Application of Patient Similarity in Smart Health: A Case Study in Medical Education

• Published in: Web Information Systems and Applications Vol. 11817; pp. 714 - 719
• Main Authors: Eteffa, Kalkidan Fekadu, Ansong, Samuel, Li, Chao, Sheng, Ming, Zhang, Yong, Xing, Chunxiao
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: EHR (Electronic Health Record); Medical education; Patient similarity
• ISBN: 9783030309510; 3030309517
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-30952-7_72

Patient similarity relies on computations that synthesize EHRs (Electronic Health Records) to give personalized predictions, which inform diagnoses and treatments. Given the complexities in pre-processing EHRs, representing patient data and utilizing the most suitable similarity metrics and evaluation methods, patient similarity computations are far from the era of regular use in hospitals. This paper aims to both support further patient similarity research and to inform the importance of its application in medical education. It accomplishes this by examining relevant literature that offer techniques to tackle the computational challenges and by presenting their various applications in the healthcare industry.