Abstract Aim To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System. Materials and Methods The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012–2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.5%; or (2) fasting glucose levels ≥126 mg/dL; or (3) random plasma glucose levels ≥200 mg/dL; or (4) a diabetes‐related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes‐related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type. Results Presumed cases without type 1 (T1D) or type 2 diabetes (T2D) diagnosis codes were categorized as non‐diabetes/other types of diabetes. The rest were categorized as T1D if the most recent diagnosis was T1D, or otherwise categorized as T2D if the most recent diagnosis was T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from Step 1. Among the 536 reviewed cases, 159 and 64 had T1D and T2D, respectively. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98% and 96%, respectively, for T1D and 95%, 95% and 73% for T2D. Conclusion We developed a highly accurate EHR‐based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods.
[Objective] In order to study the indicated diseases and symptoms of moving cupping therapy to guide the clinical practice.[Methods] The retrospective study of periodical literature series is used and all clinical literatures about moving cupping therapy was searched in the China Journal Full Text Database of CNKI(VipDatabase,Wanfang Database and Chinese bilolgical Abstracts).The frequency of the literature and the total cases treated with moving cupping therapy were statistically analyzed.The principles and methods of evidence-based medicine were used to classify and grade the literatures and the diseases were classified according to the international statistical classification about diseases and related healthy problems.[Results] 470 effective literatures including 32 644 cases were searched out,which were involved in 16 major systems and 130 kinds of diseases.[Condusion] It is indicated that the moving cupping therapy has a large number of suitable diseases and symptoms and it is worth to be popularized.Particularly,this therapy has obvious advantages for treatment of myofascitis,frozen shoulder,influenza,cutaneous neuritis,protrasion of the lumbar intervertebral disci,facial paralysis and insomnia,cervical syndrome.
The Internet has impacted how we shop. This dissertation focuses on opening the black box of consumer experience in online retailing environments. In order to do so, a microscopic approach is used to detail the processes and internal states of a customer when interacting with a commercial web site. Based on previous research from related fields, a new concept of Shopping Experience is proposed to study the dynamic, on-going interaction between a customer and commercial web sites. Shopping Experience is defined as an episode of interaction between a customer and web sites. Flow is an optimal and intrinsically engrossing and enjoyable experience, which is so inner rewarding that people want to repeat it. How to achieve flow in web shopping? What are the factors that facilitate or hinder flow? A series of studies are carried out to programmatically tackle these questions. First, exploratory studies are used to verify the concept of shopping experience and phenomenon of flow in the Internet shopping. Secondly, more effort is undertaken to validate measures of relevant constructs. Finally, a controlled experiment is used to gauge the effect of two factors, namely design factor and task factor. The dissertation will provide deeper understanding of facilitating factors of optimal shopping experience, which certainly will help to build better online shopping environments.
Key Points KRT awareness is important for informed choice and use of dialysis modalities, but we lack validated instruments capable of measuring such awareness. We present a newly developed KRT Knowledge instrument, which can be used to evaluate the kidney failure and KRT awareness among patients with CKD. Our results show that KRT awareness is different and significantly lower than CKD awareness among patients with advanced CKD. Background Awareness of KRTs is associated with greater home dialysis use. However, validated instruments evaluating patient knowledge and awareness of various KRTs are currently lacking and are critical for informed decision making. Methods We developed a 24-item KRT knowledge instrument (Know-KRT) encompassing three domains of General, Technical, and Correlative information critical for informed dialysis decision making. We conducted a cross-sectional study among Veterans with advanced CKD to determine its reliability, dimensionality, and validity. Results The Know-KRT instrument dimensionality was acceptable with a root mean squared error of approximation of 0.095 for the conceptual three-domain model fit (χ 2 =824.6, P < 0.001). Corrected Item-Total Correlation indices were excellent (>0.4) for all individual items. Internal consistency was excellent for the full instrument, Cronbach's alpha, α =0.95, with α =0.86, 0.91, and 0.79 for the General, Technical, and Correlative domains, respectively. The Know-KRT score correlated strongly with the CKD knowledge score ( r =0.68, P < 0.001). KRT awareness was low, with an ease index of 0.181 for the full instrument. The General, Technical, and Correlative domain scores demonstrated strong correlations with the Know-KRT total score ( r =0.68, 0.61, and 0.48, respectively, P < 0.001) and CKD instrument score ( r =0.95, 0.93, and 0.77, respectively, P < 0.001). KRT and CKD awareness correlated negatively with age and positively with health literacy, employment status, hypertension, and quality of nephrology care. Conclusions We report a newly developed Know-KRT instrument with three domains having acceptable internal consistency, reliability, and validity. We show that patients with advanced CKD have low awareness of KRTs, even for items related to basic descriptions of modalities, highlighting the need for targeted patient education efforts. Clinical Trial registration number: NCT04064086. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2024_08_01_CJASNJuly197812024.mp3
Pulmonary nodules and nodule characteristics are important indicators of lung nodule malignancy. However, nodule information is often documented as free text in clinical narratives such as radiology reports in electronic health record systems. Natural language processing (NLP) is the key technology to extract and standardize patient information from radiology reports into structured data elements. This study aimed to develop an NLP system using state-of-the-art transformer models to extract pulmonary nodules and associated nodule characteristics from radiology reports. We identified a cohort of 3080 patients who underwent LDCT at the University of Florida health system and collected their radiology reports. We manually annotated 394 reports as the gold standard. We explored eight pretrained transformer models from three transformer architectures including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT), for clinical concept extraction, relation identification, and negation detection. We examined general transformer models pretrained using general English corpora, transformer models fine-tuned using a clinical corpus, and a large clinical transformer model, GatorTron, which was trained from scratch using 90 billion words of clinical text. We compared transformer models with two baseline models including a recurrent neural network implemented using bidirectional long short-term memory with a conditional random fields layer and support vector machines. RoBERTa-mimic achieved the best
Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.
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