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Sensor-Based Fall Risk Assessment: A Survey

Guangyang Zhao, Liming Chen, Huansheng Ning
<span title="2021-10-27">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7happfjl2zh5fbym2owb7p7kni" style="color: black;">Healthcare</a> </i> &nbsp;
After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment.  ...  Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures.  ...  Fall Risk Modeling and Assessment Approaches Conventional Machine Learning The mainstream modeling approaches in sensor-based fall risk assessment are related to machine learning techniques.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare9111448">doi:10.3390/healthcare9111448</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34828494">pmid:34828494</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8624006/">pmcid:PMC8624006</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a3xzrtpuz5dbxgblmaikv2lj24">fatcat:a3xzrtpuz5dbxgblmaikv2lj24</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220501175826/https://mdpi-res.com/d_attachment/healthcare/healthcare-09-01448/article_deploy/healthcare-09-01448.pdf?version=1635312474" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/cd/23cd6ff2efa6ffaae206b5c9ee44781b93e60ff6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare9111448"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8624006" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

Myung Woo, Brooke Alhanti, Sam Lusk, Felicia Dunston, Stephen Blackwelder, Kay S. Lytle, Benjamin A. Goldstein, Armando Bedoya
<span title="2020-08-27">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xuwogpw7hnaoljkcejqtymrqsa" style="color: black;">Journal of Personalized Medicine</a> </i> &nbsp;
We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS).  ...  Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches.  ...  Determining High Risk for Falls  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/jpm10030104">doi:10.3390/jpm10030104</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32867023">pmid:32867023</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e4h4qypafbf45p6xxruwitoycy">fatcat:e4h4qypafbf45p6xxruwitoycy</a> </span>
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Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study

Yin-Chen Hsu, Hsu-Huei Weng, Chiu-Ya Kuo, Tsui-Ping Chu, Yuan-Hsiung Tsai
<span title="2020-10-08">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
This machine learning method provided a higher sensitivity than the standard method for fall risk stratification.  ...  As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling.  ...  Acknowledgments We thank Hsueh-Lin Wang for assisting with this study.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-020-73776-9">doi:10.1038/s41598-020-73776-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33033326">pmid:33033326</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fz445dswo5g5vphwl2idy2rpbi">fatcat:fz445dswo5g5vphwl2idy2rpbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107203340/https://www.nature.com/articles/s41598-020-73776-9.pdf?error=cookies_not_supported&amp;code=8366bd89-fd8c-4caf-8184-f4f79227da4a" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2b/e9/2be9e7506f49a8234487fd0d3a67e9b159581953.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-020-73776-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

RehabMove 2018: Automatic recognition of gait patterns with machine learning

Y. Zhou, T. Hortobágyi, C.J.C. Lamoth
<span title="2019-03-26">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
Thereafter, a non-linear classification Support Vector Machine approach (SVM) was compared with Artificial Neural Networks (ANN).  ...  PURPOSE: The purpose of this study was to automatically identify gait patterns of geriatric patients with and without cognitive impairment using non-linear machine learning.  ...  Machine learning methods is becoming increasingly popular and significant to estimate fall risk and predict disorders.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.2607030">doi:10.5281/zenodo.2607030</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vcawyyp45bgtpm73v4ymubehsq">fatcat:vcawyyp45bgtpm73v4ymubehsq</a> </span>
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Fall Risk Assessment Using Wearable Sensors: A Narrative Review

Rafael N. Ferreira, Nuno Ferrete Ribeiro, Cristina P. Santos
<span title="2022-01-27">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Recently, fall risk assessment has been a main focus in fall-related research.  ...  The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous  ...  Algorithms for the Classification of Fall Risk In this fall risk assessment method, four studies implemented Machine Learning models [8, 9, 26, 27] , two considered a Deep Learning approach [25, 30]  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22030984">doi:10.3390/s22030984</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35161731">pmid:35161731</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8838304/">pmcid:PMC8838304</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ypnwwbhehzgojikvdq64glw7xa">fatcat:ypnwwbhehzgojikvdq64glw7xa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220224223941/http://repositorium.sdum.uminho.pt/bitstream/1822/76184/1/sensors-22-00984_review.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/b6/fdb63d32357e84629ec8d13c4175fb1fbc7cbc90.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22030984"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838304" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Automatic and Efficient Fall Risk Assessment Based on Machine Learning

Nadav Eichler, Shmuel Raz, Adi Toledano-Shubi, Daphna Livne, Ilan Shimshoni, Hagit Hel-Or
<span title="2022-02-17">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality  ...  Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk  ...  Acknowledgments: The authors would like to thank Said Touré for assistance in editing and labeling the videos for analysis. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22041557">doi:10.3390/s22041557</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35214471">pmid:35214471</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8875808/">pmcid:PMC8875808</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ue6q72qjbbcyxfkq6rotdvnpya">fatcat:ue6q72qjbbcyxfkq6rotdvnpya</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220426135837/https://mdpi-res.com/d_attachment/sensors/sensors-22-01557/article_deploy/sensors-22-01557.pdf?version=1645106861" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/95/e095106ea328a2f5a01e181b920e325fa5a7959f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22041557"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8875808" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results

Masoud Hemmatpour, Renato Ferrero, Bartolomeo Montrucchio, Maurizio Rebaudengo
<span title="2019-07-01">2019</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/aeevvunffnfubmsglzxv2lycny" style="color: black;">Advances in Human-Computer Interaction</a> </i> &nbsp;
An experimental analysis compares the evaluated approaches by evaluating their accuracy and ability to predict and prevent a fall.  ...  approach to avoid a fall injury.  ...  Acknowledgments This work was partially supported by the grant "Bando Smart Cities and Communities", OPLON project (OPportunities for active and healthy LONgevity) funded by the Italian Ministry for University  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/9610567">doi:10.1155/2019/9610567</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/z6meku6erfd2hg2ppctpzgkcpq">fatcat:z6meku6erfd2hg2ppctpzgkcpq</a> </span>
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Automatic Fall Risk Detection based on Imbalanced Data

Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
We assess not only different imbalanced data handling methods but also different machine learning algorithms.  ...  The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research.  ...  MACHINE LEARNING PERFORMANCE From previous vision-based approaches, they mainly use threshold and machine learning approaches as classifiers.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3133297">doi:10.1109/access.2021.3133297</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/trntfgfzlvckbdlgyfevcqk3za">fatcat:trntfgfzlvckbdlgyfevcqk3za</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211207220309/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09638689.pdf?tp=&amp;arnumber=9638689&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0c/ac/0cacbe1d08d5b154a398f67f4892bf4a7791739c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3133297"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm

Chengyin Ye, Jinmei Li, Shiying Hao, Modi Liu, Hua Jin, Zheng Le, Minjie Xia, Bo Jin, Chunqing Zhu, Shaun T. Alfreds, Frank Stearns, Laura Kanov (+4 others)
<span title="2020-03-03">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jyf7co46pvdybh6jhdipt5d3im" style="color: black;">International Journal of Medical Informatics</a> </i> &nbsp;
The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort.  ...  increased fall risk and identify their personalized risk factors to facilitate customized fall interventions.  ...  They also thank the biostatistics colleagues at the Department of Health Research and Policy for critical discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ijmedinf.2020.104105">doi:10.1016/j.ijmedinf.2020.104105</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32193089">pmid:32193089</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3dmch5bpivaublfodf3wmypv44">fatcat:3dmch5bpivaublfodf3wmypv44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307131305/https://pdf.sciencedirectassets.com/271161/AIP/1-s2.0-S1386505619314522/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEMz%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQC4S9l82C51feHvcoVx9LtBMLRRmwaPKJXZ8wPonU4yoQIgZ6VcfMgg4FAs5Vtz8EJucC6EqcE3mOGfSlWNtrkFaGIqvQMIpf%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARACGgwwNTkwMDM1NDY4NjUiDKn4jpnrfO0303KiNiqRAwcSblskjBcOx3SZvRS53gjBtqHJ%2B1AXMUFfVG4W3wGR1VHAWH7K4WFW%2FyzISHN9jy%2BuaGwbYkE0WSpsuusx%2BP2A58wo85py6f%2FPLI1Nz8bcP%2B0qdEZU58zRHUkLUNk8L7RzzK41UcU1ul%2FymrO1TP8KSdOcLZis1SAkyZFWGrPvyme%2Br%2BCdBZ7BtXw29C5ZuG8uMUJ%2BVGbC757hQk0z9if20hLwNTk62wEOM2Uir5jaBnkvnRYFXaG8arbArmBr%2FlRPDhDn2LyhNupx%2FeWcr9BhSsxiLgwKgbnypDZhU9SCtCH1MBgldtFo59DYUOUf0UGLCbs85pYpebPVBEdLQYGvTArm4YX4UN6XMX7bXn1eBgzbTIEzYifydazWDp5XFmaYs67ygZDd%2Fdur4NRRChoMlp0Z01OyWv3SSwH1EVqAs1fmNYlpAtNRMWgEmOtJXvVluLC2kRj%2BRLIpCyRbPlXhj0JYJB2C%2BqTksbzQCG8rC6TRjUhVNq%2FvlXKEwlCM9d1n3cubhW3uzWcPZgokg5NYMK2ZjvMFOusBRB892oIVAHpoCzJ9zenR4XxBBZQ%2FHtNF1ff9dlbtip1gRSRu%2F7U1fTSXY0iO3YT5Cd7puSYgaLRH1G%2BUm4EE%2B38ZDzdbELM1IxvBJiQCerZP6%2BGDfmy%2FVkq1nAyM3ydQSbGaOvhsgXsTpg3ZwXEwWKfV0UDVZXb6hf2llMD8xaO7vV9e29W3jluH4gEDqfFyLnkKqgn9GE1emOuOrwqz3efYQzVGFh%2Fgyg4IItAnQjsrVlx7zr6DYQ1GUK0BA7y4fy8lUHrLmDf%2F9w2KFONeI0CmyFzk5dlZdd2BBN8%2BRDw2pYUOYo4YxKRUZg%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20200307T131247Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYUXRDKHEK%2F20200307%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=441dd2f1db015245e46eaea7d35e1f73913d46930615c7a40ba0d7b14d8a27f9&amp;hash=03e7641b4afd22db14e32a45ae0f24451fe62457a40810f807cea21f7176b2de&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1386505619314522&amp;tid=spdf-855474a3-cf91-451c-b5a7-d86d57b9a8bf&amp;sid=4aa7c59c9c4fa84888696b55519c3b8f316agxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ijmedinf.2020.104105"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

Ramesh Rajagopalan, Irene Litvan, Tzyy-Ping Jung
<span title="2017-11-01">2017</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
data for predicting falls.  ...  In addition, these systems lack efficient user interfaces and feedback for preventing future falls.  ...  Fall Prevention Systems Fall prediction involves the design of signal-processing techniques and machine-learning algorithms for reliable estimation of fall risk and providing timely alerts before the occurrence  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s17112509">doi:10.3390/s17112509</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29104256">pmid:29104256</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5713074/">pmcid:PMC5713074</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/padl7n3ztzhlzg3m7veoxxtj2e">fatcat:padl7n3ztzhlzg3m7veoxxtj2e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201107180709/https://escholarship.org/content/qt51v1542q/qt51v1542q.pdf?t=qamuds" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a5/22/a5225519c1883140aa6dda84eee0cecdb0b3dd3e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s17112509"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5713074" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach

Ruopeng Sun, Katherine L. Hsieh, Jacob J. Sosnoff
<span title="2019-11-06">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
This investigation implemented a machine learning approach to assess the accuracy and feature importance of various postural sway metrics to differentiate individuals with MS from healthy controls as a  ...  function of physiological fall risk. 153 participants (50 controls and 103 individuals with MS) underwent a static posturography assessment and a physiological fall risk assessment.  ...  Salma Musaad at the Illinois Biostatistics Core of the Interdisciplinary Health Sciences Institute at Illinois for the assistance and feedback in the preparation of this manuscript.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-019-52697-2">doi:10.1038/s41598-019-52697-2</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31695127">pmid:31695127</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6834625/">pmcid:PMC6834625</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zjpajavwkbaavpezevrbglcqd4">fatcat:zjpajavwkbaavpezevrbglcqd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200313173554/https://www.nature.com/articles/s41598-019-52697-2.pdf?error=cookies_not_supported&amp;code=cb226f5e-35c2-47ff-ae2b-970e2fb2f0c1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/5f/2d5fdd06c369d5132360fa5019272578a207c0c2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-019-52697-2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834625" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches

Aaron Jones, Andrew P. Costa, Angelina Pesevski, Paul D. McNicholas, Dongmei Li
<span title="2018-11-01">2018</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Objective The objective of this study was to compare the performance of several commonly used machine learning methods to traditional statistical methods for predicting emergency department and hospital  ...  within six months of a comprehensive home care clinical assessment.  ...  Acknowledgments We would like to acknowledge the role of the Hamilton-Niagara-Haldimand-Brant Local Health Integration Network in providing data for this study. McNicholas.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0206662">doi:10.1371/journal.pone.0206662</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/le3rlx7gpfbuxh3xmvu35vgqsm">fatcat:le3rlx7gpfbuxh3xmvu35vgqsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190307224121/http://pdfs.semanticscholar.org/de09/4ab373815cfc8dd0550160b3a03d2ec705b5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/de/09/de094ab373815cfc8dd0550160b3a03d2ec705b5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0206662"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a>

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Rohit Tanwar, Neha Nandal, Mazdak Zamani, Azizah Abdul Manaf
<span title="2022-01-17">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7happfjl2zh5fbym2owb7p7kni" style="color: black;">Healthcare</a> </i> &nbsp;
Falling is one of the most serious health risk problems throughout the world for elderly people.  ...  Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall.  ...  Generally, machine learning algorithms or threshold-based approaches are exploited for classification purposes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare10010172">doi:10.3390/healthcare10010172</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35052335">pmid:35052335</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8776012/">pmcid:PMC8776012</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ivghof47bjhu7kggwfmjchvpam">fatcat:ivghof47bjhu7kggwfmjchvpam</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220125061046/https://mdpi-res.com/d_attachment/healthcare/healthcare-10-00172/article_deploy/healthcare-10-00172-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/d6/c7d60b2ae733f23762aed8d7bc448dcafd962d98.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/healthcare10010172"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8776012" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Sumithra Velupillai, Gergö Hadlaczky, Enrique Baca-Garcia, Genevieve M. Gorrell, Nomi Werbeloff, Dong Nguyen, Rashmi Patel, Daniel Leightley, Johnny Downs, Matthew Hotopf, Rina Dutta
<span title="2019-02-13">2019</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7qi7czvcwfgankg6qiypfrijvm" style="color: black;">Frontiers in Psychiatry</a> </i> &nbsp;
In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk.  ...  More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift  ...  DATA-DRIVEN APPROACHES Machine learning techniques are methods that learn from and model large datasets using statistical and algorithmical approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fpsyt.2019.00036">doi:10.3389/fpsyt.2019.00036</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30814958">pmid:30814958</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6381841/">pmcid:PMC6381841</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkf63z33xbhh3ckgvpe6ozu7sy">fatcat:jkf63z33xbhh3ckgvpe6ozu7sy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200311084848/https://kclpure.kcl.ac.uk/ws/files/105479281/Risk_Assessment_Tools_and_VELUPILLAI_Accepted21January2019_GOLD_VoR_CC_BY_.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/27/5a278e5713a8932dfddcdbc82de464ad05e7d465.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fpsyt.2019.00036"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381841" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry

Ahmed Nait Aicha, Gwenn Englebienne, Kimberley van Schooten, Mirjam Pijnappels, Ben Kröse
<span title="2018-05-22">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk.  ...  fall risk assessment.  ...  In conclusion, this work shows that machine learning on accelerometer data acquired in the home environment provides comparable accuracy to conventional models in the assessment of fall risk of older adults  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18051654">doi:10.3390/s18051654</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29786659">pmid:29786659</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5981199/">pmcid:PMC5981199</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qnjdjfet45fv3lzgui7nsomi64">fatcat:qnjdjfet45fv3lzgui7nsomi64</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190220110119/http://pdfs.semanticscholar.org/2744/debfb0233f1d780fc0d09ab04518f56eedad.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/27/44/2744debfb0233f1d780fc0d09ab04518f56eedad.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18051654"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5981199" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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