2,089 results match your criteria Journal of Biomedical Informatics [Journal]


Evaluating automated entity extraction with respect to drug and non-drug treatment strategies.

J Biomed Inform 2019 Apr 12:103177. Epub 2019 Apr 12.

School of Information Sciences, University of Illinois at Urbana Champaign.

Objectives: Treatment used in a randomized clinical trial is a critical data element both for physicians at the point of care and reviewers who are evaluating different interventions. Much of existing work on treatment extraction from the biomedical literature has focused on the extraction of pharmacological interventions. However, non-pharmacological interventions (e. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193009
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103177DOI Listing
April 2019
2 Reads

A Mixed-Methods Evaluation Framework for Electronic Health Records Usability Studies.

J Biomed Inform 2019 Apr 11:103175. Epub 2019 Apr 11.

Pulmonary Diseases and Critical Care Medicine, University of North Carolina at Chapel Hill, NC, USA.

Background: Poor EHR design adds further challenges, especially in the areas of order entry and information visualization, with a net effect of increased rates of incidents, accidents, and mortality in ICU settings.

Objective: The purpose of the study was to propose a novel, mixed-methods framework to understand EHR-related information overload by identifying and characterizing areas of suboptimal usability and clinician frustration within a vendor-based, provider-facing EHR interface.

Methods: A mixed-methods, live observational usability study was conducted at a single, large, tertiary academic medical center in the Southeastern US utilizing a commercial, vendor based EHR. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193009
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103175DOI Listing
April 2019
1 Read

Cimind: A phonetic-based tool for multilingual named entity recognition in biomedical texts.

J Biomed Inform 2019 Apr 10:103176. Epub 2019 Apr 10.

Normandie Univ., TIBS - LITIS EA 4108, Rouen Normandy University, France; French National Institute for Health, INSERM, LIMICS UMR-1142, France.

Background: Extracting concepts from biomedical texts is a key to support many advanced applications such as biomedical information retrieval. However, in clinical notes Named Entity Recognition (NER) has to deal with various types of errors such as spelling errors, grammatical errors, truncated sentences, and non-standard abbreviations. Moreover, in numerous countries, NER is challenged by the availability of many resources originally developed and only suitable for English texts. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193009
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103176DOI Listing
April 2019
2 Reads

The New European Interoperability Framework as a Facilitator of Digital Transformation for Citizen Empowerment.

J Biomed Inform 2019 Apr 9:103166. Epub 2019 Apr 9.

Head of Center for eHealth Applications and Services, Foundation for Research & Technology-Hellas, Institute of Computer Science, FORTH-ICS, N. Plastira 100, Vassilika Vouton, GR-70013 Heraklion, Crete, Greece. Electronic address: http://www.ics.forth.gr/.

Healthcare is a highly regulated domain. Seamless, online access to integrated electronic health records for citizens is still far from becoming a reality. The implementation of personally managed health data systems still needs to overcome several interoperability, usability, ethics, security, and regulatory issues to deliver the envisioned benefits. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103166DOI Listing
April 2019
1 Read

A corpus to support eHealth Knowledge Discovery technologies.

J Biomed Inform 2019 Apr 6:103172. Epub 2019 Apr 6.

Department of Software and Computing Systems, University of Alicante, Carretera San Vicente del Raspeig s/n, 03690 Alicante (Spain). Electronic address:

This paper presents and describes eHealth-KD corpus. The corpus is a collection of 1173 Spanish health-related sentences manually annotated with a general semantic structure that captures most of the content, without resorting to domain-specific labels. The semantic representation is first defined and illustrated with example sentences from the corpus. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103172DOI Listing

A New Data Analysis Method Based on Feature Linear Combination.

J Biomed Inform 2019 Apr 6:103173. Epub 2019 Apr 6.

School of Computer Science & Technology, Dalian University of Technology, 116024 Dalian, China.

In biological data, feature relationships are complex and diverse, they could reflect physiological and pathological changes. Defining simple and efficient classification rules based on feature relationships is helpful for discriminating different conditions and studying disease mechanism. The popular data analysis method, k top scoring pairs (k-TSP), explores the feature relationship by focusing on the difference of the relative level of two features in different groups and classifies samples based on the exploration. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103173DOI Listing

Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data.

J Biomed Inform 2019 Apr 6:103174. Epub 2019 Apr 6.

Centre for Brain Research, The University of Auckland, New Zealand; University of Auckland, School of Biological Sciences, Private Bag 92019, Auckland 1142, New Zealand. Electronic address:

Background: Whole genome sequencing (WGS) has increased in popularity and decreased in cost over the past decade, rendering this approach as a viable and sensitive method for variant detection. In addition to its utility for single nucleotide variant detection, WGS data has the potential to detect Copy Number Variants (CNV) to fine resolution. Many CNV detection software packages have been developed exploiting four main types of data: read pair, split read, read depth, and assembly based methods. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103174DOI Listing

Assisting Radiologists with Reporting Urgent Findings to Referring Physicians: A Machine Learning Approach to Identify Cases for Prompt Communication.

J Biomed Inform 2019 Apr 5:103169. Epub 2019 Apr 5.

Computer Science Department, Dartmouth College, Hanover, NH 03755, USA; Biomedical Data Science Department, Dartmouth College, Hanover, NH 03755, USA; Epidemiology Department, Dartmouth College, Hanover, NH 03755, USA. Electronic address:

Radiologists are expected to expediently communicate critical and unexpected findings to referring clinicians to prevent delayed diagnosis and treatment of patients. However, competing demands such as heavy workload along with lack of administrative support resulted in communication failures that accounted for 7% of the malpractice payments made from 2004 to 2008 in the United States. To address this problem, we have developed a novel machine learning method that can automatically and accurately identify cases that require prompt communication to referring physicians based on analyzing the associated radiology reports. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103169DOI Listing

Pattern-based Strategic Surgical Capacity Allocation.

J Biomed Inform 2019 Apr 5:103170. Epub 2019 Apr 5.

Department of Health Sciences Research, Mayo Clinic, 200 1st Street SW, Rochester, Minnesota 55905. Electronic address:

Strategic allocation of limited operating room (OR) capacity to surgeons is crucial for the coordination of surgical work flow, including planning of consultation and surgery days, and staff assignment to perioperative teams. However, it is a challenging problem in practice, since the capacity allocation needs to be cyclic for schedule predictability and surgical team coordination, and also needs to satisfy surgeons' preferences. It is further complicated by the practice of surgeons sharing ORs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103170DOI Listing
April 2019
1 Read

Social network analysis for better understanding of influenza.

J Biomed Inform 2019 Mar 30:103161. Epub 2019 Mar 30.

Temple University, Center for Data Analytics and Biomedical Informatics (DABI), Philadelphia, PA, USA. Electronic address:

Introduction: The objective of this study is to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by creating heatmaps and social networks. They will allow to identify critical hubs and routes of spreading of Influenza, in specific geographic locations, in order to contain infections and prevent complications, that require hospitalizations.

Material And Methods: Data were downloaded from the Healthcare Cost and Utilization Project (HCUP) - SID, New York State database. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103161DOI Listing
March 2019
2 Reads

Massive integrative gene set analysis enables functional characterization of breast cancer subtypes.

J Biomed Inform 2019 Mar 27:103157. Epub 2019 Mar 27.

Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Universidad Católica de Córdoba, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina; Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Argentina. Electronic address:

The availability of large-scale repositories and integrated cancer genome efforts have created unprecedented opportunities to study and describe cancer biology. In this sense, the aim of translational researchers is the integration of multiple omics data to achieve a better identification of homogeneous subgroups of patients in order to develop adequate diagnostic and treatment strategies from the personalized medicine perspective. So far, existing integrative methods have grouped together omics data information, leaving out individual omics data phenotypic interpretation. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103157DOI Listing

Towards the first multi-epitope recombinant vaccine against Crimean-Congo hemorrhagic fever virus: A computer-aided vaccine design approach.

J Biomed Inform 2019 Mar 27:103160. Epub 2019 Mar 27.

Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran. Electronic address:

Crimean-Congo hemorrhagic fever (CCHF) is considered one of the major public health concerns with case fatality rates of up to 80%. Currently, there is no effective approved vaccine for CCHF. In this study, we used a computer-aided vaccine design approach to develop the first multi-epitope recombinant vaccine for CCHF. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103160DOI Listing
March 2019
1 Read
2.482 Impact Factor

Chief Complaint Classification with Recurrent Neural Networks.

J Biomed Inform 2019 Mar 26:103158. Epub 2019 Mar 26.

Centers for Disease Control and Prevention, Atlanta, GA.

Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse written fields and may benefit from the use of modern supervised-learning classifier models. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103158DOI Listing

A Comprehensive Review of Feature Based Methods for Drug Target Interaction Prediction.

J Biomed Inform 2019 Mar 26:103159. Epub 2019 Mar 26.

Computer Science and Engineering Department, SMVDU, J&K, India. Electronic address:

Drug target interaction is a prominent research area in the field of drug discovery. It refers to the recognition of interactions between chemical compounds and the protein targets in the human body. Wet lab experiments to identify these interactions are expensive as well as time consuming. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103159DOI Listing

Patient data discovery platforms as enablers of biomedical and translational research: a systematic review.

J Biomed Inform 2019 Mar 25:103154. Epub 2019 Mar 25.

IEETA/DETI, University of Aveiro, Portugal. Electronic address:

Background: The global shift from paper health records to electronic ones has led to an impressive growth of biomedical digital data along the past two decades. Exploring and extracting knowledge from these data has the potential to enhance translational research and lead to positive outcomes for the population's health and healthcare.

Obective: The aim of this study was to conduct a systematic review to identify software platforms that enable discovery, secondary use and interoperability of biomedical data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103154DOI Listing

Wearable Activity Trackers, Accuracy, Adoption, Acceptance and Health Impact: A Systematic Literature Review.

J Biomed Inform 2019 Mar 22:103153. Epub 2019 Mar 22.

School of Information and Library Science, University of North Carolina at Chapel Hill, USA. Electronic address:

Wearable activity trackers (WAT) are electronic monitoring devices that enable users to track and monitor their health-related physical fitness metrics including steps taken, level of activity, walking distance, heart rate, and sleep patterns. Despite the proliferation of these devices in various contexts of use and rising research interests, there is limited understanding of the broad research landscape. The purpose of this systematic review is therefore to synthesize the existing wealth of research on WAT, and to provide a comprehensive summary based on common themes and approaches. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193007
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103153DOI Listing
March 2019
2 Reads

Predicting Temporal Propagation of Seasonal Influenza Using Improved Gaussian Process Model.

J Biomed Inform 2019 Mar 21:103144. Epub 2019 Mar 21.

Shenzhen Center for Disease Control and Prevention, Shenzhen, 518073, China. Electronic address:

Influenza rapidly spreads in seasonal epidemics and imposes a considerable economic burden on hospitals and other healthcare costs. Thus, predicting the propagation of influenza accurately is crucial in preventing influenza outbreaks and protecting public health. Most current studies focus on the spread simulation of influenza. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193006
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103144DOI Listing
March 2019
5 Reads

Candidate gene prioritization for non-communicable diseases based on functional information: case studies.

J Biomed Inform 2019 Mar 19:103155. Epub 2019 Mar 19.

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150000, Heilongjiang Province, China.7. Electronic address:

Candidate gene prioritization for complex non-communicable diseases is essential to understanding the mechanism and developing better means for diagnosing and treating these diseases. Many methods have been developed to prioritize candidate genes in protein-protein interaction (PPI) networks. Integrating functional information/similarity into disease-related PPI networks could improve the performance of prioritization. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103155DOI Listing
March 2019
1 Read

Enhancing metabolic event extraction performance with multitask learning concept.

J Biomed Inform 2019 Mar 19:103156. Epub 2019 Mar 19.

Data Science and Engineering Laboratory, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand. Electronic address:

To extract and generate a valid metabolic pathway from research articles, biologists need substantial amounts of time to digest unstructured text. Text mining currently plays a central role in this research area, because it provides the ability to automatically discover useful information in a reasonable time. A text mining model can be built using a training data or a corpus in supervised manner. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103156DOI Listing

Creating the first national linked dataset on perinatal and maternal outcomes in Australia: Methods and challenges.

J Biomed Inform 2019 Mar 16:103152. Epub 2019 Mar 16.

Centre for Midwifery, Child and Family Health, Faculty of Health, University of Technology Sydney, NSW, Australia; Burnet Institute, Melbourne, Victoria, Australia.

Background: Data linkage offers a powerful mechanism for examining healthcare outcomes across populations and can generate substantial robust datasets using routinely collected electronic data. However, it presents methodological challenges, especially in Australia where eight separate states and territories maintain health datasets. This study used linked data to investigate perinatal and maternal outcomes in relation to place of birth. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193007
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103152DOI Listing
March 2019
3 Reads

Predicting anxiety state using smartphone-based passive sensing.

J Biomed Inform 2019 Mar 14:103151. Epub 2019 Mar 14.

Research into Artifacts, Center for Engineering (RACE), The University of Tokyo, Kashiwa-shi, Chiba, Japan.

This study predicts the change of stress levels using real-world and online behavioral features extracted from smartphone log information. Previous studies of stress detection using smartphone data focused on a single feature and did not consider all features simultaneously. We propose a method to extract a co-occurring combination of a user's real-world and online behavioral features by converting raw sensor data into categorical features. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103151DOI Listing
March 2019
2.482 Impact Factor

Experience and Reflection from China's Xiangya Medical Big Data Project.

J Biomed Inform 2019 Mar 13:103149. Epub 2019 Mar 13.

Information Security and Big Data Institute, Central South University, Changsha 410013 Hunan.

The construction of medical big data includes several problems that need to be solved, such as integration and data sharing of many heterogeneous information systems, efficient processing and analysis of large-scale medical data with complex structure or low degree of structure, and narrow application range of medical data. Therefore, medical big data construction is not only a simple collection and application of medical data but also a complex systematic project. This paper introduces China's experience in the construction of a regional medical big data ecosystem, including the overall goal of the project; establishment of policies to encourage data sharing; handling the relationship between personal privacy, information security, and information availability; establishing a cooperation mechanism between agencies; designing a polycentric medical data acquisition system; and establishing a large data centre. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103149DOI Listing

Unique device identification and traceability for medical software: A major challenge for manufacturers in an ever-evolving marketplace.

J Biomed Inform 2019 Mar 13:103150. Epub 2019 Mar 13.

Institute of Clinical Physiology, CNR, Pisa, Italy.

Background And Objectives: Similarly to what already established and implemented in the United States, the concept of the Unique Device Identification (UDI) system has been introduced with the European Regulations for medical devices MDR (EU) 2017/745 and in-vitro diagnostic medical devices IVDR (EU) 2017/746 and it is on the way to become a worldwide standard. The aim of this work was to provide a possible approach for the implementation of UDI and traceability in Europe for standalone software medical devices according to lifecycle and quality system standards.

Methods: The key points of the UDI regulation were determined and analyzed in order to identify the main issues related to the manufacturing of software medical devices and, in particular, labeling, privacy aspects, UDI assignment criteria, and international standards compliance. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103150DOI Listing

Multi-label Biomedical Question Classification for Lexical Answer Type Prediction.

J Biomed Inform 2019 Mar 11:103143. Epub 2019 Mar 11.

Al-Khawarizmi Institute of Computer Science, UET, Lahore, Pakistan.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type(LAT) Prediction. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103143DOI Listing
March 2019
2.482 Impact Factor

A Survey on Literature Based Discovery Approaches in Biomedical Domain.

J Biomed Inform 2019 Mar 8:103141. Epub 2019 Mar 8.

State University of New York at Buffalo, New York. Electronic address:

Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103141DOI Listing

Maintaining Automated Measurement of Choosing Wisely Adherence across the ICD 9 to 10 Transition.

J Biomed Inform 2019 Mar 7:103142. Epub 2019 Mar 7.

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center; Department of Psychiatry, Vanderbilt University Medical Center. Electronic address:

Background: It remains unclear how to incorporate terminology changes, such as the International Classification of Disease (ICD) transition from ICD-9 to ICD-10, into established automated healthcare quality metrics.

Objective: To evaluate whether general equivalence mapping (GEM) can apply ICD-9 based metrics to ICD-10 patient data. To develop and validate novel ICD-10 reference codesets. Read More

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193006
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103142DOI Listing
March 2019
2 Reads

Analyzing the performance of a blockchain-based personal health record implementation.

J Biomed Inform 2019 Apr 4;92:103140. Epub 2019 Mar 4.

Institute for Software Integrated Systems (ISIS), Vanderbilt University, 1025, 16th Ave So., Nashville, TN 37212, USA.

Background: The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103140DOI Listing
April 2019
1 Read

Reasoning about clinical guidelines based on algebraic data types and constraint logic programming.

Authors:
Beatriz Pérez

J Biomed Inform 2019 Apr 1;92:103134. Epub 2019 Mar 1.

Department of Mathematics and Computer Science, University of La Rioja, C/ Madre de Dios 53 (Edificio Científico Tecnológico), E-26006 La Rioja, Spain. Electronic address:

Previously, the authors presented an overall framework aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. Regarding the quality improvement of guidelines, we developed a proposal to verify specific requirements in guidelines, using the SPIN model checker as verification tool. Additionally, we established a pattern-based approach for defining commonly occurring types of requirements in guidelines, in order to help non experts in their formal specification. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103134DOI Listing
April 2019
1 Read

Enabling older adults to carry out paperless falls-risk self-assessments using guidetomeasure-3D: A mixed methods study.

J Biomed Inform 2019 Apr 28;92:103135. Epub 2019 Feb 28.

School of Health & Social Care, London South Bank University, 103 Borough Road, London SE1 0AA, UK. Electronic address:

Background: The home environment falls-risk assessment process (HEFAP) is a widely used falls prevention intervention strategy which involves a clinician using paper-based measurement guidance to ensure that appropriate information and measurements are taken and recorded accurately. Despite the current use of paper-based guidance, over 30% of all assistive devices installed within the home are abandoned by patients. This is in part due to poor fit between the device, the patient, and the environment in which it is installed. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103135DOI Listing

Distributed learning from multiple EHR databases: Contextual embedding models for medical events.

J Biomed Inform 2019 Apr 27;92:103138. Epub 2019 Feb 27.

University of Pennsylvania, Perelman School of Medicine, Department of Biostatistics, Epidemiology and Informatics, Philadelphia, PA 19104, USA. Electronic address:

Electronic health record (EHR) data provide promising opportunities to explore personalized treatment regimes and to make clinical predictions. Compared with regular clinical data, EHR data are known for their irregularity and complexity. In addition, analyzing EHR data involves privacy issues and sharing such data is often infeasible among multiple research sites due to regulatory and other hurdles. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103138DOI Listing

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

J Biomed Inform 2019 Apr 27;92:103139. Epub 2019 Feb 27.

Bogazici University, Computer Engineering Department, Turkey.

Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103139DOI Listing
April 2019
2 Reads

Incorporating dictionaries into deep neural networks for the Chinese clinical named entity recognition.

J Biomed Inform 2019 Apr 25;92:103133. Epub 2019 Feb 25.

Shanghai Hospital Development Center, Shanghai 200040, China. Electronic address:

Clinical named entity recognition aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other natural language processing tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103133DOI Listing
April 2019
1 Read

Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.

J Biomed Inform 2019 Apr 23;92:103137. Epub 2019 Feb 23.

Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

We propose an efficient natural language processing approach for inferring the BI-RADS final assessment categories by analyzing only the mammogram findings reported by the mammographer in narrative form. The proposed hybrid method integrates semantic term embedding with distributional semantics, producing a context-aware vector representation of unstructured mammography reports. A large corpus of unannotated mammography reports (300,000) was used to learn the context of the key-terms using a distributional semantics approach, and the trained model was applied to generate context-aware vector representations of the reports annotated with BI-RADS category (22,091). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103137DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462247PMC
April 2019
1 Read

Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.

J Biomed Inform 2019 Apr 22;92:103136. Epub 2019 Feb 22.

Dept. of Computer Applications, North-Eastern Hill University, Tura Campus, Meghalaya 794002, India. Electronic address:

Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of clinically labeled patterns are very less. In such situation, active learning technique can play an important role as it computationally selects only few most informative (confusing) samples to be labeled by the experts and are added to the training set which inturn can improve the accuracy of the prediction. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103136DOI Listing

MCN: A comprehensive corpus for medical concept normalization.

J Biomed Inform 2019 Apr 22;92:103132. Epub 2019 Feb 22.

Computer Science Department, University of Massachusetts Lowell, MA, USA.

Normalization of clinical text involves linking different ways of talking about the same clinical concept to the same term in the standardized vocabulary. To date, very few annotated corpora for normalization have been available, and existing corpora so far have been limited in scope and only dealt with the normalization of diseases and disorders. In this paper, we describe the annotation methodology we developed in order to create a new manually annotated wide-coverage corpus for clinical concept normalization, the Medical Concept Normalization (MCN) corpus. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103132DOI Listing
April 2019
2 Reads

Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis.

J Biomed Inform 2019 Apr 20;92:103124. Epub 2019 Feb 20.

School of Information Science and Engineering, Shandong Normal University, Jinan City, China; Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan City, China.

Microarray technique is a prevalent method for the classification and prediction of colorectal cancer (CRC). Nevertheless, microarray data suffers from the curse of dimensionality when selecting feature genes of the disease based on imbalance samples, thus causing low prediction accuracy. Hence, it is of vital significance to build proper models that can avoid the above problems and predict the CRC more accurately. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103124DOI Listing

Multiple retrieval case-based reasoning for incomplete datasets.

J Biomed Inform 2019 Apr 13;92:103127. 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

View Article

Download full-text PDF

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

Statistical outbreak detection by joining medical records and pathogen similarity.

J Biomed Inform 2019 Mar 13;91:103126. Epub 2019 Feb 13.

Auton Lab, Carnegie Mellon University, Pittsburgh, PA, United States.

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103126DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424617PMC

Automatic ICD code assignment of Chinese clinical notes based on multilayer attention BiRNN.

J Biomed Inform 2019 Mar 12;91:103114. Epub 2019 Feb 12.

School of Computer Science and Engineering, Central South University, Changsha 410083, China. 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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103114DOI Listing
March 2019
1 Read

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

J Biomed Inform 2019 Apr 10;92:103115. Epub 2019 Feb 10.

Stanford University, Palo Alto, 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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103115DOI Listing
April 2019
2 Reads

Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model.

J Biomed Inform 2019 Mar 10;91:103116. Epub 2019 Feb 10.

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103116DOI Listing
March 2019
1 Read

Unsupervised concept extraction from clinical text through semantic composition.

J Biomed Inform 2019 Mar 10;91:103120. Epub 2019 Feb 10.

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103120DOI Listing
March 2019
2 Reads

Word embeddings and external resources for answer processing in biomedical factoid question answering.

J Biomed Inform 2019 Apr 10;92:103118. Epub 2019 Feb 10.

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

View Article

Download full-text PDF

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

Confirm or refute?: A comparative study on citation sentiment classification in clinical research publications.

J Biomed Inform 2019 Mar 10;91:103123. Epub 2019 Feb 10.

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

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103123DOI Listing
March 2019
1 Read

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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103125DOI Listing
February 2019
4 Reads

Feature extraction for phenotyping from semantic and knowledge resources.

J Biomed Inform 2019 Mar 7;91:103122. 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

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193004
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6424621PMC
March 2019
11 Reads

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

J Biomed Inform 2019 Apr 7;92:103117. 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 1149 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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103117DOI Listing
April 2019
5 Reads
2.482 Impact Factor

Computational prediction of the bioactivity potential of proteomes based on expert knowledge.

J Biomed Inform 2019 Mar 7;91:103121. 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

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jbi.2019.103121DOI Listing
March 2019
1 Read

ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model.

J Biomed Inform 2019 Mar 7;91:103119. Epub 2019 Feb 7.

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

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 database. 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

View Article

Download full-text PDF

Source
https://linkinghub.elsevier.com/retrieve/pii/S15320464193003
Publisher Site
http://dx.doi.org/10.1016/j.jbi.2019.103119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6432939PMC
March 2019
5 Reads

Content based medical image retrieval using topic and location model.

J Biomed Inform 2019 Mar 6;91:103112. Epub 2019 Feb 6.

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

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 and Location Model. Read More

View Article

Download full-text PDF

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