Rheumatoid arthritis(RA) has a high disability rate and is highly harmful. It has a long course of treatment and is prone to adverse reactions or events(ADR/ADE). Selection of drugs in particular shall give consideration to both benefits and risk. Tripterygium Glycosides Tablets(TGT) is one of the important drugs for the treatment of RA. It has a remarkable efficacy, but a strong toxicity, which is controversial in clinical use. The study was oriented to patients, and quantitatively evaluated the efficacy and risk of TGT in treatment of RA, providing an intuitive basis for clinical safety and effective application of TGT. A multi-criteria decision-making analysis(MCDA) model of TGT was built in the treatment of RA, and then benefit and risk indicators were weighted by SWING method. Totally 53 random clinical trials(RCT) in accordance with the evaluation criteria were included by Meta-analysis method. The RCT results were merged by Meta-analysis, indicating that compared with the conventional therapy of chemical immunosuppressant(CISD), TGT could improve the curative effect whether it was used alone or in combination with CISD, but it would increase the incidence of reproductive system damage. The combined administration with CISD would also increase the incidence of liver and kidney damages. Treatment outcomes varied according to the different conditions of the combined administration with CISD. Based on MCDA model and clinical results, the benefit value, risk value and benefit-risk value of different doses, courses and combined administration of TGT in the treatment with RA were compared. The results showed that when the benefit and risk of the drug were equally important to the patient, the benefit-risk value of the single administration of TGT was 59, while that of the combined administration of TGT and CISD was 39. Therefore, the benefit-risk value of the single administration of TGT was 100% better than the combined administration. When the combined administration of TGT and CISD is unavoidable, the benefit-risk value of low-dose TGT(0.10-0.99 mg·kg~(-1)·d~(-1)) was 48, while that of high-dose TGT was 36. Therefore, low-dose TGT combined with CISD was more easily accepted by patients. The 2 to 3-month treatment course had a benefit-risk value of 40, while the long treatment course had a benefit-risk value of 38. Based on existing evidences, the single administration of TGT may be better than the combined administration with CISD. If the patients need to combine with CISD to treat RA, low dosage and 2 to 3-month course may be relatively optimal.
Arthritis, Rheumatoid/drug therapy* ; Decision Support Techniques ; Drugs, Chinese Herbal/therapeutic use* ; Glycosides/therapeutic use* ; Humans ; Randomized Controlled Trials as Topic ; Tablets ; Tripterygium/chemistry*

PURPOSE: To study the performance of the combined models of Pediatric Risk of Admission (PRISA) scores I and II and Creactive protein (CRP) for prediction of hospitalization in febrile children who visited the emergency department.METHODS: We reviewed febrile children aged 4 months-17 years who visited a tertiary hospital emergency department between January and December 2017. White blood cell count, CRP concentration, the PRISA scores, and systemic inflammatory response syndrome score were calculated. We compared areas under the curves (AUCs) of the admission decision support tools for hospitalization using receiver operating characteristic curve analysis.RESULTS: Of 1,032 enrolled children, 423 (41.0%) were hospitalized. CRP and the PRISA scores were significantly higher in the hospitalization group than in the discharge group (all P < 0.001). Among the individual tools, CRP showed the highest AUC (0.69; 95% confidence interval [CI], 0.66–0.72). AUC was 0.71 (95% CI, 0.69–0.74) for the combined model of the PRISA I score and CRP, and 0.71 (95% CI, 0.68–0.74) for that of the PRISA II score and CRP. The AUC of PRISA score I and CRP combined was significantly higher than that of isolated CRP (P = 0.048).CONCLUSION: The combined model of the PRISA I score and CRP may be useful in predicting hospitalization of febrile children in emergency departments.
Area Under Curve ; C-Reactive Protein ; Child ; Decision Support Techniques ; Emergencies ; Emergency Service, Hospital ; Fever ; Hospitalization ; Humans ; Leukocyte Count ; Patient Admission ; ROC Curve ; Systemic Inflammatory Response Syndrome ; Tertiary Care Centers

There has been an exponential increase in plastic surgery cases over the last 20 years, surging from 2.8 million to 17.5 million cases per year. Seventy-two percent of these cases are being performed in the office-based or ambulatory setting. There are certain advantages to performing aesthetic procedures in the office, but several widely publicized fatalities and malpractice claims has put the spotlight on patient safety and the lack of uniform regulation of office-based practices. While 33 states currently have legislation for office-based surgery and anesthesia, 17 states have no mandate to report patient deaths or adverse outcomes. The literature on office-base surgery and anesthesia has demonstrated significant improvements in patient safety over the last 20 years. In the following review of the proceedings from the PRS Korea 2018 meeting, we discuss several key concepts regarding safe anesthesia for officebased cosmetic surgery. These include the safe delivery of oxygen, appropriate local anesthetic usage and the avoidance of local anesthetic toxicity, the implementation of Enhanced Recovery after Surgery protocols, multimodal analgesic techniques with less reliance on narcotic pain medications, the use of surgical safety checklists, and incorporating “the patient” into the surgical decision-making process through decision aids.
Anesthesia ; Checklist ; Clothing ; Decision Support Techniques ; Humans ; Korea ; Malpractice ; Oxygen ; Patient Safety ; Plastics ; Seoul ; Surgery, Plastic

OBJECTIVES: This study developed an information and communication technology (ICT)-based comprehensive health and social-needs assessment (CHSNA) system based on the International Classification of Functioning, Disability, and Health (ICF) with the aim of enhancing person-centered community care for community residents and supporting healthcare professionals and social workers who provide healthcare and social services in the community. METHODS: Items related to a CHSNA tool were developed and mapped with ICF codes. Experts validated the CHSNA system design and process using the Delphi method, and a pilot test of the initial version of the system was conducted. RESULTS: The following three steps of CHSNA were embedded in the system, which had a user-friendly screen and images: basic health assessment, life and activity assessment, and in-depth health assessment. The assessment results for the community residents were presented with visualized health profiles, including images, graphs, and an ICF model. CONCLUSIONS: The developed CHSNA system can be used by healthcare professionals, social workers, and community residents to evaluate the reasoning underlying health and social needs, to facilitate the identification of more appropriate healthcare plans, and to guide community residents to receive the best healthcare services. A CHSNA system can improve the implementation of standardized terminology utilizing the ICF and the accuracy of needs assessments of community residents.
Classification ; Community Health Services ; Decision Support Techniques ; Delivery of Health Care ; Methods ; Needs Assessment ; Patient-Centered Care ; Social Work ; Social Workers

OBJECTIVES: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. METHODS: Based on our search and exclusion criteria, we reviewed 13 studies. RESULTS: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. CONCLUSION: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
Artificial Intelligence ; Autism Spectrum Disorder ; Autistic Disorder ; Behavior Observation Techniques ; Decision Support Systems, Clinical ; Delivery of Health Care ; Diagnosis ; Mass Screening ; Methods ; Sensitivity and Specificity

PURPOSE: The use of prostate-specific antigen as a biomarker for prostate cancer (PC) has been controversial and is, therefore, not used by many countries in their national health screening programs. The biological characteristics of PC in East Asians including Koreans and Japanese are different from those in the Western populations. Potential lifestyle risk factors for PC were evaluated with the aim of developing a risk prediction model. MATERIALS AND METHODS: A total of 1,179,172 Korean men who were cancer free from 1996 to 1997, had taken a physical examination, and completed a lifestyle questionnaire, were enrolled in our study to predict their risk for PC for the next eight years, using the Cox proportional hazards model. The model’s performance was evaluated using the C-statistic and Hosmer–Lemeshow type chi-square statistics. RESULTS: The risk prediction model studied age, height, body mass index, glucose levels, family history of cancer, the frequency of meat consumption, alcohol consumption, smoking status, and physical activity, which were all significant risk factors in a univariate analysis. The model performed very well (C statistic, 0.887; 95% confidence interval, 0.879 to 0.895) and estimated an elevated PC risk in patients who did not consume alcohol or smoke, compared to heavy alcohol consumers (hazard ratio [HR], 0.78) and current smokers (HR, 0.73) (p < 0.001). CONCLUSION: This model can be used for identifying Korean and other East Asian men who are at a high risk for developing PC, as well as for cancer screening and developing preventive health strategies.
Alcohol Drinking ; Asian Continental Ancestry Group ; Body Height ; Decision Support Techniques ; Early Detection of Cancer ; Forecasting ; Glucose ; Humans ; Life Style* ; Male ; Mass Screening ; Meat ; Motor Activity ; Physical Examination ; Population Characteristics ; Proportional Hazards Models ; Prostate* ; Prostate-Specific Antigen ; Prostatic Neoplasms* ; Risk Factors ; Smoke ; Smoking

OBJECTIVE: The suicide rate in South Korea is very high and is expected to increase in coming years. Intoxication is the most common suicide attempt method as well as one of the common reason for presenting to an emergency medical center. We used decision tree modeling analysis to identify predictors of risk for suicide by intentional intoxication. METHODS: A single-center, retrospective study was conducted at our hospital using a 4-year registry of the institute from January 1, 2013 to December 31, 2016. Demographic factors, such as sex, age, intentionality, therapeutic adherence, alcohol consumption, smoking status, physical disease, cancer, psychiatric disease, and toxicological factors, such as type of intoxicant and poisoning severity score were collected. Candidate risk factors based on the decision tree were used to select variables for multiple logistic regression analysis. RESULTS: In total, 4,023 patients with intoxication were enrolled as study participants, with 2,247 (55.9%) identified as cases of intentional intoxication. Reported annual percentages of intentional intoxication among patients were 628/937 (67.0%), 608/1,082 (56.2%), 536/1,017 (52.7), 475/987 (48.1%) from 2013 to 2016. Significant predictors identified based on decision tree analysis were alcohol consumption, old age, psychiatric disease, smoking, and male sex; those identified based on multiple regression analysis were alcohol consumption, smoking, male sex, psychiatric disease, old age, poor therapeutic adherence, and physical disease. CONCLUSION: We identified important predictors of suicide risk by intentional intoxication. A specific and realistic approach to analysis using the decision tree modeling technique is an effective method to determine those groups at risk of suicide by intentional intoxication.
Alcohol Drinking ; Decision Support Techniques ; Decision Trees* ; Demography ; Emergencies ; Humans ; Intention ; Korea ; Logistic Models ; Male ; Methods ; Poisoning ; Retrospective Studies* ; Risk Factors ; Smoke ; Smoking ; Suicide

OBJECTIVES: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. METHODS: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. RESULTS: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). CONCLUSIONS: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.
Cardiac Surgical Procedures ; Critical Care* ; Decision Support Techniques ; Forecasting ; Heart Diseases ; Humans ; Intensive Care Units* ; Iran ; Length of Stay* ; Methods ; Thoracic Surgery*

Artificial intelligence (AI) is projected to substantially influence clinical practice in the foreseeable future. However, despite the excitement around the technologies, it is yet rare to see examples of robust clinical validation of the technologies and, as a result, very few are currently in clinical use. A thorough, systematic validation of AI technologies using adequately designed clinical research studies before their integration into clinical practice is critical to ensure patient benefit and safety while avoiding any inadvertent harms. We would like to suggest several specific points regarding the role that peer-reviewed medical journals can play, in terms of study design, registration, and reporting, to help achieve proper and meaningful clinical validation of AI technologies designed to make medical diagnosis and prediction, focusing on the evaluation of diagnostic accuracy efficacy. Peer-reviewed medical journals can encourage investigators who wish to validate the performance of AI systems for medical diagnosis and prediction to pay closer attention to the factors listed in this article by emphasizing their importance. Thereby, peer-reviewed medical journals can ultimately facilitate translating the technological innovations into real-world practice while securing patient safety and benefit.
Artificial Intelligence* ; Decision Support Techniques ; Diagnosis ; Humans ; Inventions* ; Journalism, Medical ; Machine Learning ; Patient Safety ; Peer Review ; Research Personnel ; Translating

OBJECTIVES: In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition. METHODS: We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system. RESULTS: In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894). CONCLUSIONS: The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment.
Artificial Intelligence ; Dataset ; Decision Support Systems, Clinical ; Decision Support Techniques ; Dental Informatics ; Dentition, Permanent ; Diagnosis ; General Practitioners ; Hospital Records ; Humans ; Machine Learning ; Malocclusion ; Orthodontists ; Parents