{"title":"绘制康复之路:通过图表理论探索创伤性脑损伤并发症--探索益处与挑战","authors":"Shyam Kumar Sudhakar, Kaustav Mehta","doi":"10.1016/j.bosn.2024.03.002","DOIUrl":null,"url":null,"abstract":"<div><p>Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a debilitating effect on the well-being of the affected individual. TBIs are associated with a multitude of psychiatric and medical comorbidities over the long term. Furthermore, no medications prevent secondary injuries associated with a primary insult. In this perspective article, we propose applying graph theory via the construction of disease comorbidity networks to identify high-risk patient groups, offer preventive care to affected populations, and reduce the disease burden. We describe the challenges associated with monitoring the development of comorbidities in TBI subjects and explain how disease comorbidity networks can reduce disease burden by preventing disease-related complications. We further discuss the various methods used to construct disease comorbidity networks and explain how features derived from a network can help identify subjects who might be at risk of developing post-traumatic comorbidities. Lastly, we address the potential challenges of using graph theory to successfully manage comorbidities following a TBI.</p></div>","PeriodicalId":100198,"journal":{"name":"Brain Organoid and Systems Neuroscience Journal","volume":"2 ","pages":"Pages 10-16"},"PeriodicalIF":0.0000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949921624000024/pdfft?md5=9b493993309927962a8ce75a9a1bf518&pid=1-s2.0-S2949921624000024-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Charting paths to recovery: Navigating traumatic brain injury comorbidities through graph theory–exploring benefits and challenges\",\"authors\":\"Shyam Kumar Sudhakar, Kaustav Mehta\",\"doi\":\"10.1016/j.bosn.2024.03.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a debilitating effect on the well-being of the affected individual. TBIs are associated with a multitude of psychiatric and medical comorbidities over the long term. Furthermore, no medications prevent secondary injuries associated with a primary insult. In this perspective article, we propose applying graph theory via the construction of disease comorbidity networks to identify high-risk patient groups, offer preventive care to affected populations, and reduce the disease burden. We describe the challenges associated with monitoring the development of comorbidities in TBI subjects and explain how disease comorbidity networks can reduce disease burden by preventing disease-related complications. We further discuss the various methods used to construct disease comorbidity networks and explain how features derived from a network can help identify subjects who might be at risk of developing post-traumatic comorbidities. Lastly, we address the potential challenges of using graph theory to successfully manage comorbidities following a TBI.</p></div>\",\"PeriodicalId\":100198,\"journal\":{\"name\":\"Brain Organoid and Systems Neuroscience Journal\",\"volume\":\"2 \",\"pages\":\"Pages 10-16\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2949921624000024/pdfft?md5=9b493993309927962a8ce75a9a1bf518&pid=1-s2.0-S2949921624000024-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Organoid and Systems Neuroscience Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949921624000024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Organoid and Systems Neuroscience Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949921624000024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Charting paths to recovery: Navigating traumatic brain injury comorbidities through graph theory–exploring benefits and challenges
Traumatic brain injuries (TBIs) are characterized by widespread complications that exert a debilitating effect on the well-being of the affected individual. TBIs are associated with a multitude of psychiatric and medical comorbidities over the long term. Furthermore, no medications prevent secondary injuries associated with a primary insult. In this perspective article, we propose applying graph theory via the construction of disease comorbidity networks to identify high-risk patient groups, offer preventive care to affected populations, and reduce the disease burden. We describe the challenges associated with monitoring the development of comorbidities in TBI subjects and explain how disease comorbidity networks can reduce disease burden by preventing disease-related complications. We further discuss the various methods used to construct disease comorbidity networks and explain how features derived from a network can help identify subjects who might be at risk of developing post-traumatic comorbidities. Lastly, we address the potential challenges of using graph theory to successfully manage comorbidities following a TBI.