scholarly journals Estimated Number of Short-Stay Service Recipients in Hokkaido Prefecture, Japan, from 2020 to 2045: Estimation by Machine Learning and Review of Changing Trend by Cartogram

2020 ◽  
Author(s):  
Junko Ouchi ◽  
Kanetoshi Hattori
Keyword(s):  
Machine Learning ◽  
Population Data ◽  
Learning Approaches ◽  
Short Stay ◽  
Term Care ◽  
Administrative Unit ◽  
Future Care ◽  
Level 3 ◽  
Changing Trend ◽  
Administrative Units

Abstract Background: The present study aimed to estimate the numbers of short-stay service recipients in all administrative units in Hokkaido from 2020 to 2045 with the machine learning approaches and reviewed the changing trends of spatial distributions of the service recipients with cartograms.Methods: A machine learning approach was used for the estimation. To develop the model to estimate, population data in Japan from 2015 to 2017 were used as input signals, whereas data on the numbers of short-stay service recipients at each level of needs for long-term care (levels 1–5) from 2015 to 2017 were used as a supervisory signal. Three models were developed to avoid problems of repeatability. Then, data of the projected population in Hokkaido every 5 years from 2020 to 2045 were fed into each model to estimate the numbers of the service recipients for the 188 administrative units of Hokkaido. The medians of the estimations from the models were considered as the final results; the estimates for 188 administrative units were presented with continuous area cartograms on the map of Hokkaido.Results: The developed models predicted that the number of the service recipients in Hokkaido would peak at 18,016 in 2035 and the number of people at level 3 in particular would increase. The cartograms for levels 2 and 3 from 2020 to 2030 and level 3 for 2035 were heavily distorted in the several populated areas in Hokkaido, indicating that the majority of the service recipients would be concentrated in those populated areas. Conclusions: Machine learning approaches could provide estimates of future care demands for each administrative unit in a prefecture in Japan based on past population and care demand data. Results from the present study can be useful when effective allocations of human resources for nursing care in the region are discussed.

scholarly journals Estimated Number of Short-Stay Service Recipients in Hokkaido Prefecture, Japan, from 2020 to 2045: Estimation by Machine Learning and Review of Changing Trend by Cartogram

2020 ◽  
Author(s):  
Junko Ouchi ◽  
Kanetoshi Hattori
Keyword(s):  
Machine Learning ◽  
Human Resources ◽  
Elderly Care ◽  
Population Decline ◽  
Population Data ◽  
Learning Approaches ◽  
Short Stay ◽  
Term Care ◽  
Level 3 ◽  
Administrative Units

Abstract Background: For effective allocations of limited human resources, it is significant to have accurate estimates of future elderly care demands at the regional level, especially in areas where aging and population decline vary by region, such as Hokkaido prefecture in Japan.Objectives: The present study aimed to estimate the numbers of short-stay service recipients in all administrative units in Hokkaido from 2020 to 2045 with the machine learning approaches and reviewed the changing trends of spatial distributions of the service recipients with cartograms.Methods: A machine learning approach was used for the estimation. To develop the model to estimate, population data in Japan from 2015 to 2017 were used as input signals, whereas data on the numbers of short-stay service recipients at each level of needs for long-term care (levels 1–5) from 2015 to 2017 were used as a supervisory signal. Three models were developed to avoid problems of repeatability. Then, data of the projected population in Hokkaido every 5 years from 2020 to 2045 were fed into each model to estimate the numbers of the service recipients for the 188 administrative units of Hokkaido. The medians of the estimations from the models were considered as the final results; the estimates for 188 administrative units were presented with continuous area cartograms on the map of Hokkaido.Results: The developed models predicted that the number of the service recipients in Hokkaido would peak at 18,016 in 2035 and the number of people at level 3 in particular would increase. The cartograms for levels 2 and 3 from 2020 to 2030 and level 3 for 2035 were heavily distorted in the populated areas in Hokkaido.Discussion: The large correlation coefficients indicated the accuracy of estimations by the developed models. The growing number of the service recipients especially at level 3 by 2035 was assumed to be related to aging of the first baby boomers in Japan. The distortions of the cartograms suggested that the majority of the service recipients would be concentrated in the populated areas in Hokkaido. Future allocations of human resources were discussed on the basis of the findings.

scholarly journals Ligo: An Open Source Application for the Management and Execution of Administrative Data Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Greg Lawrance ◽  
Raphael Parra Hernandez ◽  
Khalegh Mamakani ◽  
Suraiya Khan ◽  
Brent Hills ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Administrative Data ◽  
Data Science ◽  
Population Data ◽  
Probabilistic Methods ◽  
Learning Approaches ◽  
Web Interface ◽  
Science Community ◽  
Comparison Algorithms

IntroductionLigo is an open source application that provides a framework for managing and executing administrative data linking projects. Ligo provides an easy-to-use web interface that lets analysts select among data linking methods including deterministic, probabilistic and machine learning approaches and use these in a documented, repeatable, tested, step-by-step process. Objectives and ApproachThe linking application has two primary functions: identifying common entities in datasets [de-duplication] and identifying common entities between datasets [linking]. The application is being built from the ground up in a partnership between the Province of British Columbia’s Data Innovation (DI) Program and Population Data BC, and with input from data scientists. The simple web interface allows analysts to streamline the processing of multiple datasets in a straight-forward and reproducible manner. ResultsBuilt in Python and implemented as a desktop-capable and cloud-deployable containerized application, Ligo includes many of the latest data-linking comparison algorithms with a plugin architecture that supports the simple addition of new formulae. Currently, deterministic approaches to linking have been implemented and probabilistic methods are in alpha testing. A fully functional alpha, including deterministic and probabilistic methods is expected to be ready in September, with a machine learning extension expected soon after. Conclusion/ImplicationsLigo has been designed with enterprise users in mind. The application is intended to make the processes of data de-duplication and linking simple, fast and reproducible. By making the application open source, we encourage feedback and collaboration from across the population research and data science community.

scholarly journals Using Information on Settlement Patterns to Improve the Spatial Distribution of Population in Coastal Impact Assessments

2018 ◽  
Vol 10 (9) ◽  
pp. 3170 ◽  
Author(s):  
Jan-Ludolf Merkens ◽  
Athanasios Vafeidis
Keyword(s):  
Population Data ◽  
Population Densities ◽  
Exposed Population ◽  
Vulnerability Assessments ◽  
Baltic Sea Region ◽  
Administrative Unit ◽  
Urban Extent ◽  
Administrative Units ◽  
Uniform Population

Broad-scale impact and vulnerability assessments are essential for informing decisions on long-term adaptation planning at the national, regional, or global level. These assessments rely on population data for quantifying exposure to different types of hazards. Existing population datasets covering the entire globe at resolutions of 2.5 degrees to 30 arc-seconds are based on information available at administrative-unit level and implicitly assume uniform population densities within these units. This assumption can lead to errors in impact assessments and particularly in coastal areas that are densely populated. This study proposes and compares simple approaches to regionalize population within administrative units in the German Baltic Sea region using solely information on urban extent from the Global Urban Footprint (GUF). Our results show that approaches using GUF can reduce the error in predicting population totals of municipalities by factor 2 to 3. When assessing exposed population, we find that the assumption of uniform population densities leads to an overestimation of 120% to 140%. Using GUF to regionalise population within administrative units reduce these errors by up to 50%. Our results suggest that the proposed simple modeling approaches can result in significantly improved distribution of population within administrative units and substantially improve the results of exposure analyses.

scholarly journals Population patterns in World’s administrative units

2017 ◽  
Vol 4 (7) ◽  
pp. 170281 ◽  
Author(s):  
Oscar Fontanelli ◽  
Pedro Miramontes ◽  
Germinal Cocho ◽  
Wentian Li
Keyword(s):  
Power Law ◽  
Goodness Of Fit ◽  
Population Distribution ◽  
Population Data ◽  
One Dimension ◽  
City Population ◽  
Administrative Unit ◽  
Administrative Units ◽  
Extended Discussion ◽  
Generalized Beta Distribution

Whereas there has been an extended discussion concerning city population distribution, little has been said about that of administrative divisions. In this work, we investigate the population distribution of second-level administrative units of 150 countries and territories and propose the discrete generalized beta distribution (DGBD) rank-size function to describe the data. After testing the balance between the goodness of fit and number of parameters of this function compared with a power law, which is the most common model for city population, the DGBD is a good statistical model for 96% of our datasets and preferred over a power law in almost every case. Moreover, the DGBD is preferred over a power law for fitting country population data, which can be seen as the zeroth-level administrative unit. We present a computational toy model to simulate the formation of administrative divisions in one dimension and give numerical evidence that the DGBD arises from a particular case of this model. This model, along with the fitting of the DGBD, proves adequate in reproducing and describing local unit evolution and its effect on the population distribution.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

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