Artificial Intelligence for the Future of Medicine

Ruiz, Rocío B.; Velásquez, Juan D.

doi:10.1007/978-3-031-11170-9_1

Cited by 1 publication

(1 citation statement)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Along with new challenges, innovative technologies have also emerged to facility the work in health centers and hospitals. Some good examples of these widely accepted new trends are: the so-called P4 medicine, where P4 stands for Predictive, Personalized, Preventive and Participatory (Ruiz and Velásquez, 2023), the advances in clinical medicine based on artificial intelligence (Pan et al, 2022), and the increasing use of machine learning algorithms to detect and to diagnose diseases using existing patient data (Kejriwal and Rajagopalan, 2023). Engineering and Operations Management, Sydney, Australia, December 20-21, 2022 According to Kotler (2001), organizations must identify the user segments to identify specific user needs.…”

Section: Introductionmentioning

confidence: 99%

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

It is common to classify patients that arrive at an intensive care unit (ICU) by means of a classification based on gender, age and health record. However, it is interesting and helpful to take advantage of additional data to propose alternative patient segmentation that might help allocate more efficiently the existing infrastructure, supplies, medical staff. In this investigation several ICU patients' segmentations were implemented and compared. Different from a supervised task like classification, where datasets have to be a priori labelled to train and to test prediction models, clustering algorithms requires no labelling. Instead, data are grouped according to their degree of similarity. The research was carried out following a 4-phase methodology: analysis, design, development, and validation. During the analysis, a large database with record of the medical care received by patients at the ICU of a public hospital located in the south of Chile was preprocessed and analyzed. During the design, several datasets were prepared to conduct experiments. At this point, the advantages and disadvantages of different clustering algorithms were analyzed and compared, selecting Simple K-Means Algorithm (SKMA) and Expectation-Maximization Clustering (EMC) to proceed with the investigation. Whereas SKMA creates clusters of equal variance, EMC assumes a Gaussian distribution of data. The phase of development was carried out using the data mining software WEKA 3.9.6. To complete the investigation, four datasets of five, ten, fifteen, and twenty thousand ICU records were used. Since no target class was defined, the clustering was the result of applying the selected algorithms: EMC and SKMA. For both cases, different number of clusters (k) were required to establish a comparison. Results revealed clear differences in the outputs generated by each clustering algorithm. For instance, with 5 clusters (k=5) EMC distributes data in the following proportions: 19%, 15%, 10%, 43%, and 28%. With SKMA, instead, the proportion were: 44%, 13%, 10%m 9%, and 24%. In conclusion, the investigation showed that popular clustering algorithms such as EMC and SKMA can be used for segmenting not only consumers but also ICU patients according to criteria that are not easy to visualize with classical tools and techniques. An adequate segmentation can provide valuable information to help estimate the requirement of medical staff, supplies and infrastructure, and also to define specific healthcare services.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

Proceedings of the International Conference on Industrial Engineering and Operations Management

View full text Add to dashboard Cite

show abstract

Artificial Intelligence for the Future of Medicine

Cited by 1 publication

References 107 publications

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

Comparing Segmentations Based on Clustering Algorithms to Classify Patients of Intensive Care Units

Contact Info

Product

Resources

About