Unsupervised Machine Learning for Co/Multimorbidity Analysis

Singh, Satwant; Karkare, Swagata; Baswan, Sudhir M.; Singh, Vijendra

doi:10.5539/ijsp.v7n6p23

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…The procedure for group creation has differed depending on the objectives pursued and the kind of algorithm used. 19,20 While one approach 21 has used an agglomerative hierarchical clustering method, dependent on the similarity considered for this aggregation, another automatically establishes the groups of comorbidities searching for similarities in the clinical measures of patients. 22 An unsupervised disease clustering technique based on a multidimensional non-lineal projection (UMAP) 23 has also been used.…”

Section: Discussionmentioning

confidence: 99%

Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks

Navarro-Cerdán,

Sánchez-Gomis,

Pons

et al. 2023

Health Informatics J

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The objective was to assess risk of hospitalization and mortality of comorbidities using divisive hierarchical risk clustering to advice clinical interventions. Subjects and Methods: Data from the EHR of a general population, 3799885 adults, followed by 5 years. Model were performed using Spark and Scikit-learn and accuracy for the models was analyzed. Results: The number of models generated depends in part on the number of chronic diseases included (ex testing a sample of six diseases, a total number of 397 models for all-cause mortality and 431 models for hospitalization). The estimated models offered an ordered selection for the relevant clinical variables and their estimated risk as a group and for the individual patient in the group. Accuracy was assessed according to age, sex and the cardinality of the comorbid groups. A mobile version and dashboard were developed. Conclusion: The software developed stratified hospital admission and mortality risk in clusters of chronic diseases, and for a given patient, it could advise intensifying treatment or reallocating the patient risk.

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Section: Discussionmentioning

confidence: 99%

Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks

Navarro-Cerdán,

Sánchez-Gomis,

Pons

et al. 2023

Health Informatics J

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“…As is known, the available scientific literature regarding a single medical speciality has been already overwhelming. The situation becomes much worse when one is dealing with multi-morbid patients since clinical guidelines and algorithms are often aimed at the single condition scenario (81)(82)(83)(84)(85).…”

Section: Personalized Medicine: From Data Lakes To Patient Bedsmentioning

confidence: 99%

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García

Hernández‐Lemus

2022

Front. Med.

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A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

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