Abstract
Background In the medical sphere, understanding naming conventions strengthen the integrity of naming human diseases remains nominal rather than substantial yet. Since the current nosology-based standard for human diseases could not offer a one-size-fits-all corrective mechanism, many idiomatic but flawed names frequently appear in scientific literature and news outlets at the cost of sociocultural impacts.
Objective We attempt to examine the ethical oversights of current naming practices and propose heuristic rationales and approaches to determine a pithy name instead of an inopportune nosology.
Methods First, we examined the compiled global online news volumes and emotional tones on some inopportune nosology like German measles, Middle Eastern Respiratory Syndrome, Spanish flu, Hong Kong flu, and Huntington’s disease in the wake of COVID-19. Second, we prototypically scrutinize the lexical dynamics and pathological differentials of German measles and common synonyms by leveraging the capacity of the Google Books Ngram Corpus. Third, we demonstrated the empirical approaches to curate an exclusive substitute for an anachronistic nosology German measles based on deep learning models and post-hoc explanations.
Results The infodemiological study shows that the public informed the offensive names with extremely negative tones in textual and visual narratives. The findings of the historiographical study indicate that many synonyms of German measles did not survive, while German measles became an anachronistic usage, and rubella has taken the dominant place since 1994. The PubMedBERT model could identify rubella as a potential substitution for German measles with the highest semantic similarity. The results of the semantic drift experiments further indicate that rubella tends to survive during the ebb and flow of semantic drift.
Conclusions Our findings indicate that the nosological evolution of anachronistic names could result in sociocultural impacts without a corrective mechanism. To mitigate such impacts, we introduce some ethical principles for formulating an improved naming scheme. Based on deep learning models and post-hoc explanations, our illustrated experiments could provide hallmark references to the remedial mechanism of naming practices and pertinent credit allocations.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Ethics approval and consent to participate: Not applicable.
Competing interests: The authors declare no potential conflicts of interest.
Data availability: The synthetic data generated in this study and custom code supporting this study are available at GitHub (https://github.com/YaChen8/Naming_human_disease).
Abbreviations
- BERT
- Bidirectional Encoder Representations from Transformers
- COVID-19
- Coronavirus Disease 2019
- GBNC
- Google Books Ngram Corpus
- GDELT
- Global Data on Events, Location and Tone
- ICD
- International Classification of Diseases
- ICD-11
- Eleventh revision of the International Classification of Diseases
- MERS
- Middle Eastern Respiratory Syndrome
- OED Online
- Oxford English Dictionary Online
- PCA
- Principal Component Analysis
- PPMI
- Positive Pointwise Mutual Information
- SARS
- Severe Acute Respiratory Syndrome
- SVD
- Singular Value Decomposition
- WHA
- World Health Assembly
- WHO
- World Health Organization