PMC
August 2016
13 Reads

Toward a Learning Health-care System - Knowledge Delivery at the Point of Care Empowered by Big Data and NLP.

Biomed Inform Insights 2016 23;8(Suppl 1):13-22. Epub 2016 Jun 23.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Read More

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http://journals.sagepub.com/doi/10.4137/BII.S37977
Publisher Site
http://dx.doi.org/10.4137/BII.S37977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920204PMC
July 2016
19 Reads

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

Biomed Inform Insights 2016 20;8(Suppl 1):1-11. Epub 2016 Jun 20.

Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, USA.

In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Read More

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http://dx.doi.org/10.4137/BII.S37791DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915789PMC
July 2016
10 Reads

Mobile Health (mHealth) Services and Online Health Educators.

Biomed Inform Insights 2016 25;8:19-27. Epub 2016 May 25.

School of Business and Economic, Universiti Brunei Darussalam, Brunei Darussalam.

Mobile technology enables health-care organizations to extend health-care services by providing a suitable environment to achieve mobile health (mHealth) goals, making some health-care services accessible anywhere and anytime. Introducing mHealth could change the business processes in delivering services to patients. mHealth could empower patients as it becomes necessary for them to become involved in the health-care processes related to them. Read More

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http://dx.doi.org/10.4137/BII.S35388DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881870PMC
June 2016
10 Reads

Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.

Biomed Inform Insights 2016 22;8:11-8. Epub 2016 May 22.

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.

Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient's status. Read More

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http://www.la-press.com/redirect_file.php?fileId=7513&filena
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http://www.la-press.com/methodological-issues-in-predicting-
Publisher Site
http://dx.doi.org/10.4137/BII.S38308DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4876984PMC
June 2016
21 Reads

Big Data Application in Biomedical Research and Health Care: A Literature Review.

Biomed Inform Insights 2016 19;8:1-10. Epub 2016 Jan 19.

College of Health Science, Department of Health Informatics and Administration, Center for Biomedical Data and Language Processing, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Big data technologies are increasingly used for biomedical and health-care informatics research. Large amounts of biological and clinical data have been generated and collected at an unprecedented speed and scale. For example, the new generation of sequencing technologies enables the processing of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. Read More

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http://dx.doi.org/10.4137/BII.S31559DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720168PMC
February 2016
36 Reads

Some correlates of electronic health information management system success in nigerian teaching hospitals.

Biomed Inform Insights 2015 1;7:1-9. Epub 2015 Apr 1.

University of Ibadan, Ibadan, Nigeria.

Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. Read More

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http://dx.doi.org/10.4137/BII.S20229DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426943PMC
May 2015
11 Reads

Combining an expert-based medical entity recognizer to a machine-learning system: methods and a case study.

Biomed Inform Insights 2013 1;6(Suppl 1):51-62. Epub 2013 Aug 1.

LIMSI-CNRS, Orsay, France.

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Read More

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http://dx.doi.org/10.4137/BII.S11770DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3776026PMC
September 2013
15 Reads

Using conversation topics for predicting therapy outcomes in schizophrenia.

Biomed Inform Insights 2013 15;6(Suppl 1):39-50. Epub 2013 Jul 15.

Cognitive Science Research Group, School of Electronic Engineering and Computer Science, London, UK.

Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Read More

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http://dx.doi.org/10.4137/BII.S11661DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740209PMC
August 2013
9 Reads

Text Categorization of Heart, Lung, and Blood Studies in the Database of Genotypes and Phenotypes (dbGaP) Utilizing n-grams and Metadata Features.

Biomed Inform Insights 2013 22;6:35-45. Epub 2013 Jul 22.

Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, USA. ; Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, USA.

The database of Genotypes and Phenotypes (dbGaP) allows researchers to understand phenotypic contribution to genetic conditions, generate new hypotheses, confirm previous study results, and identify control populations. However, effective use of the database is hindered by suboptimal study retrieval. Our objective is to evaluate text classification techniques to improve study retrieval in the context of the dbGaP database. Read More

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http://dx.doi.org/10.4137/BII.S11987DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3728208PMC
August 2013
8 Reads

Towards Converting Clinical Phrases into SNOMED CT Expressions.

Authors:
Rohit J Kate

Biomed Inform Insights 2013 24;6(Suppl 1):29-37. Epub 2013 Jun 24.

University of Wisconsin-Milwaukee, Milwaukee, WI, USA.

Converting information contained in natural language clinical text into computer-amenable structured representations can automate many clinical applications. As a step towards that goal, we present a method which could help in converting novel clinical phrases into new expressions in SNOMED CT, a standard clinical terminology. Since expressions in SNOMED CT are written in terms of their relations with other SNOMED CT concepts, we formulate the important task of identifying relations between clinical phrases and SNOMED CT concepts. Read More

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http://dx.doi.org/10.4137/BII.S11645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702194PMC
July 2013
14 Reads

Using empirically constructed lexical resources for named entity recognition.

Biomed Inform Insights 2013 24;6(Suppl 1):17-27. Epub 2013 Jun 24.

Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Because of privacy concerns and the expense involved in creating an annotated corpus, the existing small-annotated corpora might not have sufficient examples for learning to statistically extract all the named-entities precisely. In this work, we evaluate what value may lie in automatically generated features based on distributional semantics when using machine-learning named entity recognition (NER). The features we generated and experimented with include n-nearest words, support vector machine (SVM)-regions, and term clustering, all of which are considered distributional semantic features. Read More

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http://dx.doi.org/10.4137/BII.S11664DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702195PMC
July 2013
8 Reads

Analysis of cross-institutional medication description patterns in clinical narratives.

Biomed Inform Insights 2013 24;6(Suppl 1):7-16. Epub 2013 Jun 24.

Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.

A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Read More

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http://dx.doi.org/10.4137/BII.S11634DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702197PMC
July 2013
11 Reads

Computational semantics in clinical text.

Authors:
Stephen Wu

Biomed Inform Insights 2013 24;6(Suppl 1):3-5. Epub 2013 Jun 24.

Mayo Clinic, Rochester, MN.

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http://dx.doi.org/10.4137/BII.S11847DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702196PMC
July 2013
8 Reads

Computational semantics in clinical text supplement.

Authors:
John P Pestian

Biomed Inform Insights 2013 24;6(Suppl 1):1-2. Epub 2013 Jun 24.

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http://dx.doi.org/10.4137/BII.S11868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3702198PMC
July 2013
15 Reads

Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department Without English Translation: A Feasibility Study.

Biomed Inform Insights 2013 25;6:29-33. Epub 2013 Apr 25.

AT&T Interactive, Glendale, CA, USA.

Introduction: Syndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. Read More

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http://dx.doi.org/10.4137/BII.S11334DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3653813PMC
May 2013
17 Reads

Recognizing scientific artifacts in biomedical literature.

Biomed Inform Insights 2013 2;6:15-27. Epub 2013 Apr 2.

School of ITEE, University of Queensland, Australia.

Today's search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. Read More

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http://dx.doi.org/10.4137/BII.S11572DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3623603PMC
May 2013
13 Reads

Decomposing phenotype descriptions for the human skeletal phenome.

Biomed Inform Insights 2013 4;6:1-14. Epub 2013 Feb 4.

School of ITEE, The University of Queensland, Australia.

Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. The intrinsic value and knowledge captured within such descriptions can only be expressed by taking advantage of their inner structure that implicitly combines qualities and anatomical entities. We present a meta-model (the Phenotype Fragment Ontology) and a processing pipeline that enable together the automatic decomposition and conceptualization of phenotype descriptions for the human skeletal phenome. Read More

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http://dx.doi.org/10.4137/BII.S10729DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572876PMC
February 2013
8 Reads