Clustering applications in healthcare domain

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, vvajratiya@gmail.com

Using particular qualities or attributes, similar data points are grouped into segments or clusters through the machine learning approach of clustering. Clustering can be used in a variety of contexts within the healthcare industry to extract insightful information, facilitate better decision-making, and optimise patient outcomes. The following are some examples of how clustering is used in healthcare.

1. illness Classification

Based on shared illness patterns, symptoms, or genetic traits, clustering can be used to divide patients into several categories. This can help with more specialised and focused therapy methods [1].

2. Patient Segmentation

Clustering facilitates the division of the patient population according to characteristics such as health problems, medical history, and demography. This enables medical professionals to customise treatments and treatment programs for certain patient populations [2].

3. Drug Discovery

Researchers can find novel drug candidates and better understand patient reactions to drugs by using clustering to find patterns in drug responses [3].

4. Health Risk Assessment

Based on variables including lifestyle, genetics, and medical history, clustering algorithms can help identify high-risk and low-risk patient groupings. Early intervention techniques and preventative treatment can benefit from this knowledge [4].

5. Resource Optimization

Hospitals can make the best use of clustering to distribute staff, supplies, and facilities. Healthcare facilities can increase productivity and decrease wait times by analyzing patient demand trends [5].

6. Genomic Clustering

In the field of genomics, people with comparable genetic profiles can be grouped using the clustering technique. Understanding genetic predispositions to specific diseases and developing individualised treatment regimens can benefit from this [6].

7. Mental Health Assessment

Behavioural and psychological data can be analysed using clustering to find trends associated with mental health issues. Planning individualised treatments and early detection can benefit from this [7].

Clustering has been applied in the healthcare industry, showcasing its ability to extract valuable insights from complex data, leading to better patient care and more informed decision-making.

References

1. Bhatt, C. M., Patel, P., Ghetia, T., & Mazzeo, P. L. (2023). Effective heart disease prediction using machine learning techniques. Algorithms, 16(2), 88.
2. Liu, P., Wang, Z., Liu, N., & Peres, M. A. (2023). A scoping review of the clinical application of machine learning in data-driven population segmentation analysis. Journal of the American Medical Informatics Association, 30(9), 1573-1582.
3. Chen, W., Liu, X., Zhang, S., & Chen, S. (2023). Artificial intelligence for drug discovery: Resources, methods, and applications. Molecular Therapy-Nucleic Acids.
4. Momahhed, S. S., Emamgholipour Sefiddashti, S., Minaei, B., & Shahali, Z. (2023). K-means clustering of outpatient prescription claims for health insureds in Iran. BMC Public Health, 23(1), 1-15.
5. Nuryanti, L., Suseno, J. E., & Wibowo, A. (2023, May). Human resources planning using K-means clustering and Tabu search algorithm for workload balancing. In AIP Conference Proceedings (Vol. 2683, No. 1). AIP Publishing.
6. Pandey, K. K., & Shukla, D. (2023). Min max kurtosis distance based improved initial centroid selection approach of K-means clustering for big data mining on gene expression data. Evolving Systems, 14(2), 207-244.
7. Booth, F., Potts, C., Bond, R., Mulvenna, M., Kostenius, C., Dhanapala, I., … & Ennis, E. (2023). A Mental Health and Well-Being Chatbot: User Event Log Analysis. JMIR mHealth and uHealth, 11, e43052.
8. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-43.
9. Gupta, B. B., Perez, G. M., Agrawal, D. P., & Gupta, D. (2020). Handbook of computer networks and cyber security. Springer, 10, 978-3.
10. Zhang, Q., Guo, Z., Zhu, Y., Vijayakumar, P., Castiglione, A., & Gupta, B. B. (2023). A deep learning-based fast fake news detection model for cyber-physical social services. Pattern Recognition Letters, 168, 31-38.
11. Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022). An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Transactions on Industrial Informatics, 18(11), 7946-7954.
12. Liu, R. W., Guo, Y., Lu, Y., Chui, K. T., & Gupta, B. B. (2022). Deep network-enabled haze visibility enhancement for visual IoT-driven intelligent transportation systems. IEEE Transactions on Industrial Informatics, 19(2), 1581-1591.
13. Lu, J., Shen, J., Vijayakumar, P., & Gupta, B. B. (2021). Blockchain-based secure data storage protocol for sensors in the industrial internet of things. IEEE Transactions on Industrial Informatics, 18(8), 5422-5431.

Cite As:

Vajrobol V. (2024) Clustering applications in healthcare domain, Insights2Techinfo, pp.1

671500cookie-checkClustering applications in healthcare domainyes