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 2 of 44
Laboratory indicator test results in electronic health records have been applied to many clinical big data analysis. However, it is quite common that the same laboratory examination item (i.e., lab indicator) ...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 14):331
Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 10):311
Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on si...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 14):306
Ontologies house various kinds of domain knowledge in formal structures, primarily in the form of concepts and the associative relationships between them. Ontologies have become integral components of many hea...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 10):305
It is significant to model clinical activities for process mining, which assists in improving medical service quality. However, current process mining studies in healthcare pay more attention to the control fl...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 14):303
Increased chloride in the context of intravenous fluid chloride load and serum chloride levels (hyperchloremia) have previously been associated with increased morbidity and mortality in select subpopulations o...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 14):302
The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 14):298
The increasing adoption of ontologies in biomedical research and the growing number of ontologies available have made it necessary to assure the quality of these resources. Most of the well-established ontolog...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 10):284
The National Cancer Institute (NCI) Thesaurus provides reference terminology for NCI and other systems. Previously, we proposed a hybrid prototype utilizing lexical features and role definitions of concepts in...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 10):273
While enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started ...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 10):272
Different from adult clinical stage I (CS1) testicular cancer, surveillance has been recommended for CS1 pediatric testicular cancer. However, among high-risk children, more than 50% suffer a relapse and progr...
Citation: BMC Medical Informatics and Decision Making 2020 20:337
The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).
Citation: BMC Medical Informatics and Decision Making 2020 20:336
Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the h...
Citation: BMC Medical Informatics and Decision Making 2020 20:335
Hormone therapy is one option for some types of prostate cancer. Shared decision making (SDM) is important in the decision making process, but SDM between prostate cancer patients receiving hormone therapy and...
Citation: BMC Medical Informatics and Decision Making 2020 20:334
In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semant...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):315
Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new know...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):314
To reduce cancer mortality and improve cancer outcomes, it is critical to understand the various cancer risk factors (RFs) across different domains (e.g., genetic, environmental, and behavioral risk factors) a...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):292
Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relat...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):283
Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we ...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):259
Emotions after surviving cancer can be complicated. The survivors may have gained new strength to continue life, but some of them may begin to deal with complicated feelings and emotional stress due to trauma ...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):254
Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relation...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):191
Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patient...
Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 4):139
Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies. However, different adjustment strategies may affect the estimation of odds rati...
Citation: BMC Medical Informatics and Decision Making 2020 20:333
Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization ...
Citation: BMC Medical Informatics and Decision Making 2020 20:332
Healthcare is a rapidly expanding area of application for Artificial Intelligence (AI). Although there is considerable excitement about its potential, there are also substantial concerns about the negative imp...
Citation: BMC Medical Informatics and Decision Making 2020 20:325
Home telemonitoring is a promising approach to optimizing outcomes for patients with Type 2 Diabetes; however, this care strategy has not been adapted for use with understudied and underserved Hispanic/Latinos...
Citation: BMC Medical Informatics and Decision Making 2020 20:324
This is the first study on prognostication in an entire cohort of laboratory-confirmed COVID-19 patients in the city of Hong Kong. Prognostic tool is essential in the contingency response for the next wave of ...
Citation: BMC Medical Informatics and Decision Making 2020 20:323
The aim of the study was to address the working population with an occupational stress prevention program using mHealth solution and encourage them for healthy lifestyle choices.
Citation: BMC Medical Informatics and Decision Making 2020 20:321
The impact of adjuvant chemotherapy or radiation therapy on the survival of patients with synovial sarcoma (SS), which is a rare soft-tissue sarcoma, remains controversial. Bayesian statistical approaches and ...
Citation: BMC Medical Informatics and Decision Making 2020 20:320
Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is importan...
Citation: BMC Medical Informatics and Decision Making 2020 20:319
Evidence-based practice, decision aids, patient preferences and autonomy preferences (AP) play an important role in making decisions with the patient. They are crucial in the process of a shared decision makin...
Citation: BMC Medical Informatics and Decision Making 2020 20:318
Management of health data and its use for informed-decision making is a challenging health sector aspect in developing countries. Monitoring and evaluation of health interventions for meeting health-related Su...
Citation: BMC Medical Informatics and Decision Making 2020 20:316
Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in ce...
Citation: BMC Medical Informatics and Decision Making 2020 20:310
The COVID-19 pandemic is a global public health emergency and experts emphasize the need for rapid and a high degree of communication and interaction between all parties, in order for critical research to be i...
Citation: BMC Medical Informatics and Decision Making 2020 20:309
Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance o...
Citation: BMC Medical Informatics and Decision Making 2020 20:308
Exclusive breastfeeding for the first 6 months of life is the optimal way to feed infants. However, recent studies suggest that exclusive breastfeeding rates in China remain low and are well below the recommen...
Citation: BMC Medical Informatics and Decision Making 2020 20:300
Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which ...
Citation: BMC Medical Informatics and Decision Making 2020 20:299
Evidence-based information available at the point of care improves patient care outcomes. Online knowledge bases can increase the application of evidence-based medicine and influence patient outcome data which...
Citation: BMC Medical Informatics and Decision Making 2020 20:294
The District Health Information Software-2 (DHIS2) is widely used by countries for national-level aggregate reporting of health-data. To best leverage DHIS2 data for decision-making, countries need to ensure t...
Citation: BMC Medical Informatics and Decision Making 2020 20:293
Akkermansia muciniphila is an anaerobic bacterium residing in the healthy intestinal tract of host and its quantity has a negative correlation with various host diseases. This study for the first time provides a ...
Citation: BMC Medical Informatics and Decision Making 2020 20:291
Given an increased global prevalence of complementary and alternative medicine (CAM) use, healthcare providers commonly seek CAM-related health information online. Numerous online resources containing CAM-spec...
Citation: BMC Medical Informatics and Decision Making 2020 20:290
Record linkage is the process of identifying and combining records about the same individual from two or more different datasets. While there are many open source and commercial data linkage tools, the volume ...
Citation: BMC Medical Informatics and Decision Making 2020 20:289
The use of statins for primary prevention of cardiovascular diseases is associated with different benefit and harm outcomes. The aime of this study is how important these outcomes are for people and what peopl...
Citation: BMC Medical Informatics and Decision Making 2020 20:288
Acute kidney injury (AKI) is common in hospitalized patients and is associated with poor patient outcomes and high costs of care. The implementation of clinical decision support tools within electronic medical...
Citation: BMC Medical Informatics and Decision Making 2020 20:287
In Australia, health services are seeking innovative ways to utilize data stored in health information systems to report on, and improve, health care quality and health system performance for Aboriginal Austra...
Citation: BMC Medical Informatics and Decision Making 2020 20:286
A proposed computer aided detection (CAD) scheme faces major issues during subtle nodule recognition. However, radiologists have not noticed subtle nodules in beginning stage of lung cancer while a proposed CA...
Citation: BMC Medical Informatics and Decision Making 2020 20:282
Mobile health (mHealth) has good potential for promoting self-care in patients suffering from chronic diseases. The patients' positive attitude toward this technology is a key factor for the successful impleme...
Citation: BMC Medical Informatics and Decision Making 2020 20:281
The broad adoption of electronic health records (EHRs) provides great opportunities to conduct health care research and solve various clinical problems in medicine. With recent advances and success, methods ba...
Citation: BMC Medical Informatics and Decision Making 2020 20:280
Current systematic reviews of randomized controlled trials suggest positive influences of mobile app-based health promotion programs on dietary and physical activity behaviors. However, the actual adoption of ...
Citation: BMC Medical Informatics and Decision Making 2020 20:279
Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment c...
Citation: BMC Medical Informatics and Decision Making 2020 20:276
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