A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

A Longitudinal Nonparametric Classification Method for Assessing Change in Latent Attributes Over Time

<p>This study proposed a longitudinal Hamming distance discrimination (Long-HDD) method to improve the application of longitudinal cognitive diagnosis in practical teaching by introducing a simple computation and less time-consuming nonparametric classification method—HDD—into longitudinal diagnostic data processing. Compared with the HDD, the proposed method represents correlation or dependence between adjacent time points of the same student using Hamming distance in anticipation of using information from the previous time point to improve the classification accuracy at the current time point. A simulation study was conducted to explore the performance of the proposed method in longitudinal diagnostic data analysis and to compare the performance of the proposed method with the HDD and a parametric longitudinal diagnostic classification model. The findings suggest that (1) the Long-HDD can provide high classification accuracy in longitudinal diagnostic data analysis; (2) compared with the parametric model, the Long-HDD is almost unaffected by sample size and performs better than the parametric model in small sample sizes; and (3) the Long-HDD consumes much less computing time than the parametric model. Overall, the Long-HDD is well suited to analyzing longitudinal diagnostic data and can provide speedy diagnostic feedback due to its convenient computation, which is especially significant in small-scale assessments at the classroom and school levels.</p>

Evaluation of the effectiveness of hip arthroscopy in the surgical treatment of ANFH in adults

During 2017–2021 in the facilities of the orthopedic department of the Khabarovsk Krai Clinical Hospital named after prof. O.V. Vladimirtseva patients with avascular necrosis of the femoral head (ANFH) stages 0, 1 and 2 have been surgically treated with minimally invasive two-stage decompression with bone alloplasty. The results of radiological diagnosis and arthroscopic picture in the initial stages of the disease were evaluated. The results obtained, the identity of the MRI diagnostic data and the arthroscopic featers indicate the unreasonableness of performing therapeutic and diagnostic arthroscopy in combination with tunnelization, revision of the femoral head cyst and further alloplasty in stages 1 and 2 of the disease, which does not exclude the possibility of using this technology in other stages of ANFH

The Object According State Prediction to Diagnostic Data

The predicting methodology the state of the object based on diagnostic data is considered. With the selected parameter that determines the state of the object, it is measured in real time at a fixed sampling step. According to the measurement data, the value of this parameter is predicted in the future. This operation is implemented by an extrapolator of the l order - a l degree polynomial, built using the least squares method based on the previous measurements results. The changing process model of the diagnosed parameter is a random time function described by the stationary centered random component sum and a mathematical expectation deterministic change. The estimating prediction error method and the extrapolator parameters influence on its value are presented.

Utilization of 2D Barcode Technology to Create Surgical Pathology Reports

Introduction/Objective After professional transcription service is eliminated, pathologists inevitably undertake the task of diagnostic data entry into pathology repot by adapting a variety of methods such as speech recognition, manual typing, and pre-texted command. Errors and inefficiency in reporting remain common problems, especially for information with unusual syntax such as genotype or nucleotide sequences. To overcome these shortcomings, we introduce here a novel application of a well-established technology as a complementary method, namely 2- dimensional (2D) barcode symbology. Methods/Case Report Commonly used diagnostic wordings of pathology reports including specimen type, surgical procedure, diagnosis, and test results are collated and organized by organ (specimen type) and by their frequency of usage/occurrence. Next, 2D data matrix barcodes are created for these diagnostic wordings using a on-line tool (www.free-barcode-generator.net/datamatrix/). The 2D barcodes along with their text are displayed on the computer screen (or printed out as a booklet). A 2D barcode scanner (Symbol LS2208, Motorola) was used to retrieve the text information from the barcodes and transfer into the pathology report. To assess the efficacy of this barcode method, we evaluated the time of data entry into reports for 117 routine cases using an on-line stopwatch and compared with those by other data entry methods. Results (if a Case Study enter NA) Unlike manual typing or speech recognition, the barcode method did not introduce typographic or phonosemantic errors since the method simply transferred pre-texted and proof-read text content to report. It was also faster than manual typing or speech recognition, and its speed was comparable to that of the pre-text method integrated in LIS but did not require human memorization of innumerable text commands to retrieve desired diagnosis wordings. Conclusion Our preliminary results demonstrated that the diagnostic data entry time was reduced from 28.5% by other methods to 22.1% by the barcode method although due to the small sample size, statistical analysis was not conclusive.

Practical Applications of Diagnostic Data Science in Drilling and Completions

The application of data science remains relatively new to the oil and gas industry but continues to gain traction on many projects due to its potential to assist in solving complex problems. The amount and quality of the right type of data can be as much of a limitation as the complex algorithms and programing required. The scope of any data science project should look for easy wins early on and not attempt an all-encompassing solution with the click of a button (although that would be amazing). This paper focuses on several specific applications of data applied to a sizable database to extract useful solutions and provide an approach for data science on future projects. The first step when applying data analytics is to build a suitable database. This might appear rudimentary at first glance, but historical data is seldom catalogued optimally for future projects. This is especially true if specific portions of the recorded data were not known to be of use in solving future problems. The approach to improving the quality of the database for this paper is to establish requirements for the data science objectives and apply this to past, present and future data. Once the data are in the right "format", the extensive process of quality control can begin. Although this part of the paper is not the most exciting, it might be the most important, as most programing yields the same "garbage in = garbage out" equation. After the data have found a home and are quality checked, the data science can be applied. Case studies are presented based on the application of diagnostic data from an extensive project/well database. To leverage historical data in new projects, metrics are created as a benchmarking tool. The case studies in this paper include metrics such as the Known Lateral Contribution (KLC), Heel-to-Toe Ratio (HTR), Communication Intensity (CI), Proppant Efficiency (PE) and stage level performance. These results are compared to additional stimulation and geological information. This paper includes case studies that apply data science to diagnostics on a large scale to deliver actionable results. The results discussed will allow for the utilization of this approach in future projects and provide a roadmap to better understand diagnostic results as they relate to drilling and completion activity.

Common Presenting Complaints and Predisposing Factors among Adult Ophthalmic Patients with Low Vision and Blindness in Aba

Aim: To determine the common complaints and predisposing factors of low vision and blindness among adult ophthalmic patients in Abia State University Teaching Hospital, (ABSUTH), Aba, Nigeria. Study Design: A retrospective, descriptive study. Methodology: An institutional-based study involving 457 patients who attended Abia State University Teaching Hospital eye clinic between April and September 2018 was undertaken. The patients’ biodata, clinical history, ophthalmic examination findings, and results of ancillary investigations were obtained from patients’ hospital records within the period under study and analyzed using IBM SPSS version 25.0. AP-value of < 0.05 was taken to be statistical significant. Result: A total of 457 patients comprising of 206 males and 251 females, aged 18-85 years were seen in the study period. Based on World Health Organization (WHO)’s definition of low vision and blindness, two hundred and eighty-four (62.1%) patients had normal vision, 25(5.5%) patients had low vision while 32(7%) patients were blind. The most common complaint was blurring of vision, 136 (23.5%) followed by itching of the eye, 91 (15.7%) and tearing, 86 (14.9%). Forty percent of the respondents’ complaints had lasted for over 12 months before presentation at the clinic. Common predisposing factors for low vision and blindness observed in this study were hypertension (24.1%), previous drug use (5%), previous use of traditional medication (1.3%) and family ocular history (3.7%). No statistically significant association was found between diagnostic data and family ocular history, previous use of traditional medication and previous drug use. Statistically, significant relationship was found between diagnostic data and blood pressure (P< 0.001). Conclusion: There are various presentations of low vision and blindness in Aba. Routine eye check, early referral and appropriate treatment is advocated for the populace.