2,053 results match your criteria Journal of Biomedical Informatics [Journal]


Multiple retrieval case-based reasoning for incomplete datasets.

J Biomed Inform 2019 Feb 13:103127. Epub 2019 Feb 13.

Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Electronic address:

The performance of case-based reasoning (CBR) depends on an accurate ranking of similar cases in the retrieval phase that affects all subsequent phases and profits from the potential of large databases. Unfortunately, growing databases come along with a rising amount of missing data that reduces the stability of the ranking since incomplete cases cannot be ranked as reliable as complete ones. In context of CBR hardly any work was done so far to rigorously analyze the impact of missing data and solutions to tackle this issue. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103127DOI Listing
February 2019
1 Read

Statistical outbreak detection by joining medical records and pathogen similarity.

J Biomed Inform 2019 Feb 13:103126. Epub 2019 Feb 13.

Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania.

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103126DOI Listing
February 2019

Automatic ICD code Assignment of Chinese Clinical Notes based on Multilayer Attention BiRNN.

J Biomed Inform 2019 Feb 12:103114. Epub 2019 Feb 12.

School of Information Science and Engineering, Central South University, Changsha, P.R. China, 410083. Electronic address:

International Classification of Diseases (ICD) code is an important label of electronic health record. The automatic ICD code assignment based on the narrative of clinical documents is an essential task which has drawn much attention recently. When Chinese clinical notes are the input corpus, the nature of Chinese brings some issues that need to be considered, such as the accuracy of word segmentation and the representation of single Chinese characters which contain semantics. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103114DOI Listing
February 2019

Predicting Need for Advanced Illness or Palliative Care In A Primary Care Population Using Electronic Health Record Data.

J Biomed Inform 2019 Feb 9:103115. Epub 2019 Feb 9.

Stanford University, Palo Alto, CA, USA; Sutter Health Research, Walnut Creek, CA, USA.

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103115DOI Listing
February 2019

Predicting the Function of Transplanted Kidney in Long-term Care Processes: Application of a Hybrid Model.

J Biomed Inform 2019 Feb 9:103116. Epub 2019 Feb 9.

Patient Safety Research Center, Urmia University of Medical Sciences, Urmia, Iran; Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands. Electronic address:

Background: A tool that can predict the estimated glomerular filtration rate (eGFR) in routine daily care can help clinicians to make better decisions for kidney transplant patients and to improve transplantation outcome. In this paper, we proposed a hybrid prediction model for predicting a future value for eGFR during long-term care processes.

Methods: Longitudinal, historical data of 942 transplant patients who received a kidney between 2001 and 2016 at Urmia kidney transplant center was used to develop a hybrid model. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103116DOI Listing
February 2019
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Unsupervised Concept Extraction from Clinical Text through Semantic Composition.

J Biomed Inform 2019 Feb 9:103120. Epub 2019 Feb 9.

Computational Linguistics and Psycholinguistics (CLiPS) research center, University of Antwerp, Prinsstraat 13, 2000, Antwerp, Belgium. Electronic address:

Concept extraction is an important step in clinical natural language processing. Once extracted, the use of concepts can improve the accuracy and generalization of downstream systems. We present a new unsupervised system for the extraction of concepts from clinical text. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103120DOI Listing
February 2019

Word Embeddings and External Resources for Answer Processing in Biomedical Factoid Question Answering.

J Biomed Inform 2019 Feb 9:103118. Epub 2019 Feb 9.

School of Informatics, Aristotle University of Thessaloniki, 54124, Greece. Electronic address:

Biomedical question answering (QA) is a challenging task that has not been yet successfully solved, according to results on international benchmarks, such as BioASQ. Recent progress on deep neural networks has led to promising results in domain independent QA, but the lack of large datasets with biomedical question-answer pairs hinders their successful application to the domain of biomedicine. We propose a novel machine-learning based answer processing approach that exploits neural networks in an unsupervised way through word embeddings. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103118DOI Listing
February 2019

Confirm or Refute?: A Comparative Study on Citation Sentiment Classification in Clinical Research Publications.

J Biomed Inform 2019 Feb 9:103123. Epub 2019 Feb 9.

School of Information Sciences, University of Illinois, Champaign, IL, 61820.

Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach. The need for more suitable alternatives has prompted research into developing advanced metrics, such as h-index and Relative Citation Ratio (RCR), as well as better citation categorization schemes to capture the various functions that citations serve in a publication. One such scheme involves citation sentiment: whether a reference paper is cited positively (agreement with the findings of the reference paper), negatively (disagreement), or neutrally. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103123DOI Listing
February 2019
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Temporal Phenotyping of Medically Complex Children via PARAFAC2 Tensor Factorization.

J Biomed Inform 2019 Feb 8:103125. Epub 2019 Feb 8.

Georgia Institute of Technology. Electronic address:

Objective: Our aim is to extract clinically-meaningful phenotypes from longitudinal electronic health records (EHRs) of medically-complex children. This is a fragile set of patients consuming a disproportionate amount of pediatric care resources but who often end up with sub-optimal clinical outcome. The rise in available electronic health records (EHRs) provide a rich data source that can be used to disentangle their complex clinical conditions into concise, clinically-meaningful groups of characteristics. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103125DOI Listing
February 2019
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f(1Feature Extraction for Phenotyping from Semantic and Knowledge Resources.

J Biomed Inform 2019 Feb 7:103122. Epub 2019 Feb 7.

Center for Statistical Science, Tsinghua University, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, China; Institute for Data Science, Tsinghua University, Beijing, China. Electronic address:

Objective: Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464193004
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http://dx.doi.org/10.1016/j.jbi.2019.103122DOI Listing
February 2019
5 Reads

Semi-supervised learning to improve generalizability of risk prediction models.

J Biomed Inform 2019 Feb 7:103117. Epub 2019 Feb 7.

Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China. Electronic address:

The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk prediction relative to supervised learning methods. Data on 113,141 patients diagnosed with nonmetastatic CRC from 2004 to 2012 were obtained from the Surveillance Epidemiology End Results registry for model development, and data on 1,149 patients from the Second Affiliated Hospital, Zhejiang University School of Medicine, who were diagnosed between 2004 and 2011, were collected for generalizability testing. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103117DOI Listing
February 2019
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Computational prediction of the bioactivity potential of proteomes based on expert knowledge.

J Biomed Inform 2019 Feb 7:103121. Epub 2019 Feb 7.

ESEI: Escuela Superior de Ingeniería Informática, University of Vigo, Edificio Politécnico, Campus Universitario As Lagoas s/n 32004, Ourense, Spain; CINBIO - Centro de Investigaciones Biomédicas, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310, Vigo, Spain; SING Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Hospital Álvaro Cunqueiro 36312 Vigo, Spain; CEB - Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. Electronic address:

Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103121DOI Listing
February 2019
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ADEpedia-on-OHDSI: A Next Generation Pharmacovigilance Signal Detection Platform Using the OHDSI Common Data Model.

J Biomed Inform 2019 Feb 7:103119. Epub 2019 Feb 7.

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

Objective: Supplementing the Spontaneous Reporting System (SRS) with Electronic Health Record (EHR) data for adverse drug reaction detection could augment sample size, increase population heterogeneity and cross-validate results for pharmacovigilance research. The difference in the underlying data structures and terminologies between SRS and EHR data presents challenges when attempting to integrate the two into a single data base. The Observational Health Data Sciences and Informatics (OHDSI) collaboration provides a Common Data Model (CDM) for organizing and standardizing EHR data to support large-scale observational studies. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464193003
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http://dx.doi.org/10.1016/j.jbi.2019.103119DOI Listing
February 2019
2 Reads

Content Based Medical Image Retrieval using Topic and Location Model.

J Biomed Inform 2019 Feb 6:103112. Epub 2019 Feb 6.

Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, Kerala, India-673 601.

Background And Objective: Retrieval of medical images from an anatomically diverse dataset is a challenging task. Objective of our present study is to analyse the automated medical image retrieval system incorporating topic and location probabilities to enhance the performance.

Materials And Methods: In this paper, we present an automated medical image retrieval system using Topic andLocation Model. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103112DOI Listing
February 2019

Healthcare Cost Prediction: Leveraging Fine-grain Temporal Patterns.

J Biomed Inform 2019 Feb 6:103113. Epub 2019 Feb 6.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Computer Science Department, Cairo University, Giza, Egypt. Electronic address:

Objective: To design and assess a method to leverage individuals' temporal data for predicting their healthcare cost. To achieve this goal, we first used patients' temporal data in their fine-grain form as opposed to coarse-grain form. Second, we devised novel spike detection features to extract temporal patterns that improve the performance of cost prediction. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103113DOI Listing
February 2019

Employing a User-Centered Cognitive Walkthrough to Evaluate a mHealth Diabetes Self-Management Application: A Case Study and Beginning Method Validation.

J Biomed Inform 2019 Feb 2:103110. Epub 2019 Feb 2.

School of Health Information Science, University of Victoria, Victoria, Canada.

Introduction: Self-management of chronic diseases using mobile health (mHealth) systems and applications is becoming common. Current evaluation methods such as formal usability testing can be very costly and time-consuming; others may be more efficient but lack a user focus. We propose an enhanced cognitive walkthrough (CW) method, the user-centered CW (UC-CW), to address identified deficiencies in the original technique and perform a beginning validation with think aloud protocol (TA) to assess its effectiveness, efficiency and user acceptance in a case study with diabetes patient users on a mHealth self-management application. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103110DOI Listing
February 2019

A Method For Analyzing Inpatient Care Variability Through Physicians' Orders.

J Biomed Inform 2019 Jan 30:103111. Epub 2019 Jan 30.

Dept. of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA.

Objective: Administrators assess care variability through chart review or cost variability to inform care standardization efforts. Chart review is costly and cost variability is imprecise. This study explores the potential of physician orders as an alternative measure of care variability. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103111DOI Listing
January 2019
3 Reads

Unsupervised low-dimensional vector representations for words, phrases and text that are transparent, scalable, and produce similarity metrics that are not redundant with neural embeddings.

J Biomed Inform 2019 Feb 14;90:103096. Epub 2019 Jan 14.

Department of Psychiatry and Psychiatric Institute, University of Illinois College of Medicine, Chicago, IL 60612, USA.

Neural embeddings are a popular set of methods for representing words, phrases or text as a low dimensional vector (typically 50-500 dimensions). However, it is difficult to interpret these dimensions in a meaningful manner, and creating neural embeddings requires extensive training and tuning of multiple parameters and hyperparameters. We present here a simple unsupervised method for representing words, phrases or text as a low dimensional vector, in which the meaning and relative importance of dimensions is transparent to inspection. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103096DOI Listing
February 2019
1 Read

Temporal biomedical data analytics.

J Biomed Inform 2019 Feb 14;90:103092. Epub 2019 Jan 14.

Department of Biomedical Informatics, Columbia University, New York, NY, USA. Electronic address:

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https://linkinghub.elsevier.com/retrieve/pii/S15320464193000
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http://dx.doi.org/10.1016/j.jbi.2018.12.006DOI Listing
February 2019
5 Reads

Computer mediated reality technologies: A conceptual framework and survey of the state of the art in healthcare intervention systems.

J Biomed Inform 2019 Feb 12;90:103102. Epub 2019 Jan 12.

Department of Computer Science, Brunel University London, Uxbridge, London UB8 3PH, United Kingdom. Electronic address:

Introduction: The trend of an ageing and growing world population, particularly in developed countries, is expected to continue for decades to come causing an increase in demand for healthcare resources and services. Consequently, demand is growing faster than rises in funding. The UK government, in partnership with the European Commission's Vision for 2020, propose a paradigm shift towards the delivery of more patient-centred self-care interventions, facilitated by novel ubiquitous computer mediated reality technology applications, as a key strategy to overcome the scarcity of health resources gap. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103102DOI Listing
February 2019
15 Reads

A multivalued agent-based model for the study of noncommunicable diseases.

J Biomed Inform 2019 Jan 10:103101. Epub 2019 Jan 10.

Université de Lille, Laboratoire de Santé Publique, EA 2694 : Epidémiologie et Qualité de soins, 42 rue Ambroise Paré 59120, France.

This paper aims to test and illustrate the utility and extensibility of an existing model, SimNCD (Simulation of NonCommunicable Diseases). It also proposes a way to include questionnaires - widely used in epidemiology - in the individual's reasoning mechanism in order to identify his/her attitude and personal choices. SimNCD is a formal agent-based model. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103101DOI Listing
January 2019

Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.

J Biomed Inform 2019 Feb 9;90:103103. Epub 2019 Jan 9.

The Visiting Nurse Service of New York, New York, NY, USA; School of Nursing, University of Pennsylvania, Philadelphia, PA, USA.

Background: Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components. Read More

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http://dx.doi.org/10.1016/j.jbi.2019.103103DOI Listing
February 2019
7 Reads

Personalization in biomedical-informatics: Methodological considerations and recommendations.

Authors:
Maurits Kaptein

J Biomed Inform 2019 Feb 8;90:103088. Epub 2019 Jan 8.

Jheronimus Academy of Data Science & Tilburg University Statistics and Research Methods, Sint Janssingel 92, 5211 DA 's Hertogenbosch, The Netherlands. Electronic address:

Over the last decades there has been an increasing interest in personalization: can we make sure that treatments are effective for individual patients? The quest for personalization affects biomedical informatics in two ways: first, we design systems-for example eHealth applications-that directly interact with patients and these systems might themselves one day be personalized. Hence, we seek effective methods to do so. Second, we design systems that collect the data which will one day be used to personalize treatments: hence, we need to critically consider design requirements that improve the utility of (e. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464183022
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http://dx.doi.org/10.1016/j.jbi.2018.12.002DOI Listing
February 2019
4 Reads

Rapamycin - mTOR + BRAF = ? Using relational similarity to find therapeutically relevant drug-gene relationships in unstructured text.

J Biomed Inform 2019 Feb 4;90:103094. Epub 2019 Jan 4.

Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States. Electronic address:

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http://dx.doi.org/10.1016/j.jbi.2019.103094DOI Listing
February 2019
7 Reads

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

J Biomed Inform 2019 Feb 4;90:103091. Epub 2019 Jan 4.

School of Computing and Engineering, University of Missouri-Kansas, Kansas City, MO, United States.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences). Read More

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http://dx.doi.org/10.1016/j.jbi.2018.12.005DOI Listing
February 2019
3 Reads
2.482 Impact Factor

Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform.

J Biomed Inform 2019 Feb 2;90:103090. Epub 2019 Jan 2.

AstraZeneca, Gothenburg, Sweden. Electronic address:

Objective: To determine if inclusion/exclusion (I/E) criteria of clinical trial protocols can be represented as structured queries and executed using a secure federated research platform (InSite) on hospital electronic health records (EHR) systems, to estimate the number of potentially eligible patients.

Methods: Twenty-three clinical trial protocols completed during 2011-2017 across diverse disease areas were analyzed to construct queries that were executed with InSite using EHR records from 24 European hospitals containing records of >14 million patients. The number of patients matching I/E criteria of each protocol was estimated. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.12.004DOI Listing
February 2019
1 Read

A comprehensive data level analysis for cancer diagnosis on imbalanced data.

J Biomed Inform 2019 Feb 3;90:103089. Epub 2019 Jan 3.

Department of Quantitative Health Sciences, Cleveland Cancer Foundation, Cleveland, OH, United States. Electronic address:

The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous patients. Diagnosing diseases in general and cancer in particular is a considerable application of data analysis for medical science. However, imbalanced data distribution and imbalanced quality of the majority and minority classes, which lead to misclassification, is a great challenge in this field. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.12.003DOI Listing
February 2019
2 Reads

Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness.

J Biomed Inform 2019 Jan 14;89:114-121. Epub 2018 Dec 14.

Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Leonard Davis Institute of Health Economics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Sentiment analysis may offer insights into patient outcomes through the subjective expressions made by clinicians in the text of encounter notes. We analyzed the predictive, concurrent, convergent, and content validity of six sentiment methods in a sample of 793,725 multidisciplinary clinical notes among 41,283 hospitalizations associated with an intensive care unit stay. None of these approaches improved early prediction of in-hospital mortality using logistic regression models, but did improve both discrimination and calibration when using random forests. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.12.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342660PMC
January 2019
1 Read

Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective.

J Biomed Inform 2019 Jan 7;89:101-113. Epub 2018 Dec 7.

Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia. Electronic address:

Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.014DOI Listing
January 2019

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research.

J Biomed Inform 2019 Jan 3;89:122-133. Epub 2018 Dec 3.

Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil. Electronic address:

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.013DOI Listing
January 2019
3 Reads

An interactive and low-cost full body rehabilitation framework based on 3D immersive serious games.

J Biomed Inform 2019 Jan 3;89:81-100. Epub 2018 Dec 3.

Department of Computer Science, Sapienza University of Rome, Via Salaria 113, 00198 Rome, Italy. Electronic address:

Strokes, surgeries, or degenerative diseases can impair motor abilities and balance. Long-term rehabilitation is often the only way to recover, as completely as possible, these lost skills. To be effective, this type of rehabilitation should follow three main rules. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.012DOI Listing
January 2019
7 Reads

Micro ad-hoc Health Social Networks (uHSN). Design and evaluation of a social-based solution for patient support.

J Biomed Inform 2019 Jan 29;89:68-80. Epub 2018 Nov 29.

HOWLab Research Group, Aragon Institute of Engineering Research (I3A), University of Zaragoza (UZ), Spain. Electronic address:

Objective: To contribute the design, development, and assessment of a new concept: Micro ad hoc Health Social Networks (uHSN), to create a social-based solution for supporting patients with chronic disease.

Design: After in-depth fieldwork and intensive co-design over a 4-year project following Community-Based Participatory Research (CBPR), this paper contributes a new paradigm of uHSN, defining two interaction areas (the "backstage", the sphere invisible to the final user, where processes that build services take place; and the "onstage", the visible part that includes the patients and relatives), and describes a new transversal concept, i.e. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.009DOI Listing
January 2019
2 Reads

A combined modelling of fuzzy logic and Time-Driven Activity-based Costing (TDABC) for hospital services costing under uncertainty.

J Biomed Inform 2019 Jan 23;89:11-28. Epub 2018 Nov 23.

Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran; Hospital Management Research Center (HMRC), Iran University of Medical Sciences (IUMS), Tehran 1969714713, Iran.

Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appropriate information on the activities needed to provide a quality service. However, TDABC is not flawless. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.011DOI Listing
January 2019

Automatic identification of recent high impact clinical articles in PubMed to support clinical decision making using time-agnostic features.

J Biomed Inform 2019 Jan 22;89:1-10. Epub 2018 Nov 22.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States. Electronic address:

Objectives: Finding recent clinical studies that warrant changes in clinical practice ("high impact" clinical studies) in a timely manner is very challenging. We investigated a machine learning approach to find recent studies with high clinical impact to support clinical decision making and literature surveillance.

Methods: To identify recent studies, we developed our classification model using time-agnostic features that are available as soon as an article is indexed in PubMed®, such as journal impact factor, author count, and study sample size. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6342626PMC
January 2019
10 Reads

An unsupervised and customizable misspelling generator for mining noisy health-related text sources.

J Biomed Inform 2018 Dec 13;88:98-107. Epub 2018 Nov 13.

Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, United States.

Background: Data collection and extraction from noisy text sources such as social media typically rely on keyword-based searching/listening. However, health-related terms are often misspelled in such noisy text sources due to their complex morphology, resulting in the exclusion of relevant data for studies. In this paper, we present a customizable data-centric system that automatically generates common misspellings for complex health-related terms, which can improve the data collection process from noisy text sources. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464183021
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http://dx.doi.org/10.1016/j.jbi.2018.11.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322919PMC
December 2018
7 Reads

Manifold regularized matrix factorization for drug-drug interaction prediction.

J Biomed Inform 2018 Dec 13;88:90-97. Epub 2018 Nov 13.

Department of Computer Science and Engineering, The Ohio State University, OH 43210, USA. Electronic address:

Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464183021
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http://dx.doi.org/10.1016/j.jbi.2018.11.005DOI Listing
December 2018
11 Reads

CIBS: A biomedical text summarizer using topic-based sentence clustering.

Authors:
Milad Moradi

J Biomed Inform 2018 Dec 13;88:53-61. Epub 2018 Nov 13.

Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address:

Automatic text summarizers can reduce the time required to read lengthy text documents by extracting the most important parts. Multi-document summarizers should produce a summary that covers the main topics of multiple related input texts to diminish the extent of redundant information. In this paper, we propose a novel summarization method named Clustering and Itemset mining based Biomedical Summarizer (CIBS). Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.006DOI Listing
December 2018
12 Reads

A framework for data-driven adaptive GUI generation based on DICOM.

J Biomed Inform 2018 Dec 9;88:37-52. Epub 2018 Nov 9.

Dipartimento dell'Innovazione Industriale e Digitale (DIID), Università degli Studi di Palermo, Viale delle Scienze, Ed.8, 90133 Palermo, Italy.

Computer applications for diagnostic medical imaging provide generally a wide range of tools to support physicians in their daily diagnosis activities. Unfortunately, some functionalities are specialized for specific diseases or imaging modalities, while other ones are useless for the images under investigation. Nevertheless, the corresponding Graphical User Interface (GUI) widgets are still present on the screen reducing the image visualization area. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464183020
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http://dx.doi.org/10.1016/j.jbi.2018.10.009DOI Listing
December 2018
15 Reads

A method for harmonization of clinical abbreviation and acronym sense inventories.

J Biomed Inform 2018 Dec 7;88:62-69. Epub 2018 Nov 7.

Department of Biomedical Informatics, Columbia University, New York, NY, USA; Value Institute, NewYork-Presbyterian Hospital, New York, NY, USA.

Background: Previous research has developed methods to construct acronym sense inventories from a single institutional corpus. Although beneficial, a sense inventory constructed from a single institutional corpus is not generalizable, because acronyms from different geographic regions and medical specialties vary greatly.

Objective: Develop an automated method to harmonize sense inventories from different regions and specialties towards the development of a comprehensive inventory. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.004DOI Listing
December 2018
8 Reads

A smartwatch-based framework for real-time and online assessment and mobility monitoring.

J Biomed Inform 2019 Jan 7;89:29-40. Epub 2018 Nov 7.

University of Florida, Gainesville, FL 32611, United States; Johns Hopkins University, Baltimore, MD 21205, United States.

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.003DOI Listing
January 2019
17 Reads

Lifetime trajectory simulation of chronic disease progression and comorbidity development.

J Biomed Inform 2018 Dec 7;88:29-36. Epub 2018 Nov 7.

Health Services & Outcomes Research, National Healthcare Group, Singapore.

Introduction: Comorbidity is common in elderly patients and it imposes heavy burden on both individual and the whole healthcare system. This study aims to gain insights of comorbidity development by simulating the lifetime trajectory of disease progression from single chronic disease to comorbidity.

Methods: Eight health states spanning from no chronic condition to comorbidity are considered in this study. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.002DOI Listing
December 2018
1 Read

Integrated Bioinformatics Analysis for Identificating the Therapeutic Targets of Aspirin in Small Cell Lung Cancer.

J Biomed Inform 2018 Dec 7;88:20-28. Epub 2018 Nov 7.

Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China. Electronic address:

Purpose: We explored the mechanism of aspirin in SCLC by dissecting many publicly available databases.

Methods And Results: Firstly, 11 direct protein targets (DPTs) of aspirin were identified by DrugBank 5.0. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.11.001DOI Listing
December 2018

CBN: Constructing a clinical Bayesian network based on data from the electronic medical record.

J Biomed Inform 2018 Dec 3;88:1-10. Epub 2018 Nov 3.

ICNLAB, School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, 518055 Shenzhen, PR China. Electronic address:

The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.10.007DOI Listing
December 2018
11 Reads

Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework.

J Biomed Inform 2018 Dec 30;88:70-89. Epub 2018 Oct 30.

Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran. Electronic address:

Background: One of the significant problems in the field of healthcare is the low survival rate of people who have experienced sudden cardiac arrest. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Traditional statistical methods have been used to predict cardiac arrest. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.10.008DOI Listing
December 2018

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances.

J Biomed Inform 2018 Dec 24;88:11-19. Epub 2018 Oct 24.

Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; South London and Maudsley NHS Foundation Trust, London, UK. Electronic address:

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.10.005DOI Listing
December 2018
9 Reads

The Internet of Things (IoT): Informatics methods for IoT-enabled health care.

Authors:
Po Yang Lida Xu

J Biomed Inform 2018 Nov 17;87:154-156. Epub 2018 Oct 17.

Old Dominion University, Norfolk, VA, USA. Electronic address:

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November 2018

Causal discovery from sequential data in ALS disease based on entropy criteria.

J Biomed Inform 2019 Jan 16;89:41-55. Epub 2018 Oct 16.

Computer Engineering Department, Iran University of Science and Technology, Narmak, Tehran, Iran. Electronic address:

One of the most important issues in predictive modeling is to determine major cause factors of a phenomenon and causal relationships between them. Extracting causal relationships between parameters in a natural phenomenon can be accomplished through checking the parameters' changes in consecutive events. In addition, using information and probabilistic theory help better conception of causal relationships of a phenomenon. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.10.004DOI Listing
January 2019
7 Reads

An evaluation method of risk grades for prostate cancer using similarity measure of cubic hesitant fuzzy sets.

J Biomed Inform 2018 Nov 16;87:131-137. Epub 2018 Oct 16.

Department of Electrical Engineering and Automation, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang Province 312000, PR China. Electronic address:

Prostate cancer (PC) is more common cancer in older men. Then, the existing evaluation method of PC risk grades is based on the AJCC (American Joint Committee on Cancer) staging/scoring system. It utilizes the comprehensive risk data of the prostate-specific antigen (PSA) test, Gleason score, and T staging score as the evaluation criteria of PC patients. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S15320464183019
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http://dx.doi.org/10.1016/j.jbi.2018.10.003DOI Listing
November 2018
13 Reads

Using neural attention networks to detect adverse medical events from electronic health records.

J Biomed Inform 2018 Nov 15;87:118-130. Epub 2018 Oct 15.

College of Biomedical Engineering and Instrument Science, Zhejiang University, PR China. Electronic address:

The detection of Adverse Medical Events (AMEs) plays an important role in disease management in ensuring efficient treatment delivery and quality improvement of health services. Recently, with the rapid development of hospital information systems, a large volume of Electronic Health Records (EHRs) have been produced, in which AMEs are regularly documented in a free-text manner. In this study, we are concerned with the problem of AME detection by utilizing a large volume of unstructured EHR data. Read More

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http://dx.doi.org/10.1016/j.jbi.2018.10.002DOI Listing
November 2018
18 Reads