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Journal of Biomedical Informatics

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https://www.readbyqxmd.com/read/30300713/association-networks-in-a-matched-case-control-design-co-occurrence-patterns-of-preexisting-chronic-medical-conditions-in-patients-with-major-depression-versus-their-matched-controls
#1
Min-Hyung Kim, Samprit Banerjee, Yize Zhao, Fei Wang, Yiye Zhang, Yongjun Zhu, Joseph DeFerio, Lauren Evans, Sang Min Park, Jyotishman Pathak
OBJECTIVE: We present a method for comparing association networks in a matched case-control design, which provides a high-level comparison of co-occurrence patterns of features after adjusting for confounding factors. We demonstrate this approach by examining the differential distribution of chronic medical conditions in patients with major depressive disorder (MDD) compared to the distribution of these conditions in their matched controls. MATERIALS AND METHODS: Newly diagnosed MDD patients were matched to controls based on their demographic characteristics, socioeconomic status, place of residence, and healthcare service utilization in the Korean National Health Insurance Service's National Sample Cohort...
October 6, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30296491/relscan-a-system-for-extracting-chemical-induced-disease-relation-from-biomedical-literature
#2
Stanley Chika Onye, Arif Akkeleş, Nazife Dimililer
This paper proposes an effective and robust approach for Chemical-Induced Disease (CID) relation extraction from PubMed articles. The study was performed on the Chemical Disease Relation (CDR) task of BioCreative V track-3 corpus. The proposed system, named relSCAN, is an efficient CID relation extraction system with two phases to classify relation instances from the Co-occurrence and Non-Co-occurrence mention levels. We describe the case of chemical and disease mentions that occur in the same sentence as 'Co-occurrence', or as 'Non-Co-occurrence' otherwise...
October 5, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30292855/social-media-mining-for-birth-defects-research-a-rule-based-bootstrapping-approach-to-collecting-data-for-rare-health-related-events-on-twitter
#3
Ari Z Klein, Abeed Sarker, Haitao Cai, Davy Weissenbacher, Graciela Gonzalez-Hernandez
BACKGROUND: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited. OBJECTIVE: The primary objectives of this study were (i) to assess whether rare health-related events-in this case, birth defects-are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis...
October 4, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30292854/utilizing-soft-constraints-to-enhance-medical-relation-extraction-from-the-history-of-present-illness-in-electronic-medical-records
#4
Li Chen, Yuanju Li, Weipeng Chen, Xinglong Liu, Zhonghua Yu, Siyuan Zhang
Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous researches utilizing machine learning algorithms judge the semantic types of medical concept pair mentions independently. In fact, different concept pair mentions in the same context are of dependencies which can provide beneficial evidences for identifying their relation types. To the best of our knowledge, only one study has considered such dependencies in discharge summaries...
October 4, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30278276/call-for-papers-deep-phenotyping-for-precision-medicine
#5
EDITORIAL
Chunhua Weng, Nigam Shah, George Hripcsak
No abstract text is available yet for this article.
September 29, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30268843/a-novel-depth-estimation-algorithm-of-chest-compression-for-feedback-of-high-quality-cardiopulmonary-resuscitation-based-on-a-smartwatch
#6
Tsung-Chien Lu, Yi Chen, Te-Wei Ho, Yao-Ting Chang, Yi-Ting Lee, Yu-Siang Wang, Yen-Pin Chen, Chia-Ming Fu, Wen-Chu Chiang, Matthew Huei-Ming Ma, Cheng-Chung Fang, Feipei Lai, Anne M Turner
INTRODUCTION: High-quality cardiopulmonary resuscitation (CPR) is a key factor affecting cardiac arrest survival. Accurate monitoring and real-time feedback are emphasized to improve CPR quality. The purpose of this study was to develop and validate a novel depth estimation algorithm based on a smartwatch equipped with a built-in accelerometer for feedback instructions during CPR. METHODS: For data collection and model building, researchers wore an Android Wear smartwatch and performed chest compression-only CPR on a Resusci Anne QCPR training manikin...
September 27, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30268842/piston-predicting-drug-indications-and-side-effects-using-topic-modeling-and-natural-language-processing
#7
Giup Jang, Taekeon Lee, Soyoun Hwang, Chihyun Park, Jaegyoon Ahn, Sukyung Seo, Youhyeon Hwang, Youngmi Yoon
The process of discovering novel drugs to treat diseases requires a long time and high cost. It is important to understand side effects of drugs as well as their therapeutic effects, because these can seriously damage the patients due to unexpected actions of the derived candidate drugs. In order to overcome these limitations, computational methods for predicting the therapeutic effects and side effects have been proposed. In particular, text mining is a widely used technique in the field of systems biology, because it can discover hidden relationships between drugs, genes and diseases from a large amount of literature data...
September 27, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30267895/examining-sensor-based-physical-activity-recognition-and-monitoring-for-healthcare-using-internet-of-things-a-systematic-review
#8
REVIEW
Jun Qi, Po Yang, Atif Waraich, Zhikun Deng, Youbing Zhao, Yun Yang
Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps...
September 26, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30266231/predicting-of-anaphylaxis-in-big-data-emr-by-exploring-machine-learning-approaches
#9
Isabel Segura-Bedmar, Cristobal Colón-Ruíz, Miguél Ángel Tejedor-Alonso, Mar Moro-Moro
Anaphylaxis is a life-threatening allergic reaction that occurs suddenly after contact with an allergen. Epidemiological studies about anaphylaxis are very important in planning and evaluating new strategies that prevent this reaction, but also in providing a guide to the treatment of patients who have just suffered an anaphylactic reaction. Electronic Medical Records (EMR) are one of the most effective and richest sources for the epidemiology of anaphylaxis, because they provide a low-cost way of accessing rich longitudinal data on large populations...
September 25, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30244122/integration-of-transcriptomic-data-and-metabolic-networks-in-cancer-samples-reveals-highly-significant-prognostic-power
#10
Alex Graudenzi, Davide Maspero, Marzia Di Filippo, Marco Gnugnolo, Claudio Isella, Giancarlo Mauri, Enzo Medico, Marco Antoniotti, Chiara Damani
Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements...
September 20, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30240803/factorization-machines-and-deep-views-based-co-training-for-improving-answer-quality-prediction-in-online-health-expert-question-answering-services
#11
Zhan Zhang, Ze Hu, Haiqin Yang, Rong Zhu, Decheng Zuo
In online health expert question-answering (HQA) services, it is significant to automatically determine the quality of the answers. There are two prominent challenges in this task. First, the answers are usually written in short text, which makes it difficult to absorb the text semantic information. Second, it usually lacks sufficient labeled data but contains a huge amount of unlabeled data. To tackle these challenges, we propose a novel deep co-training framework based on factorization machines (FM) and deep textual views to intelligently and automatically identify the quality of HQA systems...
September 18, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30237014/predicting-the-risk-of-acute-care-readmissions-among-rehabilitation-inpatients-a-machine-learning-approach
#12
Yajiong Xue, Huigang Liang, John Norbury, Rita Gillis, Brenda Killingworth
INTRODUCTION: Readmission from inpatient rehabilitation facilities to acute care hospitals is a serious problem. This study aims to develop a predictive model based on machine learning algorithms to identify patients at high risk of readmission. METHODS: A retrospective dataset (2001-2017) including 16,902 patients admitted into a large inpatient rehabilitation facility in North Carolina was collected in 2017. Three types of machine learning models with different predictors were compared in 2018...
September 17, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30217670/a-comparison-of-word-embeddings-for-the-biomedical-natural-language-processing
#13
Yanshan Wang, Sijia Liu, Naveed Afzal, Majid Rastegar-Mojarad, Liwei Wang, Feichen Shen, Paul Kingsbury, Hongfang Liu
Background Word embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the vector representations of words capturing useful semantic properties and linguistic relationships between words. Different textual resources (e.g., Wikipedia and biomedical literature corpus) have been utilized in biomedical NLP to train word embeddings and these word embeddings have been commonly leveraged as feature input to downstream machine learning models. However, there has been little work on evaluating the word embeddings trained from different textual resources...
September 11, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30213556/identifying-health-information-technology-related-safety-event-reports-from-patient-safety-event-report-databases
#14
Allan Fong, Katharine T Adams, Michael J Gaunt, Jessica L Howe, Kate Kellogg, Raj M Ratwani
OBJECTIVE: The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports. METHODS: 5,287 PSE reports manually coded as likely or unlikely related to HIT were used to train unigram, bigram, and combined logistic regression and support vector machine models using five-fold cross validation...
September 10, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30205172/supporting-biomedical-ontology-evolution-by-identifying-outdated-concepts-and-the-required-type-of-change
#15
Silvio Domingos Cardoso, Cédric Pruski, Marcos Da Silveira
The consistent evolution of ontologies is a major challenge for systems using semantically enriched data, for example, for annotating, indexing, or reasoning. The biomedical domain is a typical example where ontologies, expressed with different formalisms, have been used for a long time and whose dynamic nature requires the regular revision of underlying systems. However, the automatic identification of outdated concepts and proposition of revision actions to update them are still open research questions. Solutions to these problems are of great interest to organizations that manage huge and dynamic ontologies...
September 8, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30205171/quality-assurance-of-biomedical-terminologies-and-ontologies
#16
EDITORIAL
James Geller, Yehoshua Perl, Licong Cui, G Q Zhang
No abstract text is available yet for this article.
September 8, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30195660/an-evaluation-of-clinical-order-patterns-machine-learned-from-clinician-cohorts-stratified-by-patient-mortality-outcomes
#17
Jason K Wang, Jason Hom, Santhosh Balasubramanian, Alejandro Schuler, Nigam H Shah, Mary K Goldstein, Michael T M Baiocchi, Jonathan H Chen
OBJECTIVE: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. MATERIALS AND METHODS: Inpatient electronic health records from 2010-2013 were extracted from a tertiary academic hospital. Clinicians (n=1,822) were stratified into low-mortality (21.8%, n=397) and high-mortality (6.0%, n=110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates...
September 6, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30195659/identification-of-target-gene-and-prognostic-evaluation-for-lung-adenocarcinoma-using-gene-expression-meta-analysis-network-analysis-and-neural-network-algorithms
#18
Gurudeeban Selvaraj, Satyavani Kaliamurthi, Aman Chandra Kaushik, Abbas Khan, Yong-Kai Wei, William C Cho, Keren Gu, Dong-Qing Wei
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles. RESULTS: A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients...
September 6, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30195086/the-use-of-model-constructs-to-design-collaborative-health-information-technologies-a-case-study-to-support-child-development
#19
Sean P Mikles, Hyewon Suh, Julie A Kientz, Anne M Turner
OBJECTIVE: Health information technology could provide valuable support for inter-professional collaboration to address complex health issues, but current HIT systems do not adequately support such collaboration. Existing theoretical research on supporting collaborative work can help inform the design of collaborative HIT systems. Using the example of supporting collaboration between child development service providers, we describe a deductive approach that leverages concepts from the literature and analyzes qualitative user-needs data to aid in collaborative system design...
September 5, 2018: Journal of Biomedical Informatics
https://www.readbyqxmd.com/read/30189255/mixed-effect-machine-learning-a-framework-for-predicting-longitudinal-change-in-hemoglobin-a1c
#20
Che Ngufor, Holly Van Houten, Brian S Caffo, Nilay D Shah, Rozalina G McCoy
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning...
September 3, 2018: Journal of Biomedical Informatics
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