BMC Medical Informatics and Decision Making
MultiSourcDSim: an integrated approach for exploring disease similarity
A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This mi...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):269This article is part of a Supplement: Volume 19 Supplement 6EEG-based image classification via a region-level stacked bi-directional deep learning framework
As a physiological signal, EEG data cannot be subjectively changed or hidden. Compared with other physiological signals, EEG signals are directly related to human cortical activities with excellent temporal re...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):268This article is part of a Supplement: Volume 19 Supplement 6Incorporating medical code descriptions for diagnosis prediction in healthcare
Diagnosis aims to predict the future health status of patients according to their historical electronic health records (EHR), which is an important yet challenging task in healthcare informatics. Existing diag...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):267This article is part of a Supplement: Volume 19 Supplement 6Statistical and spectral analysis of ECG signal towards achieving non-invasive blood glucose monitoring
Globally, the cases of diabetes mellitus (diabetes) have increased in the past three decades, and it is recorded as one of the leading cause of death. This epidemic is a metabolic condition where the body cann...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):266This article is part of a Supplement: Volume 19 Supplement 6Fast read alignment with incorporation of known genomic variants
Many genetic variants have been reported from sequencing projects due to decreasing experimental costs. Compared to the current typical paradigm, read mapping incorporating existing variants can improve the pe...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):265This article is part of a Supplement: Volume 19 Supplement 6Heterogeneous information network based clustering for precision traditional Chinese medicine
Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors o...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):264This article is part of a Supplement: Volume 19 Supplement 6Evaluating global and local sequence alignment methods for comparing patient medical records
Sequence alignment is a way of arranging sequences (e.g., DNA, RNA, protein, natural language, financial data, or medical events) to identify the relatedness between two or more sequences and regions of simila...BMC Medical Informatics and Decision Making 2019 19(Suppl 6):263This article is part of a Supplement: Volume 19 Supplement 6Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports
Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. Early and accurate detection of potential ADRs can help to improve drug safety and reduce financial costs. Post-market sponta...BMC Medical Informatics and Decision Making 2019 19:279Promoting healthy teenage behaviour across three European countries through the use of a novel smartphone technology platform, PEGASO fit for future: study protocol of a quasi-experimental, controlled, multi-Centre trial
Behaviour change interventions targeting physical activity, diet, sleep and sedentary behaviour of teenagers show promise when delivered through smartphones. However, to date there is no evidence of effectiven...BMC Medical Informatics and Decision Making 2019 19:278Representation learning for clinical time series prediction tasks in electronic health records
Electronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high ...BMC Medical Informatics and Decision Making 2019 19(Suppl 8):259This article is part of a Supplement: Volume 19 Supplement 8A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes
Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients ...BMC Medical Informatics and Decision Making 2019 19(Suppl 8):258This article is part of a Supplement: Volume 19 Supplement 8Implementation of machine learning algorithms to create diabetic patient re-admission profiles
Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is g...BMC Medical Informatics and Decision Making 2019 19(Suppl 9):253This article is part of a Supplement: Volume 19 Supplement 9Biometric handwriting analysis to support Parkinson’s Disease assessment and grading
Handwriting represents one of the major symptom in Parkinson’s Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in ...BMC Medical Informatics and Decision Making 2019 19(Suppl 9):252This article is part of a Supplement: Volume 19 Supplement 9An adaptive term proximity based rocchio’s model for clinical decision support retrieval
In order to better help doctors make decision in the clinical setting, research is necessary to connect electronic health record (EHR) with the biomedical literature. Pseudo Relevance Feedback (PRF) is a kind ...BMC Medical Informatics and Decision Making 2019 19(Suppl 9):251This article is part of a Supplement: Volume 19 Supplement 9A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several ...BMC Medical Informatics and Decision Making 2019 19(Suppl 9):244This article is part of a Supplement: Volume 19 Supplement 9A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s Disease
Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialist...BMC Medical Informatics and Decision Making 2019 19(Suppl 9):243This article is part of a Supplement: Volume 19 Supplement 9Using machine learning models to improve stroke risk level classification methods of China national stroke screening
With the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China nationa...BMC Medical Informatics and Decision Making 2019 19:261Representation learning in intraoperative vital signs for heart failure risk prediction
The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postop...BMC Medical Informatics and Decision Making 2019 19:260Improving reference prioritisation with PICO recognition
Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study...BMC Medical Informatics and Decision Making 2019 19:256Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer’s disease
Extracting useful information from biomedical literature plays an important role in the development of modern medicine. In natural language processing, there have been rigorous attempts to find meaningful rela...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):240This article is part of a Supplement: Volume 19 Supplement 5Natural language processing for populating lung cancer clinical research data
Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics a...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):239This article is part of a Supplement: Volume 19 Supplement 5Improving rare disease classification using imperfect knowledge graph
Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the ...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):238This article is part of a Supplement: Volume 19 Supplement 5TestIME: an application for evaluating the efficiency of Chinese input method engines in electronic medical record entry task
With the wide application of Electronic Medical Record (EMR) systems, it has become a daily work for doctors using keyboards to input clinical information into the EMR system. Chinese Input Method Engine (IME)...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):237This article is part of a Supplement: Volume 19 Supplement 5Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text
To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):236This article is part of a Supplement: Volume 19 Supplement 5An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records
Clinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):235This article is part of a Supplement: Volume 19 Supplement 5RCorp: a resource for chemical disease semantic extraction in Chinese
To robustly identify synergistic combinations of drugs, high-throughput screenings are desirable. It will be of great help to automatically identify the relations in the published papers with machine learning ...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):234This article is part of a Supplement: Volume 19 Supplement 5- BMC Medical Informatics and Decision Making 2019 19(Suppl 5):233This article is part of a Supplement: Volume 19 Supplement 5
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has...BMC Medical Informatics and Decision Making 2019 19(Suppl 5):232This article is part of a Supplement: Volume 19 Supplement 5Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine
The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-wo...BMC Medical Informatics and Decision Making 2019 19:255Deterrence approach on the compliance with electronic medical records privacy policy: the moderating role of computer monitoring
This study explored the possible antecedents that will motivate hospital employees’ compliance with privacy policy related to electronic medical records (EMR) from a deterrence perspective. Further, we also in...BMC Medical Informatics and Decision Making 2019 19:254Working with patients and the public to design an electronic health record interface: a qualitative mixed-methods study
Enabling patients to be active users of their own medical records may promote the delivery of safe, efficient care across settings. Patients are rarely involved in designing digital health record systems which...BMC Medical Informatics and Decision Making 2019 19:250Effectiveness of a chat-bot for the adult population to quit smoking: protocol of a pragmatic clinical trial in primary care (Dejal@)
The wide scale and severity of consequences of tobacco use, benefits derived from cessation, low rates of intervention by healthcare professionals, and new opportunities stemming from novel communications tech...BMC Medical Informatics and Decision Making 2019 19:249Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches
Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems an...BMC Medical Informatics and Decision Making 2019 19:248Standards as applied in reality: a case study on the translation of standards in eHealth evaluation practice
Application of standards is a way to increase quality in an evaluation study. However, standards are used insufficiently in eHealth evaluation, affecting the generalization of the knowledge generated.BMC Medical Informatics and Decision Making 2019 19:247Developing a standardised approach to the aggregation of inpatient episodes into person-based spells in all specialties and psychiatric specialties
Electronic health record (EHR) data are available for research in all UK nations and cross-nation comparative studies are becoming more common. All UK inpatient EHRs are based around episodes, but episode-base...BMC Medical Informatics and Decision Making 2019 19:246Effectiveness of electronic point-of-care reminders versus monthly feedback to improve adherence to 10 clinical recommendations in primary care: a cluster randomized clinical trial
Numerous studies have analyzed the effectiveness of electronic reminder interventions to improve different clinical conditions, and most have reported a small to moderate effect. Few studies, however, have ana...BMC Medical Informatics and Decision Making 2019 19:245Latent Dirichlet Allocation in predicting clinical trial terminations
This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, ...BMC Medical Informatics and Decision Making 2019 19:242Evaluating the implementation of a personal health record for chronic primary and secondary care: a mixed methods approach
Personal health records (PHRs) provide the opportunity for self-management support, enhancing communication between patients and caregivers, and maintaining and/or improving the quality of chronic disease mana...BMC Medical Informatics and Decision Making 2019 19:241“Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model”
Hospital electronic information management systems (HEIMS) are widely used in Ghana, and hence its performance must be carefully assessed. Nurses as clinical health personnel are the largest cluster of hospita...BMC Medical Informatics and Decision Making 2019 19:230A novel hybrid modeling approach for the evaluation of integrated care and economic outcome in heart failure treatment
Demographic changes, increased life expectancy and the associated rise in chronic diseases pose challenges to public health care systems. Optimized treatment methods and integrated concepts of care are potenti...BMC Medical Informatics and Decision Making 2019 19:229
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