scholarly journals Population Estimates from Orbital Data of Medium Spatial Resolution: Applications for a Brazilian Municipality

2020 ◽  
Vol 12 (9) ◽  
pp. 3565
Author(s):  
Járvis Campos ◽  
José Irineu Rangel Rigotti ◽  
Emerson Augusto Baptista ◽  
Antônio Miguel Vieira Monteiro ◽  
Ilka Afonso Reis
Keyword(s):  
Population Dynamics ◽  
Environmental Planning ◽  
External Validation ◽  
Population Estimates ◽  
Internal Validation ◽  
Small Areas ◽  
Explanatory Variables ◽  
Spectral Bands ◽  
Landsat 7 ◽  
Orbital Data

In recent decades, there has been an increase in the search for more detailed information on population dynamics, given the growing demand for more sustainable economic, social, and environmental planning. The dissemination of Geographic Information Systems (GIS) has contributed to the development of methodologies for the field of population estimates for small areas. To support more sustainable policies, this study aims to evaluate the capacity and contribution of the orbital images (Landsat ETM+) for the production of post-census population estimates for the municipality of Contagem, Minas Gerais, Brazil. Firstly, models were built using the average of the reflectance of the spectral bands of the Landsat 7 ETM+ for each special intra-municipal unit, called the census sector, as explanatory variables for the population density. Secondly, this study constructed models that use the reflectance and the distributed population at the level of the pixels of the images. All models were tested through internal validation procedures, external validation, and comparative analyses with post-census estimates. Internal validation presented excellent results (below 7%), while in external validation, the method at the level of the pixels presented consistent results, below 1% relative error. These results provide useful clues and can help policymakers in the development of more sustainable and effective public policies, insofar as population estimates are extremely important for the planning of any society.

scholarly journals Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level

2021 ◽  
Vol 10 (6) ◽  
pp. 227
Author(s):  
Yago Martín ◽  
Zhenlong Li ◽  
Yue Ge ◽  
Xiao Huang
Keyword(s):  
Large Scale ◽  
Local Population ◽  
External Validation ◽  
Population Estimates ◽  
County Level ◽  
Internal Validation ◽  
Population Movements ◽  
Dynamic Component ◽  
Home Location ◽  
Non Local

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Internal validation reveals over 80% accuracy when compared with users’ self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

scholarly journals Modification and Validation of the Phosphate Removal Model: A Multicenter Study

2021 ◽  
pp. 1-10
Author(s):  
Weichen Zhang ◽  
Qiuna Du ◽  
Jing Xiao ◽  
Zhaori Bi ◽  
Chen Yu ◽  
...  
Keyword(s):  
Polynomial Regression ◽  
External Validation ◽  
Original Model ◽  
Phosphate Concentration ◽  
Phosphate Removal ◽  
Internal Validation ◽  
Modified Model ◽  
New Model ◽  
Significant Difference ◽  
Removal Model

<b><i>Background:</i></b> Our research group has previously reported a noninvasive model that estimates phosphate removal within a 4-h hemodialysis (HD) treatment. The aim of this study was to modify the original model and validate the accuracy of the new model of phosphate removal for HD and hemodiafiltration (HDF) treatment. <b><i>Methods:</i></b> A total of 109 HD patients from 3 HD centers were enrolled. The actual phosphate removal amount was calculated using the area under the dialysate phosphate concentration time curve. Model modification was executed using second-order multivariable polynomial regression analysis to obtain a new parameter for dialyzer phosphate clearance. Bias, precision, and accuracy were measured in the internal and external validation to determine the performance of the modified model. <b><i>Results:</i></b> Mean age of the enrolled patients was 63 ± 12 years, and 67 (61.5%) were male. Phosphate removal was 19.06 ± 8.12 mmol and 17.38 ± 6.75 mmol in 4-h HD and HDF treatments, respectively, with no significant difference. The modified phosphate removal model was expressed as Tpo<sub>4</sub> = 80.3 × <i>C</i><sub>45</sub> − 0.024 × age + 0.07 × weight + β × clearance − 8.14 (β = 6.231 × 10<sup>−3</sup> × clearance − 1.886 × 10<sup>−5</sup> × clearance<sup>2</sup> – 0.467), where <i>C</i><sub>45</sub> was the phosphate concentration in the spent dialysate measured at the 45th minute of HD and clearance was the phosphate clearance of the dialyzer. Internal validation indicated that the new model was superior to the original model with a significantly smaller bias and higher accuracy. External validation showed that <i>R</i><sup>2</sup>, bias, and accuracy were not significantly different than those of internal validation. <b><i>Conclusions:</i></b> A new model was generated to quantify phosphate removal by 4-h HD and HDF with a dialyzer surface area of 1.3–1.8 m<sup>2</sup>. This modified model would contribute to the evaluation of phosphate balance and individualized therapy of hyperphosphatemia.

scholarly journals How Does Administrative Data Compare with Census Data for the Measurement of Internal Migration? The Case of Northern Ireland, 2001–2011

2021 ◽  
Author(s):  
Brian Foley ◽  
Tony Champion ◽  
Ian Shuttleworth
Keyword(s):  
Administrative Data ◽  
Internal Migration ◽  
Census Data ◽  
Local Population ◽  
Population Data ◽  
Population Estimates ◽  
Demographic Groups ◽  
Small Areas ◽  
Intercensal Period ◽  
Statistical Agencies

AbstractThe paper compares and contrasts internal migration measured by healthcard-based administrative data with census figures. This is useful because the collection of population data, its processing, and its dissemination by statistical agencies is becoming more reliant on administrative data. Statistical agencies already use healthcard data to make migration estimates and are increasingly confident about local population estimates from administrative sources. This analysis goes further than this work as it assesses how far healthcard data can produce reliable data products of the kind to which academics are accustomed. It does this by examining migration events versus transitions over a full intercensal period; population flows into and out of small areas; and the extent to which it produces microdata on migration equivalent to that in the census. It is shown that for most demographic groups and places healthcard data is an adequate substitute for census-based migration counts, the exceptions being for student households and younger people. However, census-like information is still needed to provide covariates for analysis and this will still be required whatever the future of the traditional census.

scholarly journals Automatized Hepatic Tumor Volume Analysis of Neuroendocrine Liver Metastases by Gd-EOB MRI—A Deep-Learning Model to Support Multidisciplinary Cancer Conference Decision-Making

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2726
Author(s):  
Uli Fehrenbach ◽  
Siyi Xin ◽  
Alexander Hartenstein ◽  
Timo Alexander Auer ◽  
Franziska Dräger ◽  
...  
Keyword(s):  
Liver Metastases ◽  
External Validation ◽  
High Accuracy ◽  
Hepatic Tumor ◽  
Liver Malignancies ◽  
Internal Validation ◽  
Matthew’S Correlation Coefficient ◽  
Therapy Success ◽  
Neuroendocrine Liver Metastases

Background: Rapid quantification of liver metastasis for diagnosis and follow-up is an unmet medical need in patients with secondary liver malignancies. We present a 3D-quantification model of neuroendocrine liver metastases (NELM) using gadoxetic-acid (Gd-EOB)-enhanced MRI as a useful tool for multidisciplinary cancer conferences (MCC). Methods: Manual 3D-segmentations of NELM and livers (149 patients in 278 Gd-EOB MRI scans) were used to train a neural network (U-Net architecture). Clinical usefulness was evaluated in another 33 patients who were discussed in our MCC and received a Gd-EOB MRI both at baseline and follow-up examination (n = 66) over 12 months. Model measurements (NELM volume; hepatic tumor load (HTL)) with corresponding absolute (ΔabsNELM; ΔabsHTL) and relative changes (ΔrelNELM; ΔrelHTL) between baseline and follow-up were compared to MCC decisions (therapy success/failure). Results: Internal validation of the model’s accuracy showed a high overlap for NELM and livers (Matthew’s correlation coefficient (φ): 0.76/0.95, respectively) with higher φ in larger NELM volume (φ = 0.80 vs. 0.71; p = 0.003). External validation confirmed the high accuracy for NELM (φ = 0.86) and livers (φ = 0.96). MCC decisions were significantly differentiated by all response variables (ΔabsNELM; ΔabsHTL; ΔrelNELM; ΔrelHTL) (p < 0.001). ΔrelNELM and ΔrelHTL showed optimal discrimination between therapy success or failure (AUC: 1.000; p < 0.001). Conclusion: The model shows high accuracy in 3D-quantification of NELM and HTL in Gd-EOB-MRI. The model’s measurements correlated well with MCC’s evaluation of therapeutic response.

Automated Pancreas Segmentation And Volumetry Using Deep Neural Network On Computed Tomography

2021 ◽  
Author(s):  
Sang-Heon Lim ◽  
Young Jae Kim ◽  
Yeon-Ho Park ◽  
Doojin Kim ◽  
Kwang Gi Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Segmentation ◽  
External Validation ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Quantitative Information ◽  
Similarity Coefficients ◽  
Internal Validation ◽  
Pancreas Segmentation

Abstract Pancreas segmentation is necessary for observing lesions, analyzing anatomical structures, and predicting patient prognosis. Therefore, various studies have designed segmentation models based on convolutional neural networks for pancreas segmentation. However, the deep learning approach is limited by a lack of data, and studies conducted on a large computed tomography dataset are scarce. Therefore, this study aims to perform deep-learning-based semantic segmentation on 1,006 participants and evaluate the automatic segmentation performance of the pancreas via four individual three-dimensional segmentation networks. In this study, we performed internal validation with 1,006 patients and external validation using the Cancer Imaging Archive (TCIA) pancreas dataset. We obtained mean precision, recall, and dice similarity coefficients of 0.869, 0.842, and 0.842, respectively, for internal validation via a relevant approach among the four deep learning networks. Using the external dataset, the deep learning network achieved mean precision, recall, and dice similarity coefficients of 0.779, 0.749, and 0.735, respectively. We expect that generalized deep-learning-based systems can assist clinical decisions by providing accurate pancreatic segmentation and quantitative information of the pancreas for abdominal computed tomography.

An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study

2021 ◽  
Author(s):  
Shaoxu Wu ◽  
Xiong Chen ◽  
Jiexin Pan ◽  
Wen Dong ◽  
Xiayao Diao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
External Validation ◽  
Operation Time ◽  
Short Latency ◽  
Lesion Detection ◽  
Diagnostic Study ◽  
Internal Validation ◽  
Predictive Values

Abstract Background Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. Methods In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. Results The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974–0.979) in the internal validation set and 0.990 (95% CI = 0.979–0.996), 0.982 (95% CI = 0.974–0.988), 0.978 (95% CI = 0.959–0.989), and 0.991 (95% CI = 0.987–0.994) in different external validation sets. In the CAIDS versus urologists’ comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902–0.964; and sensitivity = 0.954, 95% CI = 0.902–0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. Conclusions The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

scholarly journals Automatic differentiation of thyroid scintigram by deep convolutional neural network: a dual center study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pei Yang ◽  
Yong Pi ◽  
Tao He ◽  
Jiangming Sun ◽  
Jianan Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Predictive Value ◽  
Automatic Differentiation ◽  
External Validation ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Training Dataset ◽  
Internal Validation

Abstract Background 99mTc-pertechnetate thyroid scintigraphy is a valid complementary avenue for evaluating thyroid disease in the clinic, the image feature of thyroid scintigram is relatively simple but the interpretation still has a moderate consistency among physicians. Thus, we aimed to develop an artificial intelligence (AI) system to automatically classify the four patterns of thyroid scintigram. Methods We collected 3087 thyroid scintigrams from center 1 to construct the training dataset (n = 2468) and internal validating dataset (n = 619), and another 302 cases from center 2 as external validating datasets. Four pre-trained neural networks that included ResNet50, DenseNet169, InceptionV3, and InceptionResNetV2 were implemented to construct AI models. The models were trained separately with transfer learning. We evaluated each model’s performance with metrics as following: accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), recall, precision, and F1-score. Results The overall accuracy of four pre-trained neural networks in classifying four common uptake patterns of thyroid scintigrams all exceeded 90%, and the InceptionV3 stands out from others. It reached the highest performance with an overall accuracy of 92.73% for internal validation and 87.75% for external validation, respectively. As for each category of thyroid scintigrams, the area under the receiver operator characteristic curve (AUC) was 0.986 for ‘diffusely increased,’ 0.997 for ‘diffusely decreased,’ 0.998 for ‘focal increased,’ and 0.945 for ‘heterogeneous uptake’ in internal validation, respectively. Accordingly, the corresponding performances also obtained an ideal result of 0.939, 1.000, 0.974, and 0.915 in external validation, respectively. Conclusions Deep convolutional neural network-based AI model represented considerable performance in the classification of thyroid scintigrams, which may help physicians improve the interpretation of thyroid scintigrams more consistently and efficiently.

scholarly journals Early Identification of Patients with Acute Gastrointestinal Bleeding using Electronic Health Record Phenotyping

2020 ◽  
Author(s):  
Dennis Shung ◽  
Cynthia Tsay ◽  
Loren Laine ◽  
Prem Thomas ◽  
Caitlin Partridge ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Decision Rule ◽  
Language Processing ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Internal Validation ◽  
Electronic Health

Background and AimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify (“phenotype”) patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.MethodsWe specified criteria using structured data elements to create rules for identifying patients, and also developed a natural-language-processing (NLP)-based algorithm for automated phenotyping of patients, tested them with tenfold cross-validation (n=7144) and external validation (n=2988), and compared them with the standard method for encoding patient conditions in the EHR, Systematized Nomenclature of Medicine (SNOMED). The gold standard for GIB diagnosis was independent dual manual review of medical records. The primary outcome was positive predictive value (PPV).ResultsA decision rule using GIB-specific terms from ED triage and from ED review-of-systems assessment performed better than SNOMED on internal validation (PPV=91% [90%-93%] vs. 74% [71%-76%], P<0.001) and external validation (PPV=85% [84%-87%] vs. 69% [67%-71%], P<0.001). The NLP algorithm (external validation PPV=80% [79-82%]) was not superior to the structured-datafields decision rule.ConclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision-making in real time for patients with acute GIB presenting to the ED.

Sign in / Sign up

Export Citation Format

Share Document