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  1. The existing community-wide bodies of biomedical ontologies are known to contain quality and content problems. Past research has revealed various errors related to their semantics and logical structure. Automa...

    Authors: Muhammad Amith, Frank Manion, Chen Liang, Marcelline Harris, Dennis Wang, Yongqun He and Cui Tao
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 4):152

    This article is part of a Supplement: Volume 19 Supplement 4

  2. Padua linear model is widely used for the risk assessment of venous thromboembolism (VTE), a common but preventable complication for inpatients. However, genetic and environmental differences between Western a...

    Authors: Yuqing Yang, Xin Wang, Yu Huang, Ning Chen, Juhong Shi and Ting Chen
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 4):151

    This article is part of a Supplement: Volume 19 Supplement 4

  3. Dietary supplements (DSs) are widely used. However, consumers know little about the safety and efficacy of DSs. There is a growing interest in accessing health information online; however, health information, ...

    Authors: Xing He, Rui Zhang, Rubina Rizvi, Jake Vasilakes, Xi Yang, Yi Guo, Zhe He, Mattia Prosperi, Jinhai Huo, Jordan Alpert and Jiang Bian
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 4):150

    This article is part of a Supplement: Volume 19 Supplement 4

  4. The aging population has led to an increase in cognitive impairment (CI) resulting in significant costs to patients, their families, and society. A research endeavor on a large cohort to better understand the ...

    Authors: Somaieh Goudarzvand, Jennifer St. Sauver, Michelle M. Mielke, Paul Y. Takahashi, Yugyung Lee and Sunghwan Sohn
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 4):149

    This article is part of a Supplement: Volume 19 Supplement 4

  5. Hepatitis C affects about 3 % of the world’s population. In the United States, about 3.5 million have chronic hepatitis C, and it is the leading cause of liver cancer and the most common indication for liver t...

    Authors: Jing Huang, Xinyuan Zhang, Jiayi Tong, Jingcheng Du, Rui Duan, Liu Yang, Jason H. Moore, Cui Tao and Yong Chen
    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 4):147

    This article is part of a Supplement: Volume 19 Supplement 4

  6. Imaging examinations, such as ultrasonography, magnetic resonance imaging and computed tomography scans, play key roles in healthcare settings. To assess and improve the quality of imaging diagnosis, we need t...

    Authors: Tao Zheng, Yimei Gao, Fei Wang, Chenhao Fan, Xingzhi Fu, Mei Li, Ya Zhang, Shaodian Zhang and Handong Ma
    Citation: BMC Medical Informatics and Decision Making 2019 19:156
  7. The potential for smartphones to revolutionize the way that medical doctors practice has become a reality, particularly in specialities where visual examination is a principal step in assessing a medical case,...

    Authors: Ali Jasem Buabbas, Prem Sharma, Adel Al-Abdulrazaq and Hashem Shehab
    Citation: BMC Medical Informatics and Decision Making 2019 19:155
  8. Female BRCA1 and BRCA2 mutation carriers have an increased lifetime risk of developing breast and/or ovarian cancer. Hence, they face the difficult decision of choosing a preventive strategy such as risk-reducing...

    Authors: Lisa Krassuski, Vera Vennedey, Stephanie Stock and Sibylle Kautz-Freimuth
    Citation: BMC Medical Informatics and Decision Making 2019 19:154
  9. Following publication of the original article

    Authors: Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier and Sheraz Ahmed
    Citation: BMC Medical Informatics and Decision Making 2019 19:153

    The original article was published in BMC Medical Informatics and Decision Making 2019 19:136

  10. Although complex machine learning models are commonly outperforming the traditional simple interpretable models, clinicians find it hard to understand and trust these complex models due to the lack of intuitio...

    Authors: Radwa Elshawi, Mouaz H. Al-Mallah and Sherif Sakr
    Citation: BMC Medical Informatics and Decision Making 2019 19:146
  11. A user-centered design approach for eHealth interventions improves their effectiveness in stroke rehabilitation. Nevertheless, insight into requirements of end-users (patients/informal caregivers and/or health...

    Authors: Manon Wentink, L. van Bodegom-Vos, B. Brouns, H. Arwert, S. Houdijk, P. Kewalbansing, L. Boyce, T. Vliet Vlieland, A. de Kloet and J. Meesters
    Citation: BMC Medical Informatics and Decision Making 2019 19:145
  12. After publication of this supplement article [1], it was brought to our attention that the first and correspondence authors’ affiliation information was incorrectly spelt. The original spelling was written as ...

    Authors: Xia Zhao, Xiaohua Li, Wei Yang, Qianjin Feng, Yi Zhou and Qiong Wang
    Citation: BMC Medical Informatics and Decision Making 2019 19:144

    The original article was published in BMC Medical Informatics and Decision Making 2018 18:112

  13. Approximately 20% of deaths in the US each year are attributable to smoking, yet current practices in the recording of this health risk in electronic health records (EHRs) have not led to discernable changes i...

    Authors: Ellen L. Palmer, John Higgins, Saeed Hassanpour, James Sargent, Christina M. Robinson, Jennifer A. Doherty and Tracy Onega
    Citation: BMC Medical Informatics and Decision Making 2019 19:143

    The Research article to this article has been published in BMC Medical Informatics and Decision Making 2019 19:141

  14. Usage of structured fields in Electronic Health Records (EHRs) to ascertain smoking history is important but fails in capturing the nuances of smoking behaviors. Knowledge of smoking behaviors, such as pack ye...

    Authors: Ellen L. Palmer, Saeed Hassanpour, John Higgins, Jennifer A. Doherty and Tracy Onega
    Citation: BMC Medical Informatics and Decision Making 2019 19:141

    The Research article to this article has been published in BMC Medical Informatics and Decision Making 2019 19:143

  15. Identifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods ...

    Authors: Hossein Estiri, Jeffrey G. Klann and Shawn N. Murphy
    Citation: BMC Medical Informatics and Decision Making 2019 19:142
  16. The challenges faced by caregivers of the elderly with chronic diseases are always complex. In this context, mobile technologies have been used with promising results, but often have restricted functionality, ...

    Authors: Matheus Costa Stutzel, Michel Pedro Filippo, Alexandre Sztajnberg, Rosa Maria E.M. da Costa, André da Silva Brites, Luciana Branco da Motta and Célia Pereira Caldas
    Citation: BMC Medical Informatics and Decision Making 2019 19:140
  17. Despite WHO guidelines for testing all suspected cases of malaria before initiating treatment, presumptive malaria treatment remains common practice among some clinicians and in certain low-resource settings t...

    Authors: S. Smith, R. Koech, D. Nzorubara, M. Otieno, L. Wong, G. Bhat, E. van den Bogaart, M. Thuranira, D. Onchonga and T. F. Rinke de Wit
    Citation: BMC Medical Informatics and Decision Making 2019 19:139
  18. Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably duri...

    Authors: Brian W. Patterson, Gwen C. Jacobsohn, Manish N. Shah, Yiqiang Song, Apoorva Maru, Arjun K. Venkatesh, Monica Zhong, Katherine Taylor, Azita G. Hamedani and Eneida A. Mendonça
    Citation: BMC Medical Informatics and Decision Making 2019 19:138
  19. With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These methods conti...

    Authors: Muhammad Naseer Bajwa, Muhammad Imran Malik, Shoaib Ahmed Siddiqui, Andreas Dengel, Faisal Shafait, Wolfgang Neumeier and Sheraz Ahmed
    Citation: BMC Medical Informatics and Decision Making 2019 19:136

    The Correction to this article has been published in BMC Medical Informatics and Decision Making 2019 19:153

  20. Medication therapy management (MTM) is a service, most commonly provided by pharmacists, intended to identify and resolve medication therapy problems (MTPs) to enhance patient care. MTM is typically documented...

    Authors: Margie E. Snyder, Heather Jaynes, Stephanie A. Gernant, Julie DiIulio, Laura G. Militello, William R. Doucette, Omolola A. Adeoye and Alissa L. Russ
    Citation: BMC Medical Informatics and Decision Making 2019 19:135
  21. A large provider of community health services (an NHS Trust in England) deployed Apple iPads to its front-line community-based healthcare clinicians (predominantly nurses) to enable them to increase responsive...

    Authors: Jasmine Harvey and John Powell
    Citation: BMC Medical Informatics and Decision Making 2019 19:134
  22. A 2016 study standardized the definition of stillbirths. It estimated the rate as a proportion of total births. A 2015 paper addressed the problem of disability-adjusted life-years (DALY) for stillbirths. Ther...

    Authors: Chander Kant
    Citation: BMC Medical Informatics and Decision Making 2019 19:133
  23. This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clini...

    Authors: Wangjin Lee and Jinwook Choi
    Citation: BMC Medical Informatics and Decision Making 2019 19:132
  24. High utilizers receive great attention in health care research because they have a largely disproportionate spending. Existing analyses usually identify high utilizers with an empirical threshold on the number...

    Authors: Chengliang Yang, Chris Delcher, Elizabeth Shenkman and Sanjay Ranka
    Citation: BMC Medical Informatics and Decision Making 2019 19:131
  25. Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach makin...

    Authors: Anshu Ankolekar, Ben G. L. Vanneste, Esther Bloemen-van Gurp, Joep G. van Roermund, Evert J. van Limbergen, Kees van de Beek, Tom Marcelissen, Victor Zambon, Matthias Oelke, Andre Dekker, Cheryl Roumen, Philippe Lambin, Adriana Berlanga and Rianne Fijten
    Citation: BMC Medical Informatics and Decision Making 2019 19:130
  26. Electronic patient portals are increasingly being implemented, also in (haemato) oncology. However, portal usage is low and depends on user and provider engagement. We explored wishes, expectations and thought...

    Authors: Paul A. F. Geerts, Trudy van der Weijden, Pien G. M. Loeffen, Lise E. F. Janssen, Celine Almekinders, Tobias A. Wienhold and Gerard M. J. Bos
    Citation: BMC Medical Informatics and Decision Making 2019 19:129
  27. Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approa...

    Authors: Yijun Shao, Qing T. Zeng, Kathryn K. Chen, Andrew Shutes-David, Stephen M. Thielke and Debby W. Tsuang
    Citation: BMC Medical Informatics and Decision Making 2019 19:128
  28. A verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person’s death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated ...

    Authors: Serena Jeblee, Mireille Gomes, Prabhat Jha, Frank Rudzicz and Graeme Hirst
    Citation: BMC Medical Informatics and Decision Making 2019 19:127
  29. 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

  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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

  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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

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