Felix C Oettl, Jacob F Oeding, Robert Feldt, Christophe Ley, Michael T Hirschmann, Kristian Samuelsson
{"title":"The artificial intelligence advantage: Supercharging exploratory data analysis.","authors":"Felix C Oettl, Jacob F Oeding, Robert Feldt, Christophe Ley, Michael T Hirschmann, Kristian Samuelsson","doi":"10.1002/ksa.12389","DOIUrl":null,"url":null,"abstract":"<p><p>Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.</p>","PeriodicalId":17880,"journal":{"name":"Knee Surgery, Sports Traumatology, Arthroscopy","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knee Surgery, Sports Traumatology, Arthroscopy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/ksa.12389","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
Abstract
Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.
期刊介绍:
Few other areas of orthopedic surgery and traumatology have undergone such a dramatic evolution in the last 10 years as knee surgery, arthroscopy and sports traumatology. Ranked among the top 33% of journals in both Orthopedics and Sports Sciences, the goal of this European journal is to publish papers about innovative knee surgery, sports trauma surgery and arthroscopy. Each issue features a series of peer-reviewed articles that deal with diagnosis and management and with basic research. Each issue also contains at least one review article about an important clinical problem. Case presentations or short notes about technical innovations are also accepted for publication.
The articles cover all aspects of knee surgery and all types of sports trauma; in addition, epidemiology, diagnosis, treatment and prevention, and all types of arthroscopy (not only the knee but also the shoulder, elbow, wrist, hip, ankle, etc.) are addressed. Articles on new diagnostic techniques such as MRI and ultrasound and high-quality articles about the biomechanics of joints, muscles and tendons are included. Although this is largely a clinical journal, it is also open to basic research with clinical relevance.
Because the journal is supported by a distinguished European Editorial Board, assisted by an international Advisory Board, you can be assured that the journal maintains the highest standards.
Official Clinical Journal of the European Society of Sports Traumatology, Knee Surgery and Arthroscopy (ESSKA).