Background: Several loco-regional flaps have been described for plantar forefoot coverage. We, herein, report our single-centre experience in plantar forefoot reconstruction and propose a decision-making process based...
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Background: Several loco-regional flaps have been described for plantar forefoot coverage. We, herein, report our single-centre experience in plantar forefoot reconstruction and propose a decision-making process based on the defect's size. Methods: This is a retrospective case series study of all patients who underwent plantar forefoot reconstruction in a 10-year period. We propose a treatment algorithm, based on the defect size. Defects are classified into small, moderate and large. Small defects (<10cm(2)) can be covered with the hemi-pulp toe flap. Patients with moderate defects (10-25cm(2)) can be treated with the reverse medial plantar artery flap (MPAF) from the instep area. For large defects (>25cm(2)), we recommend regional flaps, that is the distally based sural flap (DBSF) from the ipsilateral calf, or free flaps, such as the anterolateral thigh flap (ALT) or the skin-grafted gracilis flap. Results: The data of 51 patients were collected and analysed. The median age was 58 years (range 19-84). Nine patients had small defects and underwent hemi-pulp toe flap reconstruction. Three patients presented with moderate defects that were covered with reverse MPFs. The vast majority of the patients (39 patients) had large defects. Of these, eight cases were treated with DBSF and 31 cases with free flaps. Free flap transfers were successful in 97% of the cases. Overall complication rate was 25%. Conclusion: We conclude that local flaps should be preferred in plantar forefoot reconstruction as they provide like-tissue for small to moderate defects, for large defects regional flaps or free flaps were indicated. A defect-based approach can facilitate the decision-making process. (C) 2021 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.
Atopic Dermatitis (AD) is a chronic, inflammatory skin condition that imposes an enormous personal and economic burden in the United States. Due to the ubiquity of the use of electronic medical records (EMR) in the Un...
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Atopic Dermatitis (AD) is a chronic, inflammatory skin condition that imposes an enormous personal and economic burden in the United States. Due to the ubiquity of the use of electronic medical records (EMR) in the United States, utilizing such data is critically important to studying common dermatologic diseases, such as AD. Our goal was to create a simple-to-use algorithm applied to EMR data to accurately identify AD patients thereby making it possible to efficiently use EMR data to ascertain and then study individuals with AD. Our results suggest that the algorithm that is most likely to accurately identify AD patients from the EMR based on PPV utilizes ICD-10 code for L20.89, L20.9, or L20.84 in conjunction with a diagnosis code for asthma or allergic rhinitis, treatment code, and dermatology consult code. This approach yields a PPV of 95.00% in our training cohort and 100.00% in our validation cohort. Therefore, future studies can use this algorithm to better assure that a subject has AD for studies of the pathogenesis and/or potential treatment targets of AD.
This study aimed to specialise a directional H^(2)(DH^(2))compression to matrices arising from the discontinuous Galerkin(DG)discretisation of the hypersingular equation in *** significantfinding is an algorithm that ...
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This study aimed to specialise a directional H^(2)(DH^(2))compression to matrices arising from the discontinuous Galerkin(DG)discretisation of the hypersingular equation in *** significantfinding is an algorithm that takes a DG stiffness matrix andfinds a near-optimal DH^(2) approximation for low and high-frequency *** introduced the necessary special optimisations to make this algorithm more efficient in the case of a DG stiffness ***,an automatic parameter tuning strategy makes it easy to use and *** comparisons with a classical Boundary Element Method(BEM)show that a DG scheme combined with a DH^(2) gives better computational efficiency than a classical BEM in the case of high-order finite elements and hp heterogeneous *** results indicate that DG is suitable for an auto-adaptive context in integral equations.
Rule-based classification is one of the important tasks in data mining due to its wide applications, partic-ularly in the domains that need to interpret the classification decision such as medical diagnosis. The rule-...
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Rule-based classification is one of the important tasks in data mining due to its wide applications, partic-ularly in the domains that need to interpret the classification decision such as medical diagnosis. The rule-based classification is a combination of the classification and association rule mining fields which aims at building interpretable classifiers by means of classification rules. This paper presents a novel and efficient sequential covering strategy for Classification Rule Mining to improve the interpretability of classifiers using a Discrete Equilibrium Optimization algorithm called DEOA-CRM. Our approach ben-efits from the advantages of associative classification and population-based intelligence. It is inspired by the recent meta-heuristic equilibrium optimization algorithm. New discrete operators defined enable our approach to avoid local solutions and find global ones, improving the exploration and exploitation power in the search space. The proposed DEOA-CRM is tested on a total of 12 test data sets of various sizes and benchmarked with four recent and well-known rule-based classification mining algorithms. The obtained results confirm the efficiency of our algorithm in three chosen measures. Our approach fully deserves its use for classification rules generation to help decision-makers generate accurate and interpretable models. (c) 2022 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Background and Aim The prevalence of ulcerative colitis (UC) is increasing in Japan. Validated claims-based definitions are required to investigate the epidemiology of UC and its treatment and disease course in clinic...
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Background and Aim The prevalence of ulcerative colitis (UC) is increasing in Japan. Validated claims-based definitions are required to investigate the epidemiology of UC and its treatment and disease course in clinical practice. This study aimed to develop a claims-based algorithm for UC in Japan. Methods A committee of epidemiologists, gastroenterologists, and internal medicine physicians developed a claims-based definition for UC, based on diagnostic codes and claims for UC treatments, procedures (cytapheresis), or surgery (postoperative claims). Claims data and medical records for a random sample of 200 cases per site at two large tertiary care academic centers in Japan were used to calculate the positive predictive value (PPV) of the algorithm for three gold standards of diagnosis, defined as physician diagnosis in the medical records, adjudicated cases, or registration in the Japanese Intractable Disease Registry (IDR). Results Overall, 1139 claims-defined UC cases were identified. Among 393 randomly sampled cases (mean age 44;48% female), 94% had received >= 1 systemic treatment (immunosuppressants, tumor necrosis factor inhibitors, corticosteroids, or antidiarrheals), 7% had cytapheresis, and 7% had postoperative claims. When physician diagnosis was used as a gold standard, PPV was 90.6% (95% confidence interval [CI]: 87.7-93.5). PPV with expert adjudication was also 90.6% (95% CI: 87.7-93.5). PPVs with enrollment in the IDR as gold standard were lower at 41.5% (95% CI: 36.6-46.3) due to incomplete case registration. Conclusions The claims-based algorithm developed for use in Japan is likely to identify UC cases with high PPV for clinical studies using administrative claims databases.
This paper presents a new successive approximation algorithm that can digitize the second-order difference of signal samples rather than each sample point individually or plain difference of sample points. This method...
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ISBN:
(纸本)9798350303209
This paper presents a new successive approximation algorithm that can digitize the second-order difference of signal samples rather than each sample point individually or plain difference of sample points. This method is able to drastically reduce the number of comparisons required to convert a new signal sample to digital numbers, from a fixed N comparisons commonly used in a conventional successive-approximation-register (SAR) analog-to-digital converter (ADC), to a number in between 2 and N for almost all the signal samples in an N-bit ADC. This proposed algorithm is implemented in MATLAB and tested on electrocardiogram (ECG) signals in this work. The experimental results show that our algorithm can reduce the number of comparisons by 58.75% compared to the conventional SAR ADC, and by 17.78% or more compared to several other state-of-the-art methods. In addition, it is able to reduce the DAC updating time by the same percentages, leading to lower power consumption in both DAC and digital parts of SAR ADC.
Focal liver lesions are commonly encountered. Grey-scale and Doppler sonographic characteristics of focal liver lesions are often non-specific and insufficient to conclusively characterise lesions as benign or maligna...
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Objective To develop a claims-based algorithm identifying systemic lupus erythematosus (SLE) flares using a linked claims-electronic medical record (EMR) dataset. Methods This study was a retrospective analysis of lin...
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Objective To develop a claims-based algorithm identifying systemic lupus erythematosus (SLE) flares using a linked claims-electronic medical record (EMR) dataset. Methods This study was a retrospective analysis of linked administrative claims and EMR data spanning 1 January 2003 to 31 March 2019. Included were adult SLE patients with at least 12 months of continuous enrollment in claims data, 12 months of clinical activity in EMR, and an absence of malignancies excluding basal and squamous cell carcinoma. Patient follow-up was divided into 30-day windows, and a proxy SLEDAI-2K score based on the EMR data was calculated for each 30-day period. A flare was defined as an increase of at least 4 from the baseline score. A series of potential flare predictor variables identified in claims were based on a combination of established variables from a previous algorithm, with the addition of other SLE-related indicators based on clinical input. Logistic regression models were built to predict monthly SLE flares. Results Inclusion criteria identified 2427 patients. Results from a logistic model with forward selection capping the number of variables at 10 performed well with a c-statistic of 0.76 and a Brier score of 0.07. The top five predictors were any inpatient admission (OR = 4.76), outpatient office visit (OR = 3.04), MRI (OR = 2.26), ER visit (OR = 2.25), and number of rheumatology visits (OR = 1.75);p < .01 for all. Conclusions The final algorithm shows promise in providing an alternative and more streamlined way for identifying likely flares in administrative claims data that will advance the study of SLE within the context of flares.
Introduction: Latin America accounts for one-quarter of global COVID-19 cases and one-third of deaths. Inequalities in the region lead to barriers to the best use of diagnostic tests during the pandemic. There is a ne...
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Introduction: Latin America accounts for one-quarter of global COVID-19 cases and one-third of deaths. Inequalities in the region lead to barriers to the best use of diagnostic tests during the pandemic. There is a need for simplified guidelines that consider the region's limited health resources, international guidelines, medical literature, and local expertise. Methods: Using a modified Delphi method, 9 experts from Latin American countries developed a simplified algorithm for COVID-19 diagnosis on the basis of their answers to 24 questions related to diagnostic settings, and discussion of the literature and their experiences. Results: The algorithm considers 3 timeframes ( 7 days, 8-13 days, and 14 days) and presents diagnostic options for each. SARS-CoV-2 real- time reverse transcription-polymerase chain reaction is the test of choice from day 1 to 14 after symptom onset or close contact, although antigen testing may be used in specific circumstances, from day 5 to 7. Antibody assays may be used for confirmation, usually after day 14;however, if clinical suspicion is very high, but other tests are negative, these assays may be used as an adjunct to decision-making from day 8 to 13. Conclusion: The proposed algorithm aims to support COVID-19 diagnosis decision-making in Latin America. (c) 2021 Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Artificial intelligence (AI) has the capability of making decisions in real-time using well-before techniques and computer technologies built through data analysis to instantly adapt and learn to provide more complex ...
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ISBN:
(纸本)9791188428106
Artificial intelligence (AI) has the capability of making decisions in real-time using well-before techniques and computer technologies built through data analysis to instantly adapt and learn to provide more complex actions to circumstances. Human resource management (HRM), which incorporates both the human aspect and the use of AI tools, can provide employees with a better perception. The component of HRM decision-making by AI technologies has not been hindered by a restricted awareness of the theoretical underpinnings of AI integration;however, the enhanced usage of artificial intelligence and advancements in AI qualities have put a greater emphasis on the moral values and administrators influencing AI development for using Data-driven forecasting have been suggested for HRM to use to forecast employee desires and revenue growth. The emphasis on decision- making in AI technologies is abruptly shifting to strategies. Machine learning concentrates on enabling computers to make logical conclusions by educating them to adapt to shifts in innovation or to new knowledge. While ML is an enhanced form of AI that analyses data to find similarities and alters program action steps, AI simplifies and converts data into a format that is simple to grasp. It emphasizes the development of algorithms that will enhance HR choices by utilizing machine learning to create precise forecasts.
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