Prediction via Similarity: Biomedical Big Data and the Case of Cancer Models

被引：0

作者：

Boniolo F. ^{[1
]}

Boniolo G. ^{[2
]}

Valente G. ^{[3
]}

机构：

[1] Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, 02215, MA

[2] Dipartimento di Neuroscienze e Riabilitazione, Università di Ferrara, Ferrara

[3] Dipartimento di Matematica, Politecnico di Milano, Milan

来源：

Philosophy & Technology | 2023年 / 36卷 / 1期

关键词：

Biomedical Big Data; Cancer; Distance; Models; Prediction; Similarity;

D O I：

10.1007/s13347-023-00608-9

中图分类号：

学科分类号：

摘要：

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to draw predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. © 2023, The Author(s).

引用

共 46 条

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