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. 2020 Nov 17;15(11):e0242197.
doi: 10.1371/journal.pone.0242197. eCollection 2020.

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

Paolo Giordani  1 , Serena Perna  2 , Annamaria Bianchi  3 , Antonio Pizzulli  2   4 , Salvatore Tripodi  5 , Paolo Maria Matricardi  2
Affiliations

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

Paolo Giordani et al. PLoS One. .

Abstract

The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.

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Conflict of interest statement

We state that TPS (Technology Projects & Software) Production srl has patented in Italy (International registration number: PCT / IT2018 / 000119) and it is undergoing the registration process at the European Patent Office with the number: 18804719.5 - 1126). AllergyMonitor is an online service developed by TPS Production. It is a free downloadable app for users with the aim of enabling the recording of clinical symptoms, drug consumption and adherence to specific sublingual and subcutaneous immunotherapy (SLIT, SCIT) by patients suffering from allergic diseases such as rhino-conjunctivitis and asthma. The AllergyMonitor system consists of two parts: the web site for the patient (front end) and the website for the doctor (back office). The AllergyMonitor back office was used only for clinical studies. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Salvatore Tripodi is a co-founder of TPS. Paolo Giordani, Serena Perna, Annamaria Bianchi, Antonio Pizzulli and Paolo Maria Matricardi declare that they have no significant competing financial, professional, or personal interests that might have influenced the performance or presentation of the study described in this manuscript.

Figures

Fig 1
Fig 1. Plot of the FkMedFD solution and of the pollen (red functional).
Cyan and grey functionals identify patients assigned to Cluster 1 (medoid in blue) and Cluster 2 (medoid in black), respectively. Solid, dashed and dotted functionals denote membership degrees higher than 0.90, between 0.70 and 0.90 and between 0.50 and 0.70, respectively.
Fig 2
Fig 2. Plot of the solutions of the methods reported in Table 4 and of the pollen (red functional).
Cyan and grey functionals identify patients assigned to Cluster 1 (medoid in blue) and Cluster 2 (medoid in black), respectively. Solid, dashed and dotted functionals denote membership degrees higher than 0.90, between 0.70 and 0.90 and between 0.50 and 0.70, respectively.
Fig 3
Fig 3. Plot of the FkMedFD solution and of the pollens (red, violet and brown functionals).
Cyan, grey and green functionals identify patients assigned to Cluster 1 (medoid in blue), Cluster 2 (medoid in black) and Cluster 3 (medoid in dark green), respectively. Solid, dashed and dotted functionals denote membership degrees higher than 0.90, between 0.70 and 0.90 and between 0.50 and 0.70, respectively.
Fig 4
Fig 4. Plot of the solutions of the methods reported in Table 4 and of the pollens (red, violet and brown functionals).
Cyan, grey and green functionals identify patients assigned to Cluster 1 (medoid in blue), Cluster 2 (medoid in black) and Cluster 3 (medoid in dark green), respectively. Solid, dashed and dotted functionals denote membership degrees higher than 0.90, between 0.70 and 0.90 and between 0.50 and 0.70, respectively.
See this image and copyright information in PMC

References

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