{"title":"Knowledge graphs in psychiatric research: Potential applications and future perspectives.","authors":"Sebastian Freidel, Emanuel Schwarz","doi":"10.1111/acps.13717","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).</p><p><strong>Methods: </strong>We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as \"Psychiatric Knowledge Graphs\", \"Biomedical Knowledge Graphs\", \"Knowledge Graph Machine Learning Applications\", \"Knowledge Graph Applications for Biomedical Sciences\". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.</p><p><strong>Results: </strong>A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry.</p><p><strong>Conclusion: </strong>This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.</p>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Psychiatrica Scandinavica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/acps.13717","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).
Methods: We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.
Results: A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry.
Conclusion: This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.
背景：在精神病学研究领域，知识图谱（KGs）仍然是一种未得到充分利用的工具。在更广泛的生物医学领域，知识图谱已经是一种重要工具，主要用作知识数据库或生物医学实体之间新关系的检测。本综述旨在概述在人工智能（AI）和大型语言模型（LLM）时代，KGs 将如何促进精神病学领域的研究：我们对从计算机科学、知识工程到生物信息学等多个科学领域进行了全面的文献综述。所查阅的文献来自 PubMed、Semantic Scholar 和 Google Scholar 搜索，包括 "精神病学知识图谱"、"生物医学知识图谱"、"知识图谱机器学习应用"、"生物医学知识图谱应用 "等术语。本综述对这些出版物进行了评估，并就其与未来精神病学应用的可能相关性进行了汇总：结果：在本综述中，我们发现了大量相关研究领域的论文和知识图谱应用，但这些论文和应用尚未用于精神病学研究。我们就这些 KG 在精神病学中的应用案例向其他计算研究人员提出了详尽的建议：本综述阐述了基于 KG 的研究应用在生物医学及其他领域的用例，这些应用可能有助于阐明精神疾病的复杂生物学特性，并为开发创新性干预措施开辟新的途径。我们得出的结论是，KG 在精神病学研究中的应用机会很多，涉及生物标记物发现、患者分层和个性化医疗方法等多个应用领域。
期刊介绍：
Acta Psychiatrica Scandinavica acts as an international forum for the dissemination of information advancing the science and practice of psychiatry. In particular we focus on communicating frontline research to clinical psychiatrists and psychiatric researchers.
Acta Psychiatrica Scandinavica has traditionally been and remains a journal focusing predominantly on clinical psychiatry, but translational psychiatry is a topic of growing importance to our readers. Therefore, the journal welcomes submission of manuscripts based on both clinical- and more translational (e.g. preclinical and epidemiological) research. When preparing manuscripts based on translational studies for submission to Acta Psychiatrica Scandinavica, the authors should place emphasis on the clinical significance of the research question and the findings. Manuscripts based solely on preclinical research (e.g. animal models) are normally not considered for publication in the Journal.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
