Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.     

人工智能技术在辅助耳鼻咽喉科医师了解过敏性鼻炎患者需求中的应用

利用人工智能技术分析近年中国互联网用户对于过敏性鼻炎（allergic rhinitis,AR）的网上检索信息,以帮助耳鼻咽喉科医师更好地了解AR患者的实际需求,从而在临床实践中更加合理地对患者进行管理。     

Label‐free multiphoton excitation imaging as a promising diagnostic tool for breast cancer

Histopathological diagnosis is the ultimate method of attaining the final diagnosis; however, the observation range is limited to the two‐dimensional plane, and it requires thin slicing of the tissue, which limits diagnostic information. To seek solutions for these problems, we proposed a novel imaging‐based histopathological examination. We used the multiphoton excitation microscopy (MPM) technique to establish a method for visualizing unfixed/unstained human breast tissues. Under near‐infrared ray excitation, fresh human breast tissues emitted fluorescent signals with three major peaks, which enabled visualizing the breast tissue morphology without any fixation or dye staining. Our study using human breast tissue samples from 32 patients indicated that experienced pathologists can estimate normal or cancerous lesions using only these MPM images with a kappa coefficient of 1.0. Moreover, we developed an image classification algorithm with artificial intelligence that enabled us to automatically define cancer cells in small areas with a high sensitivity of ≥0.942. Taken together, label‐free MPM imaging is a promising method for the real‐time automatic diagnosis of breast cancer.     

人工智能在乳腺超声中的应用与发展

人工智能是利用计算机模拟人类学习、思考及作出判断的技术和方法,在医学影像领域应用中为临床流程带来便利,也为疾病诊断、治疗和预后提供更多的信息。而超声技术与人工智能的结合为乳腺诊断带来革新,本文就人工智能在乳腺超声中的流程及方法、应用和发展作一综述。     

“毒胶囊”事件舆情分析及其对医学情报工作创新的启示

以"毒胶囊"事件为案例进行舆情监测分析,总结这一事件舆情演变的管理警示,探讨医学情报机构如何在应激事件中开展舆情分析服务,及时为政府提供决策咨询,以推动医学情报工作创新与发展。     

Data‐driven research on eczema: Systematic characterization of the field and recommendations for the future

BackgroundThe past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods.MethodsWe retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature.ResultsFive key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were “eczema” papers.ConclusionsResearch areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research.     

人工智能在面瘫识别与评估中的应用

回顾了近年来部分基于人工智能的面瘫识别及评估研究。研究方法可分为基于人工选取患者面部图像特征值和基于神经网络和患者面部图像的面瘫评估研究两大类。分析表明,人工选取特征值的方法适合小样本量的情况,但对面瘫相邻评级的分类效果有待进一步优化。而神经网络的方法能够相对较好地区分面瘫邻级,但对样本量有较高要求。两种方法均有不错的前景,人工选取与评估量表相关性更强的特征,而共同的发展方向可能是提取时间域特征,从而达到提升面瘫评估准确率的目的。     

1例人工耳蜗植入术患儿的护理

目的 探讨人工耳蜗植入术患儿的最佳护理措施.方法 对临床1例人工耳蜗植入术患儿进行手术前后的观察和护理,预防各种并发症发生.结果 患儿手术成功,术后一个月开启语言处理器能听到声音,现正接受听力语言训练,将获得精彩的有声世界,和正常儿童一样学习、生活和快乐成长.结论 针对患儿具体情况及时有效的手术前后护理,也是手术成功的重要环节之一.     

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

Background  We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. Objectives  The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). Methods  We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. Results  Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. Conclusion  An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. Trial registration  ClinicalTrials.gov identifier: {"type":"clinical-trial","attrs":{"text":"NCT04570488","term_id":"NCT04570488"}}NCT04570488.