1,609 results match your criteria BMC medical informatics and decision making[Journal]


Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.

BMC Med Inform Decis Mak 2019 Apr 18;19(1):86. Epub 2019 Apr 18.

Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, UK.

Background: COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records.

Methods: We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. Read More

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http://dx.doi.org/10.1186/s12911-019-0805-0DOI Listing

Falls Sensei: a serious 3D exploration game to enable the detection of extrinsic home fall hazards for older adults.

BMC Med Inform Decis Mak 2019 Apr 16;19(1):85. Epub 2019 Apr 16.

Programonks, Eleftheriou Venizelou 1 Athienou, 7600, Larnaca, Cyprus.

Background: Falls are the main cause of death and injury for older adults in the UK. Many of these falls occur within the home as a result of extrinsic falls risk factors such as poor lighting, loose/uneven flooring, and clutter. Falls education plays an important role in self-management education about extrinsic hazards and is typically delivered via information leaflets, falls apps, and educational booklets. Read More

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http://dx.doi.org/10.1186/s12911-019-0808-xDOI Listing

Talking about treatment benefits, harms, and what matters to patients in radiation oncology: an observational study.

BMC Med Inform Decis Mak 2019 Apr 11;19(1):84. Epub 2019 Apr 11.

Department of Family and Emergency Medicine, Faculty of Medicine, Laval University, Pavillon Ferdinand-Vandry 2881, 1050 avenue de la Médecine, Quebec City, QC, G1V 0A6, Canada.

Background: Shared decision making is associated with improved patient outcomes in radiation oncology. Our study aimed to capture how shared decision-making practices-namely, communicating potential harms and benefits and discussing what matters to patients-occur in usual care.

Methods: We invited a convenience sample of clinicians and patients in a radiation oncology clinic to participate in a mixed methods study. Read More

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http://dx.doi.org/10.1186/s12911-019-0800-5DOI Listing

Discovering thematic change and evolution of utilizing social media for healthcare research.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):50. Epub 2019 Apr 9.

The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.

Background: Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field.

Methods: With the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis. Read More

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http://dx.doi.org/10.1186/s12911-019-0757-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454597PMC

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):65. Epub 2019 Apr 9.

Communication & Computer Network Lab of Guangdong, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.

Background: The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese abbreviations and doctors' personal style may result in multiple expressions of the same entity, and we lack a common Chinese medical dictionary to perform accurate entity extraction. Secondly, the electronic medical record contains entities from a variety of categories of entities, and the length of those entities in different categories varies greatly, which increases the difficult in the extraction for the Chinese NER. Read More

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http://dx.doi.org/10.1186/s12911-019-0762-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454585PMC

A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 2):51. Epub 2019 Apr 4.

The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China.

Background: Disease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses on the prediction of one target disease, and little work is proposed for multiple associated diseases prediction. Meanwhile, a piece of EHR usually contains two main information: the textual description and physical indicators. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0765-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454594PMC
April 2019
1 Read

Applying deep matching networks to Chinese medical question answering: a study and a dataset.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):52. Epub 2019 Apr 9.

Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, China.

Background: Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the difficulty of Chinese text processing and the lack of large-scale datasets. Read More

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http://dx.doi.org/10.1186/s12911-019-0761-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454599PMC

Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):60. Epub 2019 Apr 9.

School of Computer Science and Technology, Dalian University of Technology, No. 2, Linggong Road, Dalian, 116024, China.

Background: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. However, existing studies simply apply naive RL algorithms in discovering optimal treatment strategies for a targeted problem. This kind of direct applications ignores the abundant causal relationships between treatment options and the associated outcomes that are inherent in medical domains. Read More

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http://dx.doi.org/10.1186/s12911-019-0755-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454675PMC

A fine-grained Chinese word segmentation and part-of-speech tagging corpus for clinical text.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):66. Epub 2019 Apr 9.

Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China.

Background: Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks of Chinese text processing. They are usually preliminary steps for lots of Chinese natural language processing (NLP) tasks. There have been a large number of studies on CWS and POS tagging in various domains, however, few studies have been proposed for CWS and POS tagging in the clinical domain as it is not easy to determine granularity of words. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0770-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454584PMC
April 2019
4 Reads

Relation path feature embedding based convolutional neural network method for drug discovery.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):59. Epub 2019 Apr 9.

Department of VIP, the Second Hospital of Dalian Medical University, Dalian, China.

Background: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.

Methods: Here, we propose a relation path features embedding based convolutional neural network model with attention mechanism for drug discovery from literature, which we denote as PACNN. Read More

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http://dx.doi.org/10.1186/s12911-019-0764-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454669PMC

A hybrid approach for named entity recognition in Chinese electronic medical record.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):64. Epub 2019 Apr 9.

Institute of Computer Application, China Academic of Engineering Physics, Mianyang, China.

Background: With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic.

Methods: In this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0767-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454595PMC
April 2019
2 Reads
1.496 Impact Factor

Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):56. Epub 2019 Apr 9.

School of software, Central South University, Changsha, People's Republic of China.

Background: Electronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors' decision making. The research on information extraction of Chinese Electronic Medical Records is still behind that has done in English. Read More

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http://dx.doi.org/10.1186/s12911-019-0759-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454673PMC
April 2019
1 Read

An approach for transgender population information extraction and summarization from clinical trial text.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):62. Epub 2019 Apr 9.

School of Computer Science, South China Normal University, Guangzhou, China.

Background: Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains the incompleteness and ambiguity issues in expressing transgender population, leading to difficulties or even failure of transgender population recruitment in clinical trial studies.

Methods: A new gender model is proposed for providing comprehensive transgender requirement specification. Read More

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http://dx.doi.org/10.1186/s12911-019-0768-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454593PMC

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):57. Epub 2019 Apr 9.

School of Computer Science and Technology, Dalian University of Technology, Dalian, China.

Background: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain knowledge should be specified beforehand to indicate the goal of tasks. However, there is usually no explicit information regarding the reward function in medical records. Read More

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http://dx.doi.org/10.1186/s12911-019-0763-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454602PMC

Discover high-risk factor combinations using Bayesian network from cohort data of National Stoke Screening in China.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):67. Epub 2019 Apr 9.

Information Center, Academy of Military Medical Sciences, Beijing, China.

Background: In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples' Republic of China initiated the national stroke screening and intervention program in 2011 for stroke prevention and control. In the screening, only those who have been classified to "potential high-risk" group in preliminary screening need further examination and physician confirmation to determine the risk level of stroke in rescreening. Read More

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http://dx.doi.org/10.1186/s12911-019-0753-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454672PMC

An approach for medical event detection in Chinese clinical notes of electronic health records.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):54. Epub 2019 Apr 9.

Department of Electronic Engineering, Tsinghua University, Beijing, China.

Background: Medical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection treats the task as extracting concepts at word granularity, which omits the overall structural information of the clinical notes. In this work, we treat each clinical note as a sequence of short sentences and propose an end-to-end deep neural network framework. Read More

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http://dx.doi.org/10.1186/s12911-019-0756-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454668PMC

Evidential MACE prediction of acute coronary syndrome using electronic health records.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):61. Epub 2019 Apr 9.

College of Biomedical Engineering and Instrument Science, Zhejiang University, Key Lab for Biomedical Engineering of Ministry of Education, Zheda Road, Hangzhou, China.

Background: Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals.

Methods: To remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. Read More

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http://dx.doi.org/10.1186/s12911-019-0754-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454666PMC
April 2019
1 Read

Hierarchical sequence labeling for extracting BEL statements from biomedical literature.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):63. Epub 2019 Apr 9.

School of Computer Science and Technology, Soochow University, Suzhou, China.

Background: Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a shared task to evaluate the robustness of the causal relationship extraction algorithms in Biological Expression Language (BEL) from biomedical literature.

Method: We first map the sentence-level BEL statements in the BC-V training corpus to the corresponding text segments, thus generating hierarchically tagged training instances. Read More

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http://dx.doi.org/10.1186/s12911-019-0758-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454591PMC

On building a diabetes centric knowledge base via mining the web.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):49. Epub 2019 Apr 9.

Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Pu'an Road, Shanghai, China.

Background: Diabetes has become one of the hot topics in life science researches. To support the analytical procedures, researchers and analysts expend a mass of labor cost to collect experimental data, which is also error-prone. To reduce the cost and to ensure the data quality, there is a growing trend of extracting clinical events in form of knowledge from electronic medical records (EMRs). Read More

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http://dx.doi.org/10.1186/s12911-019-0771-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454670PMC

Attention-based deep residual learning network for entity relation extraction in Chinese EMRs.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):55. Epub 2019 Apr 9.

College of Computer Science and Engineering,Northwest Normal University, 967 Anning East Road, Lanzhou, 730070, China.

Background: Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0769-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454667PMC
April 2019
1 Read

Time-sensitive clinical concept embeddings learned from large electronic health records.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):58. Epub 2019 Apr 9.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Background: Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. Read More

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http://dx.doi.org/10.1186/s12911-019-0766-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454598PMC

An ontological framework for the formalization, organization and usage of TCM-Knowledge.

BMC Med Inform Decis Mak 2019 Apr 9;19(Suppl 2):53. Epub 2019 Apr 9.

Institute of Medical Informatics, Statistics and Epidemiology (IMISE) University of Leipzig, Leipzig, Germany.

Background: The traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more than 120,000 concepts, 300,000 terms and 1.27 million semantic relational links are included. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
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http://dx.doi.org/10.1186/s12911-019-0760-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6454592PMC
April 2019
4 Reads

Mobile phone applications to overcome malnutrition among preschoolers: a systematic review.

BMC Med Inform Decis Mak 2019 Apr 5;19(1):83. Epub 2019 Apr 5.

Department of Health Information Technology, Urmia University of Medical Sciences, Nazloo Campus, Sero Road, Urmia, Iran.

Background: Malnutrition is one of the most important reasons for child mortality in developing countries, especially during the first 5 years of life. We set out to systematically review evaluations of interventions that use mobile phone applications to overcome malnutrition among preschoolers.

Methods: The review was conducted and reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses: the PRISMA statement. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
Publisher Site
http://dx.doi.org/10.1186/s12911-019-0803-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6451239PMC
April 2019
3 Reads

Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):75. Epub 2019 Apr 4.

Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA.

Background: Numbers and numerical concepts appear frequently in free text clinical notes from electronic health records. Knowledge of the frequent lexical variations of these numerical concepts, and their accurate identification, is important for many information extraction tasks. This paper describes an analysis of the variation in how numbers and numerical concepts are represented in clinical notes. Read More

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http://dx.doi.org/10.1186/s12911-019-0784-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448181PMC
April 2019
1 Read

Facilitating accurate health provider directories using natural language processing.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):80. Epub 2019 Apr 4.

Center for Quantitative Medicine, University of Connecticut Health Center, Farmington, CT, 06030, USA.

Background: Accurate information in provider directories are vital in health care including health information exchange, health benefits exchange, quality reporting, and in the reimbursement and delivery of care. Maintaining provider directory data and keeping it up to date is challenging. The objective of this study is to determine the feasibility of using natural language processing (NLP) techniques to combine disparate resources and acquire accurate information on health providers. Read More

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http://dx.doi.org/10.1186/s12911-019-0788-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448184PMC

Developing a portable natural language processing based phenotyping system.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):78. Epub 2019 Apr 4.

Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.

Background: This paper presents a portable phenotyping system that is capable of integrating both rule-based and statistical machine learning based approaches.

Methods: Our system utilizes UMLS to extract clinically relevant features from the unstructured text and then facilitates portability across different institutions and data systems by incorporating OHDSI's OMOP Common Data Model (CDM) to standardize necessary data elements. Our system can also store the key components of rule-based systems (e. Read More

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http://dx.doi.org/10.1186/s12911-019-0786-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448187PMC

Identifying peer experts in online health forums.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):68. Epub 2019 Apr 4.

Department of Computer Science, University of California, Los Angeles, 404 Westwood Plaza, Los Angeles, 90095, CA, USA.

Background: Online health forums have become increasingly popular over the past several years. They provide members with a platform to network with peers and share information, experiential advice, and support. Among the members of health forums, we define "peer experts" as a set of lay users who have gained expertise on the particular health topic through personal experience, and who demonstrate credibility in responding to questions from other members. Read More

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http://dx.doi.org/10.1186/s12911-019-0782-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448182PMC

Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):74. Epub 2019 Apr 4.

Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, (Shenzhen), Shenzhen, 518055, China.

Background: Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text. Read More

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http://dx.doi.org/10.1186/s12911-019-0787-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448175PMC

EHR problem list clustering for improved topic-space navigation.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):72. Epub 2019 Apr 4.

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Background: The amount of patient-related information within clinical information systems accumulates over time, especially in cases where patients suffer from chronic diseases with many hospitalizations and consultations. The diagnosis or problem list is an important feature of the electronic health record, which provides a dynamic account of a patient's current illness and past history. In the case of an Austrian hospital network, problem list entries are limited to fifty characters and are potentially linked to ICD-10. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
Publisher Site
http://dx.doi.org/10.1186/s12911-019-0789-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448176PMC
April 2019
5 Reads
1.496 Impact Factor

A two-site survey of medical center personnel's willingness to share clinical data for research: implications for reproducible health NLP research.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):70. Epub 2019 Apr 4.

Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, 84108, USA.

Background: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible).

Methods: We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Read More

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http://dx.doi.org/10.1186/s12911-019-0778-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448185PMC
April 2019
1 Read

Special issue of BMC medical informatics and decision making on health natural language processing.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):76. Epub 2019 Apr 4.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

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http://dx.doi.org/10.1186/s12911-019-0777-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448180PMC

Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):71. Epub 2019 Apr 4.

Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago 60611, IL, USA.

Background: Clinical text classification is an fundamental problem in medical natural language processing. Existing studies have cocnventionally focused on rules or knowledge sources-based feature engineering, but only a limited number of studies have exploited effective representation learning capability of deep learning methods.

Methods: In this study, we propose a new approach which combines rule-based features and knowledge-guided deep learning models for effective disease classification. Read More

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http://dx.doi.org/10.1186/s12911-019-0781-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448186PMC

Discovering associations between problem list and practice setting.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):69. Epub 2019 Apr 4.

Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.

Background: The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients' outcomes. One of the core criteria for "Meaningful Use" of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. Read More

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http://dx.doi.org/10.1186/s12911-019-0779-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448189PMC

Parsing clinical text using the state-of-the-art deep learning based parsers: a systematic comparison.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):77. Epub 2019 Apr 4.

School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

Background: A shareable repository of clinical notes is critical for advancing natural language processing (NLP) research, and therefore a goal of many NLP researchers is to create a shareable repository of clinical notes, that has breadth (from multiple institutions) as well as depth (as much individual data as possible).

Methods: We aimed to assess the degree to which individuals would be willing to contribute their health data to such a repository. A compact e-survey probed willingness to share demographic and clinical data categories. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
Publisher Site
http://dx.doi.org/10.1186/s12911-019-0783-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448179PMC
April 2019
7 Reads

Extracting health-related causality from twitter messages using natural language processing.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):79. Epub 2019 Apr 4.

Medical Informatics, Kaiser Permanente Southern California, San Diego, CA, 92130, USA.

Background: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques. Read More

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http://dx.doi.org/10.1186/s12911-019-0785-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448183PMC
April 2019
2 Reads
1.496 Impact Factor

Natural language processing of radiology reports for identification of skeletal site-specific fractures.

BMC Med Inform Decis Mak 2019 Apr 4;19(Suppl 3):73. Epub 2019 Apr 4.

Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 1st ST SW, Rochester, MN, USA.

Background: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk of osteoporosis-related fractures. Accurate identification and surveillance of patient populations with fractures has a significant impact on reduction of cost of care by preventing future fractures and its corresponding complications. Read More

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http://dx.doi.org/10.1186/s12911-019-0780-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448178PMC
April 2019
4 Reads

QAnalysis: a question-answer driven analytic tool on knowledge graphs for leveraging electronic medical records for clinical research.

BMC Med Inform Decis Mak 2019 Apr 1;19(1):82. Epub 2019 Apr 1.

Shanghai Shuguang Hospital, Shanghai, 200021, China.

Background: While doctors should analyze a large amount of electronic medical record (EMR) data to conduct clinical research, the analyzing process requires information technology (IT) skills, which is difficult for most doctors in China.

Methods: In this paper, we build a novel tool QAnalysis, where doctors enter their analytic requirements in their natural language and then the tool returns charts and tables to the doctors. For a given question from a user, we first segment the sentence, and then we use grammar parser to analyze the structure of the sentence. Read More

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http://dx.doi.org/10.1186/s12911-019-0798-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444506PMC
April 2019
1 Read

Correction to: Using decision fusion methods to improve outbreak detection in disease surveillance.

BMC Med Inform Decis Mak 2019 03 28;19(1):81. Epub 2019 Mar 28.

French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.

Following publication of the original article [1], the authors reported that one of the authors' names is spelled incorrectly. Read More

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http://dx.doi.org/10.1186/s12911-019-0802-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440117PMC

Predicting factors for survival of breast cancer patients using machine learning techniques.

BMC Med Inform Decis Mak 2019 03 22;19(1):48. Epub 2019 Mar 22.

Data Science and Bioinformatics Laboratory, Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.

Background: Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate. Read More

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http://dx.doi.org/10.1186/s12911-019-0801-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6431077PMC
March 2019
1 Read

The use of echocardiographic and clinical data recorded on admission to simplify decision making for elective percutaneous coronary intervention: a prospective cohort study.

BMC Med Inform Decis Mak 2019 03 18;19(1):46. Epub 2019 Mar 18.

Department of Health Management and Policy, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.

Background: Coronary artery disease (CAD), a leading cause of mortality, affects patient health-related quality of life (HRQoL). Elective percutaneous coronary interventions (ePCIs) are usually performed to improve HRQoL of CAD patients. The aim of this study was to design models using admission data to predict the outcomes of the ePCI treatments on the patients' HRQoL. Read More

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http://dx.doi.org/10.1186/s12911-019-0797-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421658PMC
March 2019
1 Read

Considering patient safety in autonomous e-mental health systems - detecting risk situations and referring patients back to human care.

BMC Med Inform Decis Mak 2019 03 18;19(1):47. Epub 2019 Mar 18.

Department of Interactive Intelligence, Delft University of Technology, van Mourik Broekmanweg 6, 2628 XE, Delft, The Netherlands.

Background: Digital health interventions can fill gaps in mental healthcare provision. However, autonomous e-mental health (AEMH) systems also present challenges for effective risk management. To balance autonomy and safety, AEMH systems need to detect risk situations and act on these appropriately. Read More

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http://dx.doi.org/10.1186/s12911-019-0796-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421702PMC
March 2019
1 Read

QLMDR: a GraphQL query language for ISO 11179-based metadata repositories.

BMC Med Inform Decis Mak 2019 03 18;19(1):45. Epub 2019 Mar 18.

Federated Information Systems, German Cancer Research Center, Heidelberg, Germany.

Background: Heterogeneous healthcare instance data can hardly be integrated without harmonizing its schema-level metadata. Many medical research projects and organizations use metadata repositories to edit, store and reuse data elements. However, existing metadata repositories differ regarding software implementation and have shortcomings when it comes to exchanging metadata. Read More

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http://dx.doi.org/10.1186/s12911-019-0794-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421684PMC

The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures.

BMC Med Inform Decis Mak 2019 03 14;19(1):44. Epub 2019 Mar 14.

Clinical Informatics, Evolent Health, Arlington, USA.

Background: Clinical data synthesis aims at generating realistic data for healthcare research, system implementation and training. It protects patient confidentiality, deepens our understanding of the complexity in healthcare, and is a promising tool for situations where real world data is difficult to obtain or unnecessary. However, its validity has not been fully examined, and no previous study has validated it from the perspective of healthcare quality, a critical aspect of a healthcare system. Read More

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http://dx.doi.org/10.1186/s12911-019-0793-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416981PMC
March 2019
2 Reads

Automatically identifying social isolation from clinical narratives for patients with prostate Cancer.

BMC Med Inform Decis Mak 2019 03 14;19(1):43. Epub 2019 Mar 14.

Holling Cancer Center and Department of Psychiatry and Behavioral Sciences at Medical University of South Carolina, Charleston, South Carolina, USA.

Background: Social isolation is an important social determinant that impacts health outcomes and mortality among patients. The National Academy of Medicine recently recommended that social isolation be documented in electronic health records (EHR). However, social isolation usually is not recorded or obtained as coded data but rather collected from patient self-report or documented in clinical narratives. Read More

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http://dx.doi.org/10.1186/s12911-019-0795-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416852PMC
March 2019
1 Read

Predicting hospital-acquired pneumonia among schizophrenic patients: a machine learning approach.

BMC Med Inform Decis Mak 2019 03 13;19(1):42. Epub 2019 Mar 13.

Department of Healthcare Administration, I-Shou University, No.8, Yida Rd., Yanchao District, Kaohsiung City, 82445, Taiwan, ROC.

Background: Medications are frequently used for treating schizophrenia, however, anti-psychotic drug use is known to lead to cases of pneumonia. The purpose of our study is to build a model for predicting hospital-acquired pneumonia among schizophrenic patients by adopting machine learning techniques.

Methods: Data related to a total of 185 schizophrenic in-patients at a Taiwanese district mental hospital diagnosed with pneumonia between 2013 ~ 2018 were gathered. Read More

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https://bmcmedinformdecismak.biomedcentral.com/articles/10.1
Publisher Site
http://dx.doi.org/10.1186/s12911-019-0792-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417112PMC
March 2019
8 Reads

Accurate and rapid screening model for potential diabetes mellitus.

BMC Med Inform Decis Mak 2019 03 12;19(1):41. Epub 2019 Mar 12.

Department of radiology, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.

Background: Prediction or early diagnosis of diabetes is crucial for populations with high risk of diabetes.

Methods: In this study, we assessed the ability of five popular classifiers (J48, AdaboostM1, SMO, Bayes Net, and Naïve Bayes) to identify individuals with diabetes based on nine non-invasive and easily obtained clinical features, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work stress, and salty food preference. A total of 4205 data entries were obtained from annual physical examination reports for adults in the Shengjing Hospital of China Medical University during January-April 2017. Read More

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http://dx.doi.org/10.1186/s12911-019-0790-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416888PMC

Evaluating a South African mobile application for healthcare professionals to improve diagnosis and notification of pesticide poisonings.

BMC Med Inform Decis Mak 2019 03 11;19(1):40. Epub 2019 Mar 11.

Environmental Health Division & Centre for Environmental and Occupational Health Research (CEOHR), School of Public Health and Family Medicine, University of Cape Town; Faculty of Health Sciences, Falmouth Building, Anzio Road, Observatory, Cape Town, 7925, South Africa.

Background: Mobile health is a fast-developing field. The use of mobile health applications by healthcare professionals (HCPs) globally has increased considerably. While several studies in high income countries have investigated the use of mobile applications by HCPs in clinical practice, few have been conducted in low- and middle-income countries. Read More

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http://dx.doi.org/10.1186/s12911-019-0791-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413459PMC

Recursive neural networks in hospital bed occupancy forecasting.

BMC Med Inform Decis Mak 2019 03 7;19(1):39. Epub 2019 Mar 7.

Department of Medical Informatics, Uniklinik RWTH Aachen, Pauwelsstrasse 30, 52057, Aachen, Germany.

Background: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning.

Methods: We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. Read More

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http://dx.doi.org/10.1186/s12911-019-0776-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407266PMC

Using decision fusion methods to improve outbreak detection in disease surveillance.

BMC Med Inform Decis Mak 2019 03 5;19(1):38. Epub 2019 Mar 5.

French Armed Forces Center for Epidemiology and Public Health (CESPA), SSA, Camp de Sainte Marthe, 13568, Marseille, France.

Background: When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors.

Methods: This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. Read More

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http://dx.doi.org/10.1186/s12911-019-0774-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402142PMC
March 2019
1 Read

Construction and application of service quality evaluation system in the preclinical research on cardiovascular implant devices.

BMC Med Inform Decis Mak 2019 02 28;19(1):37. Epub 2019 Feb 28.

Animal Experimental Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, State Key Laboratory of Cardiovascular Disease & Center for cardiovascular experimental study and evaluation, National Center for Cardiovascular Diseases, Beijing Key Laboratory of Pre-clinical Research and Evaluation for Cardiovascular Implant Materials, Beijing, 100037, China.

Background: Services for the preclinical development and evaluation of cardiovascular implant devices (CVIDs) is a new industry. However, there is still no indicator system for quality evaluation. Our aim is to construct a service for quality evaluation system for the preclinical research and development of CVIDs based on Fuzzy Analytical Hierarchy Process (FAHP). Read More

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http://dx.doi.org/10.1186/s12911-019-0773-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396521PMC
February 2019
1 Read
1.496 Impact Factor