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Introducción. La diabetes es una enfermedad crónica que se caracteriza por el aumento de la concentración de la glucosa en sangre. Puede generar complicaciones que afectan la calidad de vida y aumentan los costos de la atención en salud. En los últimos años, las tasas de prevalencia y mortalidad han aumentado en todo el mundo. El desarrollo de modelos con gran desempeño predictivo puede ayudar en la identificación temprana de la enfermedad. Objetivo. Desarrollar un modelo basado en la inteligencia artificial para apoyar la toma de decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó a cabo un estudio de corte transversal, utilizando un conjunto de datos que incluía edad, signos y síntomas de pacientes con diabetes y de individuos sanos. Se utilizaron técnicas de preprocesamiento para los datos. Posteriormente, se construyó el modelo basado en mapas cognitivos difusos. El rendimiento se evaluó mediante tres parámetros: exactitud, especificidad y sensibilidad. Resultados. El modelo desarrollado obtuvo un excelente desempeño predictivo, con una exactitud del 95 %. Además, permitió identificar el comportamiento de las variables involucradas usando iteraciones simuladas, lo que proporcionó información valiosa sobre la dinámica de los factores de riesgo asociados con la diabetes. Conclusiones. Los mapas cognitivos difusos demostraron ser de gran valor para la identificación temprana de la enfermedad y en la toma de decisiones clínicas. Los resultados sugieren el potencial de estos enfoques en aplicaciones clínicas relacionadas con la diabetes y respaldan su utilidad en la práctica médica para mejorar los resultados de los pacientes.
Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decision-making in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.
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Innovative computer techniques are now being utilized not only in academic research but also in commercial dental practice, revolutionizing various areas of dentistry. This digitalization trend is driven by the increasing demands for treatment and diagnosis in the eld. Accurate diagnosis is crucial in dentistry, whether it be in orthodontics, maxillofacial surgery, periodontics, or prosthetics, as it forms the basis for creating effective treatment plans and restoring patients' oral health. While a specialist's expertise plays a vital role in diagnosis and treatment planning, it is susceptible to the inherent risks of human error, given the multifactorial nature of dental conditions. Consequently, there is growing interest in leveraging multi-parametric pattern recognition methods, including statistics, machine learning, and articial intelligence (AI), to enhance clinical decision-making. The introduction of clinical decision support systems (CDSS) and genetic algorithms (GAs) in dental research and clinical practice holds great promise for both healthcare professionals and patients. Extensive work has been undertaken to develop CDSS in dentistry, and this article reviews the latest advancements in this eld.
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In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.
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The clinical decision support system can provide medical staff with targeted patient diagnosis, treatment and care plan according to the recommendations of the guidelines, and assist medical staff to make clinical decisions. However, the adherence to clinical decision support system which based on guidelines was poor in clinical practice. Therefore, this article reviewed the influence factors of adherence to clinical decision support system which based on guideline from four aspects: system factors, individual factors, organizational factors and environmental factors, so as to improve the hindering factors and promote the application of clinical decision support system which based on guideline in future research and clinical practice.
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Objective:To study the practical efficacy of the clinical decision support system for diagnosis and treatment of thyroid cancer (CDSS-TC) in assisting doctors to complete several diagnosis and treatment tasks, and to make a preliminary evaluation of its clinical practicability according to the test results.Methods:From Jan. 2022 to Mar. 2022, 90 patients with thyroid cancer who were admitted to the Head and Neck Surgery Department of Shaw Hospital affiliated to Zhejiang University were prospectively analyzed, and the average time spent in reading the pre-operative B-ultrasound report, as well as the individual fitness of the dose adjustment of eugenol in 70 patients with thyroid cancer after surgery. A retrospective analysis was made of the compliance of the basis of the "recommended scheme" and the deviation of the basis of the doctor’s "final scheme" for the preoperative surgery of 120 patients with thyroid cancer who were treated for the first time in the head and neck surgery of Shaw Hospital affiliated to Zhejiang University from Mar. 2021 to May. 2021. All cases were treated by pure artificial (group A) and CDSS-TC assisted (group B) , and the differences in organization were compared.Results:The average time for disposal of a single B-ultrasound report in Group B was much shorter than that in Group A ( P=5.600E-04) ; The number of patients with excellent grade and the total number of patients with excellent grade and qualified grade recommended by the doctor in group B were significantly higher than those in group A ( P=7.819E-20 and P=1.335E-18) ; The conformity rate of the basis of CDSS-TC "Recommended Scheme" ≥ 98%; The deviation rate of the basis for "final protocol" of doctors in group B was lower than that in group A ( P=0.059 for total resection or not, P=0.075 for lateral neck dissection or not) . Conclusions:CDSS-TC can accurately extract the disease-related source information in all the original examination/laboratory reports, and provide accurate decision-making suggestions through efficient correlation analysis. In view of the accurate and objective conclusions of its analysis, it can provide high-quality and all-link decision support for doctors’ clinical diagnosis and treatment, and is an ideal information work platform.
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Clinical decision support system based on medical artificial intelligence(AI)is a key link in medical artificial intelligence transformation of Alzheimer's disease.This paper reviews the status of medical AI used for diagnosis and treatment of Alzheimer's disease, proposed the deficiencies existing in the current application process, in order to provide new ideas for the development of a more professional clinical decision support system for Alzheimer's disease that is suitable for China's national conditions.
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Objective:To evaluate the effectiveness of antimicrobial stewardship based on self-developed antibiotic clinical decision support system (aCDSS) in the inpatients at a tertiary hospital for consecutive 6 years, and to provide reference for rational use and antimicrobial stewardship.Methods:aCDSS was self-designed based on information technology and applied in clinical use in our hospital from 2015. Data of inpatient information and antibacterial use from January 2015 to December 2020 were collected from HIS and aCDSS. A retrospective study was conducted in all inpatients on the utilization rate and antibiotic use density.Results:Since 2015, with the comprehensive implementation of antimicrobial stewardship based on the aCDSS,there was a significant decline on the annual rate of antibiotic usage from 44.18% in 2015 to 38.70% in 2020, as well as on the usage rate of extended-spectrum antimicrobial agents including carbapenems, broad-spectrum β-lactam/β-lactamase inhibitors, tigecycline, broad-spectrum cephalosporins, fluoroquinolones, as well as glycopeptide and antifungal drugs. Compared with 2015, the usage of carbapenems, tigecycline and broad-spectrum β-lactam/β-lactamase inhibitors was declined nearly 50% in 2020, and the density of carbapenems and tigecycline were decreased by 29.6% and 7.1%, respectively in 2020. On the other side, the utilization rate and use density of narrow-spectrum cephalosporins continued to increase by year, the use density of narrow-spectrum cephalosporins accounting for 28.2% of all antibiotics in 2020.Conclusions:With the comprehensive implementation of aCDSS, the utilization rate and density of broad-spectrum and high-priced antibacterial drugs in our hospital have decreased continuously to decline in the past 6 years, while the proportion of narrow-spectrum antimicrobials has increased year by year, indicating that the structure of antimicrobial use has been continuously optimized and that antimicrobial stewardship based on the information technology have achieved remarkable results.
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Objective:To analyze the application status and problems of clinical decision support system(CDSS) in medical institutions in China, and put forward corresponding suggestions.Methods:From April to May in 2020, a questionnaire survey was conducted in 1 013 medical institutions in 31 provinces of China. The contents of the questionnaire included the current situation of CDSS installation and deployment, the purpose of establishment, the source of knowledge base, the content to be optimized and the factors hindering the use.Results:199(19.64%) medical institutions had CDSS, among which 123 were used in the whole hospital and 76 in some departments; 426 medical institutions did not use CDSS, but had plans to use it. It was found that the current CDSS system had setbacks, such as big cognitive difference, lack of authority in knowledge, high difficulty in data governance, lack of industry standards and so on.Conclusions:In the future, the standardized use of CDSS in medical institutions could be promoted from the aspects of raising awareness, establishing knowledge authority and establishing standards.
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On May 25, 2019, the World Health Assembly approved the eleventh revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), containing a chapter on traditional medicine. This means that the traditional East Asian medicine (TEAM) is now officially recognized as a part of mainstream medical practice. However, the patterns presented in the ICD-11 traditional medicine chapter are only the tip of the iceberg of TEAM clinical practice, and it will be necessary to supplement and upgrade the contents. In order to implement this, objectification and standardization of TEAM must be premised, and grafting with proper modern science and technology is imperative. Pattern Identification and Prescription Expert-11 (PIPE-11), which is a TEAM clinical decision support system, adopts vastly from clinical literature on pattern identification and the prescription. By adopting the rule-based reasoning method, the way of diagnosis and prescription by a TEAM practitioner in actual clinical practice is implemented as it is. PIPE-11 could support to improve both the accuracy of medical diagnosis and the reliability of the medical treatment of TEAM in clinical practices. In the field of research, it might facilitate the usage for reliable reference for symptoms and signs retrieval and patient simulation. In the field of education, it can provide a high level of training for learning pattern identification and prescription, and further be used to reinforce skills of diagnosis and prescription by providing self-simulation methods. Therefore, PIPE-11 as a digital application is expected to support the traditional medicine chapter of ICD-11 to successfully contribute to the improvement of human health.
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Objective Establishing intelligent nursing clinical decision support system to improve the safety, quality and efficiency of clinical nursing work. Methods Guided by the HIMSS EMRAM Analytics stage 7 evaluation standard, the nursing decision support system is continuously improved through the establishment of knowledge bases such as nursing plans and nursing conventions. Results By comparing the writing time of nursing documents, the correct rate of nursing diagnosis, and the incidence of nursing risk events before and after the used of the system, the results showed that after the used of the nursing decision-making support system, whole hospital′s writing time could save 222.5 hours per day, improved accuracy of nursing diagnosis from 68.33％ (205/300)to 90.67％ (272/300), the difference was statistical significace (χ2=45.907, P<0.05). Decreased incidents, hospital-wide, on falls from 0.127‰(80/631702) to 0.071‰(45/638715),and on pressure ulcer form 0.064‰ (41/631702)to 0.028‰(18/638715), the difference was statistical significace (χ2=13.004～15.071, P<0.05). Conclusion Nursing clinical decision support system is the trend of hospital informatization and is worthy of clinical application.
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Explaining the clinical decision support system and its related concepts, reviewing the application status of the clinical decision support system in the nursing field, summarizing the necessity and challenges of developing the clinical decision system in the nursing field, hoping to provide suggestion for the development of nursing decision support system suitable for China′s clinical status
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Explaining the clinical decision support system and its related concepts, reviewing the application status of the clinical decision support system in the nursing field, summarizing the necessity and challenges of developing the clinical decision system in the nursing field, hoping to provide suggestion for the development of nursing decision support system suitable for China′s clinical status
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PURPOSE: Patients with a history of radiocontrast media (RCM) hypersensitivity can be overlooked, resulting in repeated reactions. Therefore, a consultation support system for RCM hypersensitivity has been in operation at Seoul National University Bundang Hospital since December 2011. We analyzed the effect of this system on physicians' practice. METHODS: A retrospective study was conducted on patients with previous RCM reactions (December 1, 2010 to November 30, 2012). The control period was December 2010 to November 2011, and the intervention period was December 2011 to November 2012. The primary outcome was the composite outcome of premedication and consultation. Premedication was defined as preventive medication prescribed by the physician who ordered RCM-enhanced computed tomography (CT) at the same time. The secondary outcome was the recurrence rate after using the consultation support system. RESULTS: A total of 189 clinicians prescribed 913 CT scans during the control period and 225 clinicians performed 1,153 examinations during the intervention period. The odds ratio (OR) of achieving the composite outcome increased significantly after use of the consultation support system (OR, 1.54; 95% confidence interval [CI], 1.15–2.05). Clinicians in both medical (OR, 1.48; 95% CI, 1.06–2.07) and surgical (OR, 2.07; 95% CI, 1.24–3.46) departments showed significant changes in their behavior, whereas those in the emergency department did not (OR, 1.07; 95% CI, 0.41–2.78). Professors (OR, 1.47; 95% CI, 1.06–2.04) and trainees (OR, 1.97, 95% CI, 1.22–3.18) showed significant changes in their behavior toward patients with previous RCM reactions. The behavior of 86 clinicians who ordered CT scans during both the control and intervention periods was unchanged. CONCLUSIONS: The consultation support system for those with previous RCM hypersensitivity reactions changed physicians' practice patterns and decreased recurrent RCM hypersensitivity reactions as well.
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Humans , Contrast Media , Drug Hypersensitivity , Emergency Service, Hospital , Hypersensitivity , Odds Ratio , Practice Patterns, Physicians' , Premedication , Recurrence , Retrospective Studies , Seoul , Tomography, X-Ray ComputedABSTRACT
The paper introduces the basic situation of research on interoperability of the Clinical Decision Support System (CDSS),based on the types of standards for semantic interoperability and functional requirements for the development and arrangement of the CDSS,classifies the standards in the CDSS,and discusses the application of various standards in the CDSS.
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Through collection,recording,integration and sharing of patients′ data,real-time monito-ring of vital signs,digital information system plays a role in simplifying the medical process and supporting decision-making clinically.Meantime,it can improve telemedicine and long-distance learning,boost clinical researches in critical care medicine.The construction and development of clinical information system of ICU will improve working efficiency,decrease medical risk,and accelerate the development of critical care medicine.
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Diabetes is a chronic noncommunicable disease,which is can't be cured,and only can be suppressed by long-term treatment and self-management.The clinical decision support system can simulate the thinking process of diabetes specialists in disease diagnosis,and can provide the regular medical treatment plans and recommend the optimal plans to doctors.Most of the existing clinical decision support systems are based on clinical guidelines,rule-based and case-based reasoning as well as ontology-based systems.The big data technology can acquire and process multiple heterogeneous data,and provide a more scientific personalized treatment plan.In recent years,a variety of big date processing methods have been applied to the clinical diagnosis of diabetes based on decision tree,neural network,fuzzy logic,support vector machine,APRIORI association rules and multidimensional analysis,and timing mining.However,these methods are still in preliminary stage.The framework of diabetes clinical decision support system based on big data technology was analyzed,and the future diagnostic and treatment methods were forecast.
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The paper introduced to the integration and synergy of knowledge bases upon workflow management technology.With existing medical knowledge bases as starting point,this method first developed knowledge services for each knowledge base,then orchestrated these services using workflow management technology,and finally achieved coordinated decision support applications.Such support system has found clinical uses in tertiary hospitals in China with proven outcomes.
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Artificial Intelligence (AI) to support the medical decision-making process has long been both an interest and concern of physicians and the public. However, the introduction of open source software, supercomputers, and a variety of industry innovations has accelerated the progress of the development of AI in clinical decision support systems. This article summarizes the current trends and challenges in the medical field, and presents how AI can improve healthcare systems by increasing efficiency and decreasing costs. At the same time, it emphasizes the centrality of the role of physicians in utilizing AI as a tool to supplement their decisions as they provide patient-oriented care.
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Artificial Intelligence , Clinical Decision-Making , Decision Support Systems, Clinical , Delivery of Health CareABSTRACT
〔Abstract〕 Taking Wuxi People′s Hospital Affiliated to Nanjing Medical University as an example, the paper introduces the constitu-tion, software architecture and main functions of Clinical Decision Support System ( CDSS) .The system is integrated with Electronic Medical Records ( EMR) system, providing scientific and acurate information support for clinicians in the whole diagnosis process.It has positive significance to improve the work quality and reduce medical errors.
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BACKGROUND AND PURPOSE: Thrombolysis is underused in acute ischemic stroke, mainly due to the reluctance of physicians to treat thrombolysis patients. However, a computerized clinical decision support system can help physicians to develop individualized stroke treatments. METHODS: A consecutive series of 958 patients, hospitalized within 12 hours of ischemic stroke onset from a representative clinical center in Korea, was used to establish a prognostic model. Multivariable logistic regression was used to develop the model for global and safety outcomes. An external validation of developed model was performed using 954 patients data obtained from 5 university hospitals or regional stroke centers. RESULTS: Final global outcome predictors were age; previous modified Rankin scale score; initial National Institutes of Health Stroke Scale (NIHSS) score; previous stroke; diabetes; prior use of antiplatelet treatment, antihypertensive drugs, and statins; lacunae; thrombolysis; onset to treatment time; and systolic blood pressure. Final safety outcome predictors were age, initial NIHSS score, thrombolysis, onset to treatment time, systolic blood pressure, and glucose level. The discriminative ability of the prognostic model showed a C-statistic of 0.89 and 0.84 for the global and safety outcomes, respectively. Internal and external validation showed similar C-statistic results. After updating the model, calibration slopes were corrected from 0.68 to 1.0 and from 0.96 to 1.0 for the global and safety outcome models, respectively. CONCLUSIONS: A novel computerized outcome prediction model for thrombolysis after ischemic stroke was developed using large amounts of clinical information. After external validation and updating, the model's performance was deemed clinically satisfactory.