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Journal of Biomedical Informatics

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https://read.qxmd.com/read/30880254/predicting-anxiety-state-using-smartphone-based-passive-sensing
#1
Yusuke Fukazawa, Taku Ito, Tsukasa Okimura, Yuichi Yamashita, Takaki Maeda, Jun Ota
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. We conducted an experiment in which the State Trait Anxiety Inventory (STAI) was used to assess the anxiety-related stress levels of 20 healthy participants...
March 14, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30878618/experience-and-reflection-from-china-s-xiangya-medical-big-data-project
#2
Bei Li, Jianbin Li, Yuqiao Jiang, Xiaoyun Lan
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...
March 13, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30878617/unique-device-identification-and-traceability-for-medical-software-a-major-challenge-for-manufacturers-in-an-ever-evolving-marketplace
#3
Elisabetta Bianchini, Martina Francesconi, Marisa Testa, Maya Tanase, Vincenzo Gemignani
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...
March 13, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30872137/multi-label-biomedical-question-classification-for-lexical-answer-type-prediction
#4
Muhammad Wasim, Muhammad Nabeel Asim, Muhammad Usman Ghani Khan, Waqar Mahmood
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. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field...
March 11, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30857950/a-survey-on-literature-based-discovery-approaches-in-biomedical-domain
#5
REVIEW
Vishrawas Gopalakrishnan, Kishlay Jha, Wei Jin, Aidong Zhang
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...
March 8, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30853653/maintaining-automated-measurement-of-choosing-wisely-adherence-across-the-icd-9-to-10-transition
#6
John Angiolillo, S Trent Rosenbloom, Melissa McPheeters, G Seibert Tregoning, Russell L Rothman, Colin G Walsh
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. DESIGN: Retrospective analysis for eleven Choosing Wisely (CW) metrics was performed using three scripted algorithms on an institutional clinical data warehouse...
March 7, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30844481/analyzing-the-performance-of-a-blockchain-based-personal-health-record-implementation
#7
Alex Roehrs, Cristiano André da Costa, Rodrigo da Rosa Righi, Valter Ferreira da Silva, José Roberto Goldim, Douglas C Schmidt
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. OBJECTIVE: This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard...
March 4, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30831315/reasoning-about-clinical-guidelines-based-on-algebraic-data-types-and-constraint-logic-programming
#8
Beatriz Pérez
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...
March 1, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30826542/enabling-older-adults-to-carry-out-paperless-falls-risk-self-assessments-using-guidetomeasure-3d-a-mixed-methods-study-corresponding-author-dr-arthur-money
#9
Julian Hamm, Arthur G Money, Anita Atwal
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...
February 28, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30825539/distributed-learning-from-multiple-ehr-databases-contextual-embedding-models-for-medical-events
#10
Ziyi Li, Kirk Roberts, Xiaoqian Jiang, Qi Long
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. A recently published work uses contextual embedding models and successfully builds one predictive model for more than seventy common diagnoses...
February 27, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30825538/stress-detection-in-daily-life-scenarios-using-smart-phones-and-wearable-sensors-a-survey
#11
REVIEW
Yekta Said Can, Bert Arnrich, Cem Ersoy
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. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited...
February 27, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30818005/incorporating-dictionaries-into-deep-neural-networks-for-the-chinese-clinical-named-entity-recognition
#12
Qi Wang, Yangming Zhou, Tong Ruan, Daqi Gao, Yuhang Xia, Ping He
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...
February 25, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30807833/automatic-inference-of-bi-rads-final-assessment-categories-from-narrative-mammography-report-findings
#13
Imon Banerjee, Selen Bozkurt, Emel Alkim, Hersh Sagreiya, Allison W Kurian, Daniel L Rubin
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)...
February 23, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30802546/active-learning-using-rough-fuzzy-classifier-for-cancer-prediction-from-microarray-gene-expression-data
#14
Anindya Halder, Ansuman Kumar
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...
February 22, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30802545/mcn-a-comprehensive-corpus-for-medical-concept-normalization
#15
REVIEW
Yen-Fu Luo, Weiyi Sun, Anna Rumshisky
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...
February 22, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30796977/whale-optimized-mixed-kernel-function-of-support-vector-machine-for-colorectal-cancer-diagnosis
#16
Dandan Zhao, Hong Liu, Yuanjie Zheng, Yanlin He, Dianjie Lu, Chen Lv
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 with more accurately. In this paper, we use an ensemble model to classify samples into healthy and CRC groups and improve prediction performance...
February 20, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30771484/multiple-retrieval-case-based-reasoning-for-incomplete-datasets
#17
Nikolas Löw, Jürgen Hesser, Manuel Blessing
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...
February 13, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30771483/statistical-outbreak-detection-by-joining-medical-records-and-pathogen-similarity
#18
James K Miller, Jieshi Chen, Alexander Sundermann, Jane W Marsh, Melissa I Saul, Kathleen A Shutt, Marissa Pacey, Mustapha M Mustapha, Lee H Harrison, Artur Dubrawski
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...
February 13, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30768971/automatic-icd-code-assignment-of-chinese-clinical-notes-based-on-multilayer-attention-birnn
#19
Ying Yu, Min Li, Liangliang Liu, Zhihui Fei, Fang-Xiang Wu, Jianxin Wang
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. Taking the lengthy text of patient notes and the representation of Chinese words into account, we present a multilayer attention bidirectional recurrent neural network (MA-BiRNN) model to implement the assignment of disease codes...
February 12, 2019: Journal of Biomedical Informatics
https://read.qxmd.com/read/30753951/predicting-need-for-advanced-illness-or-palliative-care-in-a-primary-care-population-using-electronic-health-record-data
#20
Kenneth Jung, Sylvia E K Sudat, Nicole Kwon, Walter F Stewart, Nigam H Shah
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. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value...
February 9, 2019: Journal of Biomedical Informatics
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