{"title":"预测老年人跌倒风险:使用患者特征、功能性平衡测试和计算机动态体位测量法对机器学习模型进行比较分析。","authors":"Emre Soylemez, Suna Tokgoz-Yilmaz","doi":"10.1017/S0022215124002160","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to predict the risk of falling using patient characteristics, computerized dynamic posturography and functional balance tests in machine learning.</p><p><strong>Methods: </strong>One hundred twenty elderly individuals were included in this study. The fall status, physical characteristics and medical history of individuals were investigated. Pure tone audiometry test, simple functional balance tests and sensory organization test were applied to the individuals.</p><p><strong>Results: </strong>The machine learning model that incorporated co-morbidities, physical characteristics and functional balance tests achieved a 100 per cent accuracy in predicting fall risk. Models using only co-morbidities and physical characteristics, functional balance tests or the sensory organization test had accuracies of 87.5 per cent, 83.34 per cent and 91.66 per cent, respectively.</p><p><strong>Conclusion: </strong>Advanced balance systems are not always necessary to assess fall risk. Instead, fall risk can be effectively determined using simple balance tests, co-morbidities, and patient characteristics in machine learning.</p>","PeriodicalId":16293,"journal":{"name":"Journal of Laryngology and Otology","volume":" ","pages":"464-472"},"PeriodicalIF":0.8000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting fall risk in elderly ındividuals: a comparative analysis of machine learning models using patient characteristics, functional balance tests and computerized dynamic posturography.\",\"authors\":\"Emre Soylemez, Suna Tokgoz-Yilmaz\",\"doi\":\"10.1017/S0022215124002160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study aimed to predict the risk of falling using patient characteristics, computerized dynamic posturography and functional balance tests in machine learning.</p><p><strong>Methods: </strong>One hundred twenty elderly individuals were included in this study. The fall status, physical characteristics and medical history of individuals were investigated. Pure tone audiometry test, simple functional balance tests and sensory organization test were applied to the individuals.</p><p><strong>Results: </strong>The machine learning model that incorporated co-morbidities, physical characteristics and functional balance tests achieved a 100 per cent accuracy in predicting fall risk. Models using only co-morbidities and physical characteristics, functional balance tests or the sensory organization test had accuracies of 87.5 per cent, 83.34 per cent and 91.66 per cent, respectively.</p><p><strong>Conclusion: </strong>Advanced balance systems are not always necessary to assess fall risk. Instead, fall risk can be effectively determined using simple balance tests, co-morbidities, and patient characteristics in machine learning.</p>\",\"PeriodicalId\":16293,\"journal\":{\"name\":\"Journal of Laryngology and Otology\",\"volume\":\" \",\"pages\":\"464-472\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Laryngology and Otology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1017/S0022215124002160\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"OTORHINOLARYNGOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Laryngology and Otology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1017/S0022215124002160","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"OTORHINOLARYNGOLOGY","Score":null,"Total":0}
Predicting fall risk in elderly ındividuals: a comparative analysis of machine learning models using patient characteristics, functional balance tests and computerized dynamic posturography.
Objectives: This study aimed to predict the risk of falling using patient characteristics, computerized dynamic posturography and functional balance tests in machine learning.
Methods: One hundred twenty elderly individuals were included in this study. The fall status, physical characteristics and medical history of individuals were investigated. Pure tone audiometry test, simple functional balance tests and sensory organization test were applied to the individuals.
Results: The machine learning model that incorporated co-morbidities, physical characteristics and functional balance tests achieved a 100 per cent accuracy in predicting fall risk. Models using only co-morbidities and physical characteristics, functional balance tests or the sensory organization test had accuracies of 87.5 per cent, 83.34 per cent and 91.66 per cent, respectively.
Conclusion: Advanced balance systems are not always necessary to assess fall risk. Instead, fall risk can be effectively determined using simple balance tests, co-morbidities, and patient characteristics in machine learning.
期刊介绍:
The Journal of Laryngology & Otology (JLO) is a leading, monthly journal containing original scientific articles and clinical records in otology, rhinology, laryngology and related specialties. Founded in 1887, JLO is absorbing reading for ENT specialists and trainees. The journal has an international outlook with contributions from around the world, relevant to all specialists in this area regardless of the country in which they practise. JLO contains main articles (original, review and historical), case reports and short reports as well as radiology, pathology or oncology in focus, a selection of abstracts, book reviews, letters to the editor, general notes and calendar, operative surgery techniques, and occasional supplements. It is fully illustrated and has become a definitive reference source in this fast-moving subject area. Published monthly an annual subscription is excellent value for money. Included in the subscription is access to the JLO interactive web site with searchable abstract database of the journal archive back to 1887.