Citation Impact
2.317 - 2-year Impact Factor
2.745 - 5-year Impact Factor
1.675 - Source Normalized Impact per Paper (SNIP)
0.908 - SCImago Journal Rank (SJR)
Usage
980,778 Downloads
1305 Altmetric mentions
Page 6 of 45
Shared decision-making improves the quality of patient care. Unfortunately, shared decision-making is not yet common practice among vascular surgeons. Thus, decision support tools were developed to assist vasc...
Citation: BMC Medical Informatics and Decision Making 2020 20:172
Several studies highlight the effects of artificial intelligence (AI) systems on healthcare delivery. AI-based tools may improve prognosis, diagnostics, and care planning. It is believed that AI will be an int...
Citation: BMC Medical Informatics and Decision Making 2020 20:170
Parent-clinician shared decision making is the recommended model for the care of premature infants; thus, clinicians provide prenatal prematurity counseling to parents in the event of a mother’s hospitalizatio...
Citation: BMC Medical Informatics and Decision Making 2020 20:169
Radiation therapy requires precision to target and escalate the doses to affected regions while reducing the adjacent normal tissue exposed to high radiotherapy doses. Image guidance has become the start of th...
Citation: BMC Medical Informatics and Decision Making 2020 20:168
“Artificial intelligence” (AI) is often referred to as “augmented human intelligence” (AHI). The latter term implies that computers support—rather than replace—human decision-making. It is unclear whether the ...
Citation: BMC Medical Informatics and Decision Making 2020 20:167
Building and validating electronic algorithms to identify patients with specific disease profiles using health data is becoming increasingly important to disease surveillance and population health management. The...
Citation: BMC Medical Informatics and Decision Making 2020 20:166
Surgical resection of pheochromocytoma may lead to high risk factors for intraoperative hemodynamic instability (IHD), which can be life-threatening. This study aimed to investigate the risk factors that could...
Citation: BMC Medical Informatics and Decision Making 2020 20:165
Worldwide the rate of unplanned pregnancies is more than 40%. Identifying women at risk of pregnancy can help prevent negative outcomes and also reduce healthcare costs of potential complications. It can also ...
Citation: BMC Medical Informatics and Decision Making 2020 20:164
Unequivocal identification of patients is a precondition for a safe medical journey through different information systems (ISs) and software applications that are communicating and exchanging interoperable dat...
Citation: BMC Medical Informatics and Decision Making 2020 20:163
One of the most challenging tasks for bladder cancer diagnosis is to histologically differentiate two early stages, non-invasive Ta and superficially invasive T1, the latter of which is associated with a signi...
Citation: BMC Medical Informatics and Decision Making 2020 20:162
Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9–524), we have devel...
Citation: BMC Medical Informatics and Decision Making 2020 20:161
The healthcare sector is an interesting target for fraudsters. The availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques, making the auditing...
Citation: BMC Medical Informatics and Decision Making 2020 20:160
The electronic patient record (EPR) has been introduced into nursing homes in order to facilitate documentation practices such as assessment and care planning, which play an integral role in the provision of d...
Citation: BMC Medical Informatics and Decision Making 2020 20:159
Particularly in the context of severe diseases like cancer, many patients wish to include caregivers in the planning of treatment and care. Many caregivers like to be involved but feel insufficiently enabled. ...
Citation: BMC Medical Informatics and Decision Making 2020 20:158
The promises of improved health care and health research through data-intensive applications rely on a growing amount of health data. At the core of large-scale data integration efforts, clinical data warehous...
Citation: BMC Medical Informatics and Decision Making 2020 20:157
Despite the numerous healthcare smartphone applications for self-management of diabetes, patients often fail to use these applications consistently due to various limitations, including difficulty in inputting...
Citation: BMC Medical Informatics and Decision Making 2020 20:156
Evidence-based Clinical Decision Support Systems (CDSSs) usually obtain clinical evidences from randomized controlled trials based on coarse-grained groups. Individuals who are beyond the scope of the original...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):138
Circular RNAs (circRNAs) are those RNA molecules that lack the poly (A) tails, which present the closed-loop structure. Recent studies emphasized that some circRNAs imply different functions from canonical tra...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):137
Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a pro...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):136
Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing bi...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):135
It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and bro...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):133
With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important out...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):132
The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by th...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):131
The social Q&A community quickly becomes a popular platform for consumers to find health information because of its convenience and interactivity.
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):130
With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):129
Nowadays, the latent power of technology, which can offer innovative resolutions to disease diagnosis, has awakened high-level anticipation in the community of patients as well as professionals. An easy-to-use...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):128
In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). However, t...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):127
Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Son...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):126
To provide satisfying answers, medical QA system has to understand the intentions of the users’ questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learnin...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):125
Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in healthcare domains. Recent years have seen a great progress of applying RL in addressing decis...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):124
Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disea...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):123
The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians’ healthcare practice, patient...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):122
Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):121
Although clinical guidelines provide the best practice for medical activities, there are some limitations in using clinical guidelines to assistant decision-making in practical application, such as long update...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):120
Deep learning based on segmentation models have been gradually applied in biomedical images and achieved state-of-the-art performance for 3D biomedical segmentation. However, most of existing biomedical segmen...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):119
A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words.
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):118
Various methods based on k-anonymity have been proposed for publishing medical data while preserving privacy. However, the k-anonymity property assumes that adversaries possess fixed background knowledge. Althoug...
Citation: BMC Medical Informatics and Decision Making 2020 20:155
The increasing complexity of current drug therapies jeopardizes patient adherence. While individual needs to simplify a medication regimen vary from patient to patient, a straightforward approach to integrate ...
Citation: BMC Medical Informatics and Decision Making 2020 20:154
Electronic personal health records (ePHRs) are defined as electronic applications through which individuals can access, manage, and share health information in a private, secure, and confidential environment. ...
Citation: BMC Medical Informatics and Decision Making 2020 20:153
For real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab test results is essential to enable successful ...
Citation: BMC Medical Informatics and Decision Making 2020 20:152
Recent studies increasingly examine social support for diabetes self-management delivered via mHealth. In contrast to previous studies examining social support as an outcome of technology use, or technology as...
Citation: BMC Medical Informatics and Decision Making 2020 20:151
Patients with diabetes are at an increased risk of readmission and mortality when discharged from hospital. Existing research identifies statistically significant risk factors that are thought to underpin thes...
Citation: BMC Medical Informatics and Decision Making 2020 20:150
Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied ...
Citation: BMC Medical Informatics and Decision Making 2020 20:149
Prostate cancer (PCa) represents a significant healthcare problem. The critical clinical question is the need for a biopsy. Accurate risk stratification of patients before a biopsy can allow for individualised...
Citation: BMC Medical Informatics and Decision Making 2020 20:148
Semantic interoperability is essential for improving data quality and sharing. The ISO/IEC 11179 Metadata Registry (MDR) standard has been highlighted as a solution for standardizing and registering clinical d...
Citation: BMC Medical Informatics and Decision Making 2020 20:147
Citation: BMC Medical Informatics and Decision Making 2020 20:146
The design and internal layout of modern operating rooms (OR) are influencing the surgical team’s collaboration and communication, ergonomics, as well as intraoperative hygiene substantially. Yet, there is no ...
Citation: BMC Medical Informatics and Decision Making 2020 20:145
Clinical intuition and nonanalytic reasoning play a major role in clinical hypothesis generation; however, clinicians’ intuition about whether a critically ill child is bacteremic has not been explored. We end...
Citation: BMC Medical Informatics and Decision Making 2020 20:144
As a kind of widely distributed disease in China, acquired immune deficiency syndrome (AIDS) has been quickly growing each year, become a serious problem and caused serious damage to the life and health of peo...
Citation: BMC Medical Informatics and Decision Making 2020 20:143
The adoption of robotic-assisted surgery (RAS) requires a clear willingness, not only from healthcare organization to operate the robotic system but also from the public that is going to perceive it. This stud...
Citation: BMC Medical Informatics and Decision Making 2020 20:140
Citation Impact
2.317 - 2-year Impact Factor
2.745 - 5-year Impact Factor
1.675 - Source Normalized Impact per Paper (SNIP)
0.908 - SCImago Journal Rank (SJR)
Usage
980,778 Downloads
1305 Altmetric mentions