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 analysis of big data in medical field cannot be isolated from the high quality clinical database, and the construction of first aid database in our country is still in the early stage of exploration. This paper introduces the idea and key technology of the construction of multi-parameter first aid database. By combining emergency business flow with information flow, an emergency data integration model was designed with reference to the architecture of the Medical Information Mart for Intensive Care III (MIMIC-III), created by Computational Physiology Laboratory of Massachusetts Institute of Technology (MIT), and a high-quality first-aid database was built. The database currently covers 22 941 medical records for 19 814 different patients from May 2015 to October 2017, including relatively complete information on physiology, biochemistry, treatment, examination, nursing, etc. And based on the database, the first First-Aid Big Data Datathon event, which 13 teams from all over the country participated in, was launched. The First-Aid database provides a reference for the construction and application of clinical database in China. And it could provide powerful data support for scientific research, clinical decision making and the improvement of medical quality, which will further promote secondary analysis of clinical data in our country.
Big Data ; Critical Care ; Databases, Factual ; Humans ; Medical Informatics

BACKGROUND: Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen lung cancer nodules is a huge workload. To test the effect of artificial intelligence in automatic identification of lung cancer by using artificial intelligence to find the lung cancer nodules automatically in the chest CT of 1 mm and 5 mm thick. METHODS: 5,000 cases of T1 stage lung cancer patients with 1 mm and 5 mm layer thickness were respectively labeled and learned by computer neural network, the algorithm of forming pulmonary nodules was carried out. 500 cases of chest CT in T1 stage lung cancer patients with 1 mm and 5 mm thickness were tested by artificial intelligence formation, and the sensitivity and specificity were compared with artificial reading. RESULTS: Using artificial intelligence to read chest CT 500 in 5 mm, the sensitivity was 95.20%, the specificity was 93.20%, and the Kappa value of two times repeated read was 0.926,1. For 1 mm chest CT 500 cases, the sensitivity is 96.40%, the specificity is 95.60%, and the Kappa reads two times is 0.938,6. Compared with 5 doctors, the same CT sets with 1 mm thickness were read. The detection rates of artificial intelligence and artificial reading were similar to those of lung cancer nodules and negative control read films, and there was no significant difference between them. In the comparison of the same CT slices with 5 mm thickness, the number of detection of lung cancer nodules by artificial intelligence is better than that of artificial reading, and the sensitivity is higher, but the number of false messages is increased and the specificity is slightly worse. CONCLUSIONS The automatic learning of early lung cancer chest CT images by artificial intelligence can achieve high sensitivity and specificity of early lung cancer identification, and assist doctors in the diagnosis of lung cancer.
Artificial Intelligence ; Humans ; Lung Neoplasms ; diagnosis ; pathology ; Medical Informatics ; methods ; Neoplasm Staging

BACKGROUND: It is not possible to measure how much activity is required to understand and code a medical data. We introduce an assessment method in clinical coding, and applied this method to neurosurgical terms.METHODS: Coding activity consists of two stages. At first, the coders need to understand a presented medical term (informational activity). The second coding stage is about a navigating terminology browser to find a code that matches the concept (code-matching activity). Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) was used for the coding system. A new computer application to record the trajectory of the computer mouse and record the usage time was programmed. Using this application, we measured the time that was spent. A senior neurosurgeon who has studied SNOMED CT has analyzed the accuracy of the input coding. This method was tested by five neurosurgical residents (NSRs) and five medical record administrators (MRAs), and 20 neurosurgical terms were used.RESULTS: The mean accuracy of the NSR group was 89.33%, and the mean accuracy of the MRA group was 80% (p=0.024). The mean duration for total coding of the NSR group was 158.47 seconds, and the mean duration for total coding of the MRA group was 271.75 seconds (p=0.003).CONCLUSION: We proposed a method to analyze the clinical coding process. Through this method, it was possible to accurately calculate the time required for the coding. In neurosurgical terms, NSRs had shorter time to complete the coding and higher accuracy than MRAs.
Animals ; Clinical Coding ; Humans ; Medical Informatics ; Medical Record Administrators ; Methods ; Mice ; Neurosurgeons ; Systematized Nomenclature of Medicine

Most people are now familiar with the concepts of big data, deep learning, machine learning, and artificial intelligence (AI) and have a vague expectation that AI using medical big data can be used to improve the quality of medical care. However, the expectation that big data could change the field of medicine is inconsistent with the current reality. The clinical meaningfulness of the results of research using medical big data needs to be examined. Medical staff needs to be clear about the purpose of AI that utilizes medical big data and to focus on the quality of this data, rather than the quantity. Further, medical professionals should understand the necessary precautions for using medical big data, as well as its advantages. No doubt that someday, medical big data will play an essential role in healthcare; however, at present, it seems too early to actively use it in clinical practice. The field continues to work toward developing medical big data and making it appropriate for healthcare. Researchers should continue to engage in empirical research to ensure that appropriate processes are in place to empirically evaluate the results of its use in healthcare.
Artificial Intelligence ; Delivery of Health Care ; Empirical Research ; Humans ; Learning ; Machine Learning ; Medical Informatics ; Medical Staff

The new 2019 Japanese Society of Hypertension (JSH) guidelines for the management of hypertension are now available; these update the previous guidelines published in 2014. The primary objective of the guideline is to provide all healthcare professionals with a standard management strategy and appropriate antihypertensive treatments to prevent hypertension-related target organ damage and cardiovascular events. The major changes in the new guideline relate to the definition of normal blood pressure (BP) and target BP. The terms ‘normal BP’ and ‘high normal BP’ used in the JSH 2014 guidelines are replaced with terms ‘high normal BP’ and ‘elevated BP,’ respectively. There was no change to the office BP diagnostic threshold for hypertension (140/90 mmHg). Recommended target office and home BP values for patients with hypertension aged <75 years and/or high-risk patients are <130/80 mmHg and <125/75 mmHg, respectively. Corresponding targets for elderly patients with hypertension (age≥75 years) are 140/90 and 135/85 mmHg, respectively. The goal is that these changes will contribute to reducing cardiovascular events, especially stroke and heart failure, in Japan. The dissemination of the JSH 2019 guidelines and implementation of a home BP-based approach by all general practitioners in Japan might be facilitated by digital hypertension management using health information technology.
Aged ; Antihypertensive Agents ; Asian Continental Ancestry Group ; Blood Pressure ; Delivery of Health Care ; Evidence-Based Practice ; General Practitioners ; Heart Failure ; Humans ; Hypertension ; Japan ; Medical Informatics ; Stroke

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

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