A data-driven approach for analyzing healthcare services extracted from clinical records

authored by: Manuel Scurti, Ernestina Menasalvas Ruiz, Maria Esther Vidal, Maria Torrente, Dimitrios Vogiatzis, George Paliouras, Mariano Provencio, Alejandro Rodriguez Gonzalez
Abstract: Cancer remains one of the major public health challenges worldwide. After cardiovascular diseases, cancer is one of the first causes of death and morbidity in Europe, with more than 4 million new cases and 1.9 million deaths per year. The suboptimal management of cancer patients during treatment and subsequent follows up are major obstacles in achieving better outcomes of the patients and especially regarding cost and quality of life In this paper, we present an initial data-driven approach to analyze the resources and services that are used more frequently by lung-cancer patients with the aim of identifying where the care process can be improved by paying a special attention on services before diagnosis to being able to identify possible lung-cancer patients before they are diagnosed and by reducing the length of stay in the hospital. Our approach has been built by analyzing the clinical notes of those oncological patients to extract this information and their relationships with other variables of the patient. Although the approach shown in this manuscript is very preliminary, it shows that quite interesting outcomes can be derived from further analysis.
External Organisation(s): Technical University of Madrid (UPM)
German National Library of Science and Technology (TIB)
Universidad Autónoma de Madrid
National Centre For Scientific Research Demokritos (NCSR Demokritos)
The American College of Greece (ACG)
Type: Conference contribution
Pages: 193-196
No. of pages: 4
Publication date: 2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Radiology Nuclear Medicine and imaging, Computer Science Applications
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://zenodo.org/record/3862244/files/Paper%20CBMS%20BigMed%20v0.2.pdf (Access: Open)
https://doi.org/10.1109/CBMS49503.2020.00044 (Access: Closed)