The Challenge of Building a Bayesian Homeopathic Materia Medica and Repertory from Best Cases' Data

Abstract

Background

It is essential to homeopathy that medicines' indicating symptoms and signs are accurately determined. In recent times, it has become apparent that the traditional assessment of indicating symptoms based on their absolute occurrence criteria (attributing a symptom to a medicine when it has been observed in several cases of responders) is flawed. A Bayesian approach using various study methods has been increasingly applied in this field of research.

Objective

In this publication, the feasibility of building a homeopathic Materia Medica and a Repertory based on best cases' data is explored.

Methods

Cases' data were extracted from the ‘Best Chronic Homeopathic Cases’ database. A common unification language was agreed, with clearly defined inclusion criteria of symptoms and medicines for analysis. Count, prevalence, likelihood ratio (LR), and cumulative binomial probability were calculated for each symptom–medicine combination, with adjustment for outliers. A Nat-m Bayesian Materia Medica was developed using defined criteria of statistical significance for validating the indicating symptoms. A Bayesian Homeopathic Repertory application (app) was developed.

Results

A total of 794 cases, 2,663 symptoms and 101 different medicines were extracted from the database. A total of 794 cases, 587 symptoms and 41 medicines were included for analysis, resulting in a matrix of 24,067 LRs. A total of 102 Nat-m symptoms were validated as indicating the medicine and 8 symptoms as contraindicating it.

Conclusion

It is possible to detect and validate accurately the indicating symptoms of homeopathic medicines with a Bayesian assessment of data from the best clinical cases. It is also possible to develop a Bayesian Homeopathic Repertory and a Bayesian Homeopathic Materia Medica from these data. Skilled homeopaths with different cultural backgrounds, experience and preferences are encouraged to engage in this path of research.

Keywords

Bayesian homeopathic repertory - Bayesian homeopathic Materia Medica - Natrum Muriaticum - best homeopathic cases

Publication History

Received: 08 April 2025

Accepted: 11 July 2025

Article published online:
14 October 2025

• References

• 1 Hahnemann S. Organon of medicine. 6th ed. paragraph 213. Accessed at: http://homeoint.org/books/hahorgan/index.htm
• 2 Kent JT. Repertory of the Homoeopathic Materia Medica. 6th ed.. Chicago, Illinois, United States: Ehrhart & Karl; 1957
• 3 Van Zandvoort R. Complete Repertory 2013. MacRepertoy Software. [no longer available]
• 4 Stolper CF, Rutten ALB, Lugten RFG, Barthels RJ. Improving homeopathic prescribing by applying epidemiological techniques: the role of likelihood ratio. Homeopathy 2002; 91: 230-238
• 5 Rutten ALB, Stolper CF, Lugten RFG, Barthels RW. Statistical analysis of six repertory rubrics after prospective assessment applying Bayes' theorem. Homeopathy 2009; 98: 26-34
• 6 Eizayaga JE, Pozzi MI, Canan MC, Saravia L. Prevalence and likelihood ratio of symptoms in patients with good therapeutic response to Lycopodium clavatum. A retrospective study. Homeopathy 2016; 105: 78-83
• 7 Eizayaga JE, Waisse S, Rutten L, Klein-Laansma C, Motura A. Prevalence and likelihood ratio of six objective signs among good responders to Natrum muriaticum: multi-centre observational assessment. Homeopathy 2021; 110: 36-41
• 8 Rutten L, Smedley T, Ives G. et al. Data collection during the COVID-19 pandemic: learning from experience, resulting in a Bayesian repertory. Homeopathy 2021; 110: 94-101
• 9 Rutten L, Eizayaga JE, Kaur H. et al. How to assess variation in homeopathic prognostic factor research? Homeopathy 2025 https://www.doi.org/10.1055/a-2544-2807
• 10 Rutten L, Schäferkordt R, Eizayaga JE. . Updating the homeopathic algorithms: handling confirmation bias. Homeopathy 2025 https://www.doi.org/10.1055/a-2606-4041
• 11 Medical Research Council. MRC Ethics Series: Using information about people in health research. Version 2.0; June 2018