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Methods of Information in Medicine

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https://read.qxmd.com/read/30919405/research-subjects-and-research-trends-in-medical-informatics
#1
Kemal Hakan Gülkesen, Reinhold Haux
OBJECTIVES:  To identify major research subjects and trends in medical informatics research based on the current set of core medical informatics journals. METHODS:  Analyzing journals in the Web of Science (WoS) medical informatics category together with related categories from the years 2013 to 2017 by using a smart local moving algorithm as a clustering method for identifying the core set of journals. Text mining analysis with binary counting of abstracts from these journals published in the years 2006 to 2017 for identifying major research subjects...
March 27, 2019: Methods of Information in Medicine
https://read.qxmd.com/read/30877683/deep-learning-versus-conventional-machine-learning-for-detection-of-healthcare-associated-infections-in-french-clinical-narratives
#2
Sara Rabhi, Jérémie Jakubowicz, Marie-Helene Metzger
OBJECTIVE:  The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. METHODS:  The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals...
March 15, 2019: Methods of Information in Medicine
https://read.qxmd.com/read/30763967/erratum-to-machine-learning-and-data-analytics-in-pervasive-health
#3
Nuria Oliver, Oscar Mayora, Michael Marschollek
No abstract text is available yet for this article.
February 14, 2019: Methods of Information in Medicine
https://read.qxmd.com/read/30634196/addendum-to-approaches-to-regularized-regression-a-comparison-between-gradient-boosting-and-the-lasso
#4
Tobias Hepp, Matthias Schmid, Olaf Gefeller, Elisabeth Waldmann, Andreas Mayr
No abstract text is available yet for this article.
January 11, 2019: Methods of Information in Medicine
https://read.qxmd.com/read/30605914/a-pharmacogenomics-clinical-decision-support-service-based-on-fhir-and-cds-hooks
#5
R H Dolin, A Boxwala, J Shalaby
OBJECTIVES:  Pharmacogenomics (PGx) is often considered a low-hanging fruit for genomics-electronic health record (EHR) integrations, and many have expressed the notion that drug-gene interaction checking might one day become as much a commodity in EHRs as drug-drug and drug-allergy checking. In addition, the U.S. Office of the National Coordinator has recognized the trend toward storing complete sequencing data outside the EHR in a Genomic Archiving and Communication System (GACS) and has emphasized the need for "pilots that test Fast Healthcare Interoperability Resources (FHIR) Genomics for GACS integration with EHRs...
January 3, 2019: Methods of Information in Medicine
https://read.qxmd.com/read/30453339/electronic-collection-of-multilingual-patient-reported-outcomes-across-europe
#6
I Soto-Rey, M Rehr, P Bruland, C Zeidler, C Riepe, S Steinke, S Ständer, M Dugas, M Storck
BACKGROUND:  Patient-reported outcomes (PROs) are information provided directly by patients that helps in improving patient diagnosis and treatment. Validated translations of PROs can be used to treat international patients. Electronic systems and especially mobile devices provide a great opportunity for their collection; however, these systems are normally study-oriented and therefore single language, not scalable, and not interoperable. OBJECTIVES:  This article reports the development of a multicenter, multilingual, and interoperable electronic PRO (ePRO) system and evaluates its user satisfaction in an international clinical study...
November 19, 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30453338/leveraging-electronic-dental-record-data-to-classify-patients-based-on-their-smoking-intensity
#7
J Patel, Z Siddiqui, A Krishnan, T P Thyvalikakath
BACKGROUND:  Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing...
November 19, 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30453337/linking-electronic-health-record-and-trauma-registry-data-assessing-the-value-of-probabilistic-linkage
#8
Ashimiyu B Durojaiye, Lisa L Puett, Scott Levin, Matthew Toerper, Nicolette M McGeorge, Kristen L W Webster, Gurmehar S Deol, Hadi Kharrazi, Harold P Lehmann, Ayse P Gurses
BACKGROUND:  Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone. OBJECTIVE:  This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage. METHODS:  Encounter data were independently obtained from the EHR data warehouse ( n  = 1,632) and the pediatric trauma registry ( n  = 1,829) at a Level I pediatric trauma center...
November 19, 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875708/a-comparison-of-some-nature-inspired-optimization-metaheuristics-applied-in-biomedical-image-registration
#9
Silviu Ioan Bejinariu, Hariton Costin
Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases BACKGROUND:  In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875707/analysis-of-machine-learning-algorithms-for-diagnosis-of-diffuse-lung-diseases
#10
Isadora Cardoso, Eliana Almeida, Hector Allende-Cid, Alejandro C Frery, Rangaraj M Rangayyan, Paulo M Azevedo-Marques, Heitor S Ramos
Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration BACKGROUND:  Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875706/computational-intelligence-re-meets-medical-image-processing
#11
Hariton N Costin, Thomas M Deserno
No abstract text is available yet for this article.
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875705/linking-electronic-health-record-and-trauma-registry-data-assessing-the-value-of-probabilistic-linkage
#12
Ashimiyu B Durojaiye, Lisa L Puett, Scott Levin, Matthew Toerper, Nicolette M McGeorge, Kristen L W Webster, Gurmehar S Deol, Hadi Kharrazi, Harold P Lehmann, Ayse P Gurses
BACKGROUND: Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone. OBJECTIVE: This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage. METHODS: Encounter data were independently obtained from the EHR data warehouse ( n  = 1,632) and the pediatric trauma registry ( n  = 1,829) at a Level I pediatric trauma center...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875704/leveraging-electronic-dental-record-data-to-classify-patients-based-on-their-smoking-intensity
#13
J Patel, Z Siddiqui, A Krishnan, T P Thyvalikakath
BACKGROUND:  Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875703/self-anamnesis-with-a-conversational-user-interface-concept-and-usability-study
#14
Kerstin Denecke, Sandra Lutz Hochreutener, Annkathrin Pöpel, Richard May
OBJECTIVE:  Self-anamnesis is a procedure in which a patient answers questions about the personal medical history without interacting directly with a doctor or medical assistant. If collected digitally, the anamnesis data can be shared among the health care team. In this article, we introduce a concept for digital anamnesis collection and assess the applicability of a conversational user interface (CUI) for realizing a mobile self-anamnesis application. MATERIALS AND METHODS:  We implemented our concept for self-anamnesis for the concrete field of music therapy...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30875702/prehospital-telemedical-emergency-management-of-severely-injured-trauma-patients
#15
Patrick A Eder, Birgit Reime, Thomas Wurmb, Uwe Kippnich, Layal Shammas, Asarnusch Rashid
BACKGROUND:  Trauma is a global burden. Emergency medical services (EMS) provide care for individuals who have serious injuries or suffered a major trauma. OBJECTIVE:  This paper provides a comprehensive overview of telemedicine applications in prehospital trauma care. METHODS:  We conducted a systematic review according to PRISMA guidelines. We identified articles by electronic database search (PubMed, EMBASE, the Cochrane Library, CINAHL, SpringerLink, LIVIVO, DARE, IEEE Xplore, Google Scholar and ScienceDirect) using keywords related to prehospital settings, ambulance, telemedicine and trauma...
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30296809/prehospital-telemedical-emergency-management-of-severely-injured-trauma-patients
#16
Patrick A Eder, Birgit Reime, Thomas Wurmb, Uwe Kippnich, Layal Shammas, Asarnusch Rashid
No abstract text is available yet for this article.
November 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30296808/analysis-of-machine-learning-algorithms-for-diagnosis-of-diffuse-lung-diseases
#17
Isadora Cardoso, Eliana Almeida, Hector Allende-Cid, Alejandro C Frery, Rangaraj M Rangayyan, Paulo M Azevedo-Marques, Heitor S Ramos
BACKGROUND:  Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. OBJECTIVES:  Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods...
October 8, 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30919393/creation-of-a-robust-and-generalizable-machine-learning-classifier-for-patient-ventilator-asynchrony
#18
Gregory B Rehm, Jinyoung Han, Brooks T Kuhn, Jean-Pierre Delplanque, Nicholas R Anderson, Jason Y Adams, Chen-Nee Chuah
BACKGROUND: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers...
September 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30919392/is-multiclass-automatic-text-de-identification-worth-the-effort
#19
Duy Duc An Bui, David T Redden, James J Cimino
OBJECTIVES: Automatic de-identification to remove protected health information (PHI) from clinical text can use a "binary" model that replaces redacted text with a generic tag (e.g., "<PHI>"), or can use a "multiclass" model that retains more class information (e.g., "<Phone Number>"). Binary models are easier to develop, but result in text that is potentially less informative. We investigated whether building a multiclass de-identification is worth the extra effort...
September 2018: Methods of Information in Medicine
https://read.qxmd.com/read/30677782/machine-learning-and-data-analytics-in-pervasive-health
#20
Nuria Oliver, Oscar Mayora, Michael Marschollek
INTRODUCTION: This accompanying editorial provides a brief introduction to this focus theme, focused on "Machine Learning and Data Analytics in Pervasive Health". OBJECTIVE: The innovative use of machine learning technologies combining small and big data analytics will support a better provisioning of healthcare to citizens. This focus theme aims to present contributions at the crossroads of pervasive health technologies and data analytics as key enablers for achieving personalised medicine for diagnosis and treatment purposes...
September 2018: Methods of Information in Medicine
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