scholarly journals Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

BJGP Open ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. bjgpopen18X101589 ◽  
Author(s):  
Emmanuel A Jammeh ◽  
Camille, B Carroll ◽  
Stephen, W Pearson ◽  
Javier Escudero ◽  
Athanasios Anastasiou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Case Control ◽  
Classification Model ◽  
Dementia Diagnosis ◽  
Machine Learning Classification ◽  
Primary Care Data ◽  
Dementia Risk ◽  
Diagnosis Of Dementia ◽  
Read Codes

BackgroundUp to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.AimThe aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.Design & settingThe study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.MethodRoutinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.ResultsThe model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.ConclusionThe results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

USING NHS PRIMARY CARE DATA TO IDENTIFY UNDIAGNOSED DEMENTIA

2015 ◽  
Vol 86 (11) ◽  
pp. e4.134-e4 ◽  
Author(s):  
Emmanuel Jammeh ◽  
Camille Carroll ◽  
Stephen Pearson ◽  
Javier Escudero ◽  
Athanasios Anastasiou ◽  
...  
Keyword(s):  
Primary Care ◽  
Cross Validation ◽  
Routine Clinical Practice ◽  
Healthy Controls ◽  
Patient Records ◽  
Dementia Diagnosis ◽  
Primary Care Data ◽  
Dementia Risk ◽  
Fold Cross Validation ◽  
Machine Learning Tool

BackgroundUp to 50% of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It may be possible to build a picture of ‘underlying undiagnosed dementia’ from a profile of symptoms recorded in routine clinical practice.AimTo develop a machine learning tool to identify patients who may have underlying dementia but have not yet received formal diagnosis from analysis of routinely collected NHS data.MethodRoutinely collected NHS READ-encoded data were obtained from 18 consenting GP surgeries across Devon, UK, totalling 26,483 patient records of those aged >65 years. 539 Patients were identified as having dementia within the 2 year study period (June 2010 to June 2012). We determined other codes assigned to these patients that may contribute to dementia risk. The dataset was used to train a supervised classifier (Naives Bayes) to discriminate between patients with underlying dementia and healthy controls using a ten-fold cross-validation approach.ResultsThe model obtained a sensitivity of 72.31% and a specificity of 83.06% for identifying dementia.ConclusionRoutinely collected NHS data can be used to identify patients who are likely to have undiagnosed dementia. This type of methodology is promising for increasing dementia diagnosis within primary care.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

scholarly journals Predicting dementia diagnosis from cognitive footprints in electronic health records: a case–control study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e043487
Author(s):  
Hao Luo ◽  
Kui Kai Lau ◽  
Gloria H Y Wong ◽  
Wai-Chi Chan ◽  
Henry K F Mak ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Hong Kong ◽  
Electronic Health Records ◽  
Case Control Study ◽  
Case Control ◽  
Dementia Diagnosis ◽  
Health Records ◽  
Electronic Health ◽  
Control Study

IntroductionDementia is a group of disabling disorders that can be devastating for persons living with it and for their families. Data-informed decision-making strategies to identify individuals at high risk of dementia are essential to facilitate large-scale prevention and early intervention. This population-based case–control study aims to develop and validate a clinical algorithm for predicting dementia diagnosis, based on the cognitive footprint in personal and medical history.Methods and analysisWe will use territory-wide electronic health records from the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong between 1 January 2001 and 31 December 2018. All individuals who were at least 65 years old by the end of 2018 will be identified from CDARS. A random sample of control individuals who did not receive any diagnosis of dementia will be matched with those who did receive such a diagnosis by age, gender and index date with 1:1 ratio. Exposure to potential protective/risk factors will be included in both conventional logistic regression and machine-learning models. Established risk factors of interest will include diabetes mellitus, midlife hypertension, midlife obesity, depression, head injuries and low education. Exploratory risk factors will include vascular disease, infectious disease and medication. The prediction accuracy of several state-of-the-art machine-learning algorithms will be compared.Ethics and disseminationThis study was approved by Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster (UW 18-225). Patients’ records are anonymised to protect privacy. Study results will be disseminated through peer-reviewed publications. Codes of the resulted dementia risk prediction algorithm will be made publicly available at the website of the Tools to Inform Policy: Chinese Communities’ Action in Response to Dementia project (https://www.tip-card.hku.hk/).

scholarly journals Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

2021 ◽  
Vol 13 (11) ◽  
pp. 6376
Author(s):  
Junseo Bae ◽  
Sang-Guk Yum ◽  
Ji-Myong Kim
Keyword(s):  
Machine Learning ◽  
Financial Management ◽  
Construction Projects ◽  
Transportation Infrastructure ◽  
Classification Model ◽  
Insurance Companies ◽  
Economic Damage ◽  
Infrastructure Projects ◽  
Machine Learning Classification ◽  
Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

O1-05-06: DEMENTIA DIAGNOSIS AND CARE IN UK ETHNIC GROUPS: INVESTIGATING INEQUALITIES IN UK PRIMARY CARE DATA

2006 ◽  
Vol 14 (7S_Part_4) ◽  
pp. P229-P230 ◽  
Author(s):  
Mary Elizabeth Jones ◽  
Tra My Pham ◽  
Irene Petersen ◽  
Kate Walters ◽  
Rosalind Raine ◽  
...  
Keyword(s):  
Primary Care ◽  
Ethnic Groups ◽  
Dementia Diagnosis ◽  
Primary Care Data

scholarly journals Trends in dementia diagnosis rates in UK ethnic groups: analysis of UK primary care data

2018 ◽  
Vol Volume 10 ◽  
pp. 949-960 ◽  
Author(s):  
Tra My Pham ◽  
Irene Petersen ◽  
Kate Walters ◽  
Rosalind Raine ◽  
Jill Manthorpe ◽  
...  
Keyword(s):  
Primary Care ◽  
Ethnic Groups ◽  
Dementia Diagnosis ◽  
Primary Care Data

scholarly journals Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

2020 ◽  
Vol 9 (10) ◽  
pp. 580 ◽  
Author(s):  
Maria Antonia Brovelli ◽  
Yaru Sun ◽  
Vasil Yordanov
Keyword(s):  
Machine Learning ◽  
Forest Dynamics ◽  
Google Earth ◽  
Classification Model ◽  
Mitigation Measures ◽  
Computational Time ◽  
Forest Change ◽  
Machine Learning Classification ◽  
Cloud Processing ◽  
Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

Healthcare resource utilization and cost in dementia: are there differences between patients screened positive for dementia with and those without a formal diagnosis of dementia in primary care in Germany?

2015 ◽  
Vol 28 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Bernhard Michalowsky ◽  
Tilly Eichler ◽  
Jochen René Thyrian ◽  
Johannes Hertel ◽  
Diana Wucherer ◽  
...  
Keyword(s):  
Primary Care ◽  
Resource Utilization ◽  
Healthcare Cost ◽  
Healthcare Resource ◽  
Healthcare Resource Utilization ◽  
Community Dwelling ◽  
Total Healthcare Cost ◽  
Dementia Diagnosis ◽  
Medication Treatment ◽  
Diagnosis Of Dementia

ABSTRACTBackground:It is well-known that dementia is undiagnosed, resulting in the exclusion of patients without a formal diagnosis of dementia in many studies. Objectives of the present analyses were (1) to determine healthcare resource utilization and (2) costs of patients screened positive for dementia with a formal diagnosis and those without a formal diagnosis of dementia, and (3) to analyze the association between having received a formal dementia diagnosis and healthcare costs.Method:This analysis is based on 240 primary care patients who screened positive for dementia. Within the baseline assessment, individual data about the utilization of healthcare services were assessed. Costs were assessed from the perspective of insurance, solely including direct costs. Associations between dementia diagnosis and costs were evaluated using multiple linear regression models.Results:Patients formally diagnosed with dementia were treated significantly more often by a neurologist, but less often by all other outpatient specialists, and received anti-dementia drugs and day care more often. Diagnosed patients underwent shorter and less frequent planned in-hospital treatments. Dementia diagnosis was significantly associated with higher costs of anti-dementia drug treatment, but significantly associated with less total medical care costs, which valuated to be € 5,123 compared, to € 5,565 for undiagnosed patients. We found no association between dementia diagnosis and costs of evidence-based non-medication treatment or total healthcare cost (€ 7,346 for diagnosed vs. € 6,838 for undiagnosed patients).Conclusion:There are no significant differences in total healthcare cost between diagnosed and undiagnosed patients. Dementia diagnosis is beneficial for receiving cost-intensive anti-dementia drug treatments, but is currently insufficient to ensure adequate non-medication treatment for community-dwelling patients.

scholarly journals Music Feature Extraction and Classification Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jingwen Zhang
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Large Scale ◽  
Rapid Development ◽  
Audio Signal ◽  
Classification Model ◽  
Machine Learning Classification ◽  
Gating Mechanism ◽  
Music Information ◽  
Sound Spectrum

With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

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