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  1. Following publication of the original article [1], the authors reported an error in one of the authors’ names. In this Correction the incorrect and correct author name are shown. The original publication of th...

    Authors: Joseph K. Nuamah, Farzan Sasangohar, Madhav Erraguntla and Ranjana K. Mehta
    Citation: BMC Medical Informatics and Decision Making 2019 19:126

    The original article was published in BMC Medical Informatics and Decision Making 2019 19:113

  2. Coordination of care, especially after a patient experiences an acute care event, is a challenge for many health systems. Event notification is a form of health information exchange (HIE) which has the potenti...

    Authors: Brian E. Dixon, Ashley L. Schwartzkopf, Vivian M. Guerrero, Justine May, Nicholas S. Koufacos, Andrew M. Bean, Joan D. Penrod, Cathy C. Schubert and Kenneth S. Boockvar
    Citation: BMC Medical Informatics and Decision Making 2019 19:125
  3. Decisional conflict is associated with decision quality and may affect decision outcomes. In the health sciences literature, the Decisional Conflict Scale is widely used to measure decisional conflict, yet lim...

    Authors: Rachel A. Pozzar, Donna L. Berry and Fangxin Hong
    Citation: BMC Medical Informatics and Decision Making 2019 19:124
  4. The autoverification system for coagulation consists of a series of rules that allow normal data to be released without manual verification. With new advances in medical informatics, the laboratory information...

    Authors: Zhongqing Wang, Cheng Peng, Hui Kang, Xia Fan, Runqing Mu, Liping Zhou, Miao He and Bo Qu
    Citation: BMC Medical Informatics and Decision Making 2019 19:123
  5. According to WHO stroke is a growing societal challenge and the third leading cause of global disease-burden estimated using disability-adjusted life years. Rehabilitation after stroke is an area of mutual int...

    Authors: Uno Fors, Julius T. Kamwesiga, Gunilla M. Eriksson, Lena von Koch and Susanne Guidetti
    Citation: BMC Medical Informatics and Decision Making 2019 19:122
  6. Many healthcare databases have been routinely collected over the past decades, to support clinical practice and administrative services. However, their secondary use for research is often hindered by restricte...

    Authors: João Rafael Almeida, Rosa Gini, Giuseppe Roberto, Peter Rijnbeek and José Luís Oliveira
    Citation: BMC Medical Informatics and Decision Making 2019 19:121
  7. Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estim...

    Authors: Saeed Al-Azazi, Alexander Singer, Rasheda Rabbani and Lisa M. Lix
    Citation: BMC Medical Informatics and Decision Making 2019 19:120
  8. The provision of medical services by Medical Teams (MT) on Online Healthcare Communities (OHCs) is a novel method employed by geographically-dispersed healthcare professionals to serve one patient simultaneous...

    Authors: Jiaying Li, Hong Wu, Zhaohua Deng, Naiji Lu, Richard Evans and Chenxi Xia
    Citation: BMC Medical Informatics and Decision Making 2019 19:119
  9. Most of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving p...

    Authors: Natalie Flaks-Manov, Maxim Topaz, Moshe Hoshen, Ran D. Balicer and Efrat Shadmi
    Citation: BMC Medical Informatics and Decision Making 2019 19:118
  10. Most healthcare data sources store information within their own unique schemas, making reliable and reproducible research challenging. Consequently, researchers have adopted various data models to improve the ...

    Authors: Mark D. Danese, Marc Halperin, Jennifer Duryea and Ryan Duryea
    Citation: BMC Medical Informatics and Decision Making 2019 19:117
  11. Poor adherence compromises medication treatment effectiveness which results in suboptimal illness control. This can lead to increased use of healthcare services, reduction in patients’ quality of life and incr...

    Authors: Bereket Senay, Kassahun Dessie Gashu, Adamu Takele Jemere and Zeleke Abebaw Mekonnen
    Citation: BMC Medical Informatics and Decision Making 2019 19:109
  12. In low-income settings, community health workers (CHWs) are frequently the first point of contact for newborns. Mobile technology may aid health workers in classifying illness and providing referral and manage...

    Authors: Lauren E. Schaeffer, Salahuddin Ahmed, Mahmoodur Rahman, Rachel Whelan, Sayedur Rahman, Arunangshu Dutta Roy, Tanzia Ahmed Nijhum, Nazmun Nahar Bably, Helen D’Couto, Carly Hudelson, Iffat Ara Jaben, Sayed Rubayet, Abdullah Baqui and Anne CC Lee
    Citation: BMC Medical Informatics and Decision Making 2019 19:116
  13. Multiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterp...

    Authors: Jacqueline M. Torres, Danielle Hessler-Jones, Carol Yarbrough, Adam Tapley, Raemarie Jimenez and Laura M. Gottlieb
    Citation: BMC Medical Informatics and Decision Making 2019 19:115
  14. In many low and middle-income countries (LMICs), difficulties in patient identification are a major obstacle to the delivery of longitudinal care. In absence of unique identifiers, biometrics have emerged as a...

    Authors: Lauren P. Etter, Elizabeth J. Ragan, Rachael Campion, David Martinez and Christopher J. Gill
    Citation: BMC Medical Informatics and Decision Making 2019 19:114
  15. A common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly ov...

    Authors: Joseph K. Nuamah, Farzan Sasangohar, Madhav Erraguntla and Ranjana K. Mehta
    Citation: BMC Medical Informatics and Decision Making 2019 19:113

    The Correction to this article has been published in BMC Medical Informatics and Decision Making 2019 19:126

  16. Data mining tools have been increasingly used in health research, with the promise of accelerating discoveries. Lift is a standard association metric in the data mining community. However, health researchers s...

    Authors: Khanh Vu, Rebecca A. Clark, Colin Bellinger, Graham Erickson, Alvaro Osornio-Vargas, Osmar R. Zaïane and Yan Yuan
    Citation: BMC Medical Informatics and Decision Making 2019 19:112
  17. Dengue is a serious problem around the globe, with 3.9 billion people at risk of the disease. Sri Lanka has recently seen unprecedented rates of dengue with 4.3 times more cases than during the same period ove...

    Authors: May O. Lwin, Anita Sheldenkar, Chitra Panchapakesan, Janelle Shaina Ng, Jerrald Lau, Karthikayen Jayasundar, Kasun Horathalge, Vajira Sampath Rathnayake, Adam W. Crawley and Prasad Wimalaratne
    Citation: BMC Medical Informatics and Decision Making 2019 19:111
  18. Health and social care interventions show promise as a way of managing the progression of frailty in older adults. Information technology could improve the availability of interventions and services for older ...

    Authors: Holly Gwyther, Lex van Velsen, Rachel L. Shaw, Barbara D’Avanzo, Maria Bujnowska-Fedak, Donata Kurpas, Katarzyna Szwamel, Jan-Willem van’t Klooster and Carol Holland
    Citation: BMC Medical Informatics and Decision Making 2019 19:110
  19. Cardiovascular risk management (CVRM) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease. Computerized decision support systems (CDSS) ena...

    Authors: T. Katrien J. Groenhof, Folkert W. Asselbergs, Rolf H. H. Groenwold, Diederick E. Grobbee, Frank L. J. Visseren and Michiel L. Bots
    Citation: BMC Medical Informatics and Decision Making 2019 19:108
  20. Evidence-Based Medicine (EBM) is the process of systematically locating, searching, evaluating, and using contemporaneous research findings as the basis for clinical decision making. The systematic review show...

    Authors: Teshager Worku, Meron Yeshitila, Tilaye Feto, Shiferaw Leta, Frehiwot Mesfin and Haymanot Mezmur
    Citation: BMC Medical Informatics and Decision Making 2019 19:107
  21. Pain is the most common and distressing symptom for patients in all clinical settings. The dearth of health informatics tools to support acute and chronic pain management may be contributing to the chronic pai...

    Authors: Peng Zhao, Illhoi Yoo, Robert Lancey and Ebby Varghese
    Citation: BMC Medical Informatics and Decision Making 2019 19:106
  22. Home monitoring of urine protein is a critical component of disease management in childhood nephrotic syndrome. We describe the development of a novel mobile application, UrApp – Nephrotic Syndrome Manager, to...

    Authors: Chia-shi Wang, Richard Boyd, Russell Mitchell, W. Darryl Wright, Courtney McCracken, Cam Escoffery, Rachel E. Patzer and Larry A. Greenbaum
    Citation: BMC Medical Informatics and Decision Making 2019 19:105
  23. Although previous research showed that telehealth services can reduce the misuse of resources and urban–rural disparities, most healthcare insurers do not include telehealth services in their health insurance ...

    Authors: Ching-Chin Chern, Yu-Jen Chen and Bo Hsiao
    Citation: BMC Medical Informatics and Decision Making 2019 19:104
  24. Disease trajectories for chronic diseases can span over several decades, with several time-dependent factors affecting treatment decisions. Thus, there is a need for long-term predictions of disease trajectori...

    Authors: Eugenio Ventimiglia, Mieke Van Hemelrijck, Lars Lindhagen, Pär Stattin and Hans Garmo
    Citation: BMC Medical Informatics and Decision Making 2019 19:103
  25. Inappropriate prescribing of psychotropics is a persistent and prevalent problem in nursing homes. The present study compared inappropriate prescribing of psychotropics in nursing homes 16 years apart with pre...

    Authors: Jan Schjøtt and Jörg Aßmus
    Citation: BMC Medical Informatics and Decision Making 2019 19:102
  26. Vaccination has been one of the most successful public health interventions to date, and the U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 500,000 reports for post-vacci...

    Authors: Jian-Jian Ren, Tingni Sun, Yongqun He and Yuji Zhang
    Citation: BMC Medical Informatics and Decision Making 2019 19:101
  27. The 2014–2016 West Africa Ebola epidemic highlighted the difficulty of collecting patient information during emergencies, especially in highly infectious environments. Health information systems (HISs) appropr...

    Authors: Shefali Oza, Kevin Wing, Alieu Amara Sesay, Sabah Boufkhed, Catherine Houlihan, Lahai Vandi, Sahr Charles Sebba, Catherine R. McGowan, Rachael Cummings and Francesco Checchi
    Citation: BMC Medical Informatics and Decision Making 2019 19:100
  28. Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As ...

    Authors: Jui-Tse Hsu, Yung-Wei Chen, Te-Wei Ho, Hao-Chih Tai, Jin-Ming Wu, Hsin-Yun Sun, Chi-Sheng Hung, Yi-Chong Zeng, Sy-Yen Kuo and Feipei Lai
    Citation: BMC Medical Informatics and Decision Making 2019 19:99
  29. Multiple predictive scores using Electronic Patient Record data have been developed for hospitalised patients at risk of clinical deterioration. Methods used to select patient centred variables for inclusion i...

    Authors: James Malycha, Timothy Bonnici, David A. Clifton, Guy Ludbrook, J. Duncan Young and Peter J. Watkinson
    Citation: BMC Medical Informatics and Decision Making 2019 19:98
  30. Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historic...

    Authors: Shaker El-Sappagh, Farman Ali, Abdeltawab Hendawi, Jun-Hyeog Jang and Kyung-Sup Kwak
    Citation: BMC Medical Informatics and Decision Making 2019 19:97
  31. Assessing risks of bias in randomized controlled trials (RCTs) is an important but laborious task when conducting systematic reviews. RobotReviewer (RR), an open-source machine learning (ML) system, semi-autom...

    Authors: Frank Soboczenski, Thomas A. Trikalinos, Joël Kuiper, Randolph G. Bias, Byron C. Wallace and Iain J. Marshall
    Citation: BMC Medical Informatics and Decision Making 2019 19:96
  32. Patients and citizens need access to their health information to get a retrospective as well as a prospective view on their care and rehabilitation processes. However, patients’ health information is stored in...

    Authors: Nadia Davoody, Sabine Koch, Ingvar Krakau and Maria Hägglund
    Citation: BMC Medical Informatics and Decision Making 2019 19:95
  33. Although fecal hemoglobin concentration (f-Hb) was highly associated with the risk of colorectal neoplasms, current studies on this subject are hampered by skewedness of the data and the ordinal property of f-...

    Authors: Szu-Min Peng, Han-Mo Chiu, Hsiao-Hsuan Jen, Chen-Yang Hsu, Sam Li-Sheng Chen, Sherry Yueh-Hsia Chiu, Amy Ming-Fang Yen, Jean Ching-Yuan Fann, Yi-Chia Lee and Hsiu-Hsi Chen
    Citation: BMC Medical Informatics and Decision Making 2019 19:94
  34. While early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will rem...

    Authors: Lars Müller, Rashmi Gangadharaiah, Simone C. Klein, James Perry, Greg Bernstein, David Nurkse, Dustin Wailes, Rishi Graham, Robert El-Kareh, Sanjay Mehta, Staal A. Vinterbo and Eliah Aronoff-Spencer
    Citation: BMC Medical Informatics and Decision Making 2019 19:93
  35. Maintaining physical fitness is a crucial component of the therapeutic process for patients with cardiovascular disease (CVD). Despite the known importance of being physically active, patient adherence to exer...

    Authors: Kristina Livitckaia, Vassilis Koutkias, Evangelia Kouidi, Mark van Gils, Nikolaos Maglaveras and Ioanna Chouvarda
    Citation: BMC Medical Informatics and Decision Making 2019 19:92
  36. Many clinical concepts are standardized under a categorical and hierarchical taxonomy such as ICD-10, ATC, etc. These taxonomic clinical concepts provide insight into semantic meaning and similarity among clin...

    Authors: Zheng Jia, Xudong Lu, Huilong Duan and Haomin Li
    Citation: BMC Medical Informatics and Decision Making 2019 19:91
  37. Data-intensive research in medicine and healthcare such as health-related big data research (HBDR) implies that data from clinical routine, research and patient-reported data, but also non-medical social or de...

    Authors: Katharina Beier, Mark Schweda and Silke Schicktanz
    Citation: BMC Medical Informatics and Decision Making 2019 19:90
  38. Following publication of the original article [1], the authors reported an error in one of the authors’ names.

    Authors: Vivienne J. Zhu, Leslie A. Lenert, Brian E. Bunnell, Jihad S. Obeid, Melanie Jefferson and Chanita Hughes Halbert
    Citation: BMC Medical Informatics and Decision Making 2019 19:89

    The original article was published in BMC Medical Informatics and Decision Making 2019 19:43

  39. The gateway hypothesis (and particularly the prediction of developmental stages in drug abuse) has been a subject of protracted debate since the 1970s. Extensive research has gone into this subject, but has yi...

    Authors: Phillip C. S. R. Kilgore, Nadejda Korneeva, Thomas C. Arnold, Marjan Trutschl and Urška Cvek
    Citation: BMC Medical Informatics and Decision Making 2019 19:87
  40. 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 s...

    Authors: Maria Pikoula, Jennifer Kathleen Quint, Francis Nissen, Harry Hemingway, Liam Smeeth and Spiros Denaxas
    Citation: BMC Medical Informatics and Decision Making 2019 19:86
  41. 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 ...

    Authors: Arthur G. Money, Anita Atwal, Emily Boyce, Sophie Gaber, Susan Windeatt and Kyriakos Alexandrou
    Citation: BMC Medical Informatics and Decision Making 2019 19:85
  42. 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...

    Authors: Laurie Pilote, Luc Côté, Selma Chipenda Dansokho, Émilie Brouillard, Anik M. C. Giguère, France Légaré, Roland Grad and Holly O. Witteman
    Citation: BMC Medical Informatics and Decision Making 2019 19:84
  43. 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 n...

    Authors: Xuemeng Li, Jianfei Pang, Mei Li and Dongsheng Zhao
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):67

    This article is part of a Supplement: Volume 19 Supplement 2

  44. 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) t...

    Authors: Ying Xiong, Zhongmin Wang, Dehuan Jiang, Xiaolong Wang, Qingcai Chen, Hua Xu, Jun Yan and Buzhou Tang
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):66

    This article is part of a Supplement: Volume 19 Supplement 2

  45. 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 a...

    Authors: Xiaoling Cai, Shoubin Dong and Jinlong Hu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):65

    This article is part of a Supplement: Volume 19 Supplement 2

  46. 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 sha...

    Authors: Suwen Liu, Yifan Shao, Longhua Qian and Guodong Zhou
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):63

    This article is part of a Supplement: Volume 19 Supplement 2

  47. 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...

    Authors: Zhichang Zhang, Tong Zhou, Yu Zhang and Yali Pang
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):55

    This article is part of a Supplement: Volume 19 Supplement 2

  48. 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 trea...

    Authors: Xuesi Zhou, Haoqi Xiong, Sihan Zeng, Xiangling Fu and Ji Wu
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):54

    This article is part of a Supplement: Volume 19 Supplement 2

  49. 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 tha...

    Authors: Hai Long, Yan Zhu, Lirong Jia, Bo Gao, Jing Liu, Lihong Liu and Heinrich Herre
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):53

    This article is part of a Supplement: Volume 19 Supplement 2

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