With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
Pattern Recognition, Automated ; Medical Informatics

COVID-19 ; Humans ; Medical Informatics Applications ; Pandemics ; Pharmacists ; Pharmacy Service, Hospital/methods* ; Professional Role ; SARS-CoV-2 ; Safety Management/methods*

A clinical information navigation system based on 3D human body model is designed. The system extracts the key information of diagnosis and treatment of patients by searching the historical medical records, and stores the focus information in a predefined structured patient instance. In addition, the rule mapping is established between the patient instance and the three-dimensional human body model, the focus information is visualized on the three-dimensional human body model, and the trend curve can be drawn according to the change of the focus, meanwhile, the key diagnosis and treatment information and the original report reference function are provided. The system can support the analysis, storage and visualization of various types of reports, improve the efficiency of doctors' retrieval of patient information, and reduce the treatment time.
Diagnosis, Computer-Assisted ; Humans ; Medical Informatics Applications ; Models, Anatomic ; Software

The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics (OHDSI) initiative combines over 100 controlled vocabularies into its own. However, the OHDSI vocabulary is limited in the sense that it combines multiple terminologies and does not provide a direct way to link them outside of their own self-contained scope. This issue makes the tasks of enriching feature sets by using external resources extremely difficult. In order to address these shortcomings, we have created a linked data version of the OHDSI vocabulary, connecting it with already established linked resources like bioportal, bio2rdf, etc. with the ultimate purpose of enabling the interoperability of resources previously foreign to the OHDSI universe.
Informatics ; Medical Informatics ; Vocabulary ; Vocabulary, Controlled

OBJECTIVES: The aim of this study is to develop an intelligent diagnostic system utilizing machine learning for data cleansing, then build an intelligent model and obtain new cutoff values for APRI (aspartate aminotransferase-to-platelet ratio) and FIB-4 (fibrosis score) for the prediction and staging of fibrosis in children with chronic hepatitis C (CHC). METHODS: Random forest (RF) was utilized in this study for data cleansing; then, prediction and staging of fibrosis, APRI and FIB-4 scores and their areas under the ROC curve (AUC) have been obtained on the cleaned dataset. A cohort of 166 Egyptian children with CHC was studied. RESULTS: RF, APRI, and FIB-4 achieved high AUCs; where APRI had AUCs of 0.78, 0.816, and 0.77; FIB-4 had AUCs of 0.74, 0.828, and 0.78; and RF had AUCs of 0.903, 0.894, and 0.822, for the prediction of any type of fibrosis, advanced fibrosis, and differentiating between mild and advanced fibrosis, respectively. CONCLUSIONS: Machine learning is a valuable addition to non-invasive methods of liver fibrosis prediction and staging in pediatrics. Furthermore, the obtained cutoff values for APRI and FIB-4 showed good performance and are consistent with some previously obtained cutoff values. There was some agreement between the predictions of RF, APRI and FIB-4 for the prediction and staging of fibrosis.
Area Under Curve ; Child ; Cohort Studies ; Dataset ; Fibrosis ; Forests ; Hepacivirus ; Hepatitis C ; Hepatitis C, Chronic ; Hepatitis ; Humans ; Liver Cirrhosis ; Machine Learning ; Medical Informatics ; Pediatrics ; ROC Curve

OBJECTIVES: Conventional radiological processes have been replaced by digital images and information technology systems within South Africa and other developing countries. Picture Archiving and Communication Systems (PACS) technology offers many benefits to institutions, medical personnel and patients; however, the implementation of such systems can be a challenging task. It has been documented that South Africa has been using PACS for more than a decade in public hospitals with moderate success. The aim of this study was to identify and describe the PACS challenges endured by PACS vendors during implementation in the South African public healthcare sector. METHODS: This was achieved by engaging in a methodological approach that was qualitative in nature collecting data through semi structured interviews from 10 PACS experts/participants which were later analysed qualitatively. RESULTS: The findings show that PACS vendors have countless challenges, some of which include space, insufficient infrastructure, image storage capacity, system maturity and vendor related concerns. It was clear that the PACS experts readily offered contextually appropriate descriptions of their encounters during PACS implementations in South African public healthcare institutions. CONCLUSIONS: PACS vendors anticipate these challenges when facing a public healthcare institution and it is recommended that the hospital management and potential PACS stakeholders be made aware of these challenges to mitigate their effects and aid in a successful implementation.
Commerce ; Delivery of Health Care ; Developing Countries ; Health Care Sector ; Hospitals, Public ; Humans ; Information Storage and Retrieval ; Medical Informatics ; Medical Informatics Computing ; Radiography ; Radiology Information Systems ; South Africa

OBJECTIVES: Genetic disorders are the main causes of many other diseases. Integrating genetic data into Electronic Health Records (EHRs) can facilitate the management of genetic information and care of patients in clinical practices. The aim of this study was to identify the main requirements for integrating genetic data into the EHR system from the medical geneticists' perspectives. METHODS: The research was completed in 2018 and consisted of two phases. In the first phase, the main requirements for integrating genetic data into the EHR system were identified by reviewing the literature. In the second phase, a 5-point Likert scale questionnaire was developed based on the literature review and the results derived from the first phase. Then, the Delphi method was applied to reach a consensus about the integration requirements. RESULTS: The findings of the first phase showed that data elements, including patients' and healthcare providers' personal data, clinical and genetic data, technical infrastructure, security issues and functional requirements, should be taken into account before data integration. In the second phase, a consensus was reached for most of the items (mean ≥3.75). The items with a mean value of less than 2.5 did not achieve a consensus and were removed from the final list. CONCLUSIONS: The integration of genetic data into the EHRs can provide a ground for increasing accuracy and precision in the diagnosis and treatment of genetic disorders. Such integration requires adequate investments to identify users' requirements as well as technical and non-technical issues.
Consensus ; Delivery of Health Care ; Diagnosis ; Electronic Health Records ; Genetics ; Humans ; Investments ; Medical Informatics ; Methods

OBJECTIVES: Breast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used. METHODS: We used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble. RESULTS: Experimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data. CONCLUSIONS: We compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.
Breast Neoplasms ; Breast ; Classification ; Female ; Forests ; Humans ; Linear Models ; Machine Learning ; Medical Informatics ; Statistics as Topic

There are no drugs without the risk of potential adverse reactions. All pharmacologically active substances can cause adverse drug reactions (ADRs). This paper aims at introducing recent trends in pharmacosurveillance systems for ADRs, which can be broadly classified into type A and B reactions. Since type A reactions are associated with drug pharmacology, they are usually dose-dependent and predictable. Whereas, type B reactions occur in some susceptible individuals, regardless of the pharmacological action of drug. Drug hypersensitivity reactions are typical examples of type B reactions and are subclassified according to the underlying pathomechanism. Recent advancements in pharmacogenomics have enlightened the understanding of individual differences in drug efficacy and susceptibility to ADRs. Therefore, expectations for safe personalized medicines are higher than ever before. However, premarketing clinical trials are too small and too short to uncover rare but serious ADRs and detect long-standing ADRs. In the past, post-marketing surveillance systems mainly focused on passive ADR monitoring systems, based on spontaneous reports. Recently, the importance of active pharmacovigilance systems, which use big data, is growing with recent advancements in medical informatics. Thus, regarding ADRs, suspecting and detecting the causative drug using causality assessment based on data science may contribute to decrease suffering induced by ADRs.
Adverse Drug Reaction Reporting Systems ; Drug Hypersensitivity ; Drug-Related Side Effects and Adverse Reactions ; Humans ; Individuality ; Medical Informatics ; Pharmacogenetics ; Pharmacology ; Pharmacovigilance

OBJECTIVES: The objective of this study was to investigate the clinical decision support (CDS) functions and digitalization of clinical documents of Electronic Medical Record (EMR) systems in Korea. This exploratory study was conducted focusing on current status of EMR systems. METHODS: This study used a nationwide survey on EMR systems conducted from July 25, 2018 to September 30, 2018 in Korea. The unit of analysis was hospitals. Respondents of the survey were mainly medical recorders or staff members in departments of health insurance claims or information technology. This study analyzed data acquired from 132 hospitals that participated in the survey. RESULTS: This study found that approximately 80% of clinical documents were digitalized in both general and small hospitals. The percentages of general and small hospitals with 100% paperless medical charts were 33.7% and 38.2%, respectively. The EMR systems of general hospitals are more likely to have CDS functions of warnings regarding drug dosage, reminders of clinical schedules, and clinical guidelines compared to those of small hospitals; this difference was statistically significant. For the lists of digitalized clinical documents, almost 93% of EMR systems in general hospitals have the inpatient progress note, operation records, and discharge summary notes digitalized. CONCLUSIONS: EMRs are becoming increasingly important. This study found that the functions and digital documentation of EMR systems still have a large gap, which should be improved and made more sophisticated. We hope that the results of this study will contribute to the development of more sophisticated EMR systems.
Appointments and Schedules ; Decision Support Systems, Clinical ; Electronic Health Records ; Health Information Exchange ; Hope ; Hospitals, General ; Humans ; Inpatients ; Insurance, Health ; Korea ; Medical Informatics ; Medical Records ; Medical Records Systems, Computerized ; Surveys and Questionnaires