Network medicine for comorbidites analysis
This thesis explores the analysis of human disease comorbidity through the lens of network science. Recognizing the significance of comorbidities in human health, we emphasize the need to approach diseases as complex and interconnected entities rather than isolated conditions. This paradigm shift un...
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| Format: | master thesis |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/414803 |
| Online Access: | https://hdl.handle.net/2117/414803 |
| Access Level: | Open access |
| Keyword: | Comorbidity Inference Artificial intelligence ciència de xarxes comorbiditat inferència intel·ligència artificial network science comorbidity inference artificial intelligence Comorbiditat Inferència Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Summary: | This thesis explores the analysis of human disease comorbidity through the lens of network science. Recognizing the significance of comorbidities in human health, we emphasize the need to approach diseases as complex and interconnected entities rather than isolated conditions. This paradigm shift underscores the value of leveraging network science and artificial intelligence tools to facilitate research and analysis in this field. As medicine transitions towards a patient-centered perspective, patients are viewed as dynamic systems influenced by environmental factors, genetic information, and molecular interactions. Imbalances within these intricate relationships can contribute to disease development and subsequent comorbidities, highlighting the interconnected nature of health conditions. Our study focuses on analyzing comorbidity patterns using data extracted from the MIMIC-III dataset, encompassing patients treated at Beth Israel Deaconess Medical Center critical care unit between 2001 and 2012. We construct a bipartite network from this dataset and apply various network science techniques, including the examination of degree distribution, clustering coefficients, node centrality, and community structure analysis. To delve deeper into the community structure of the network, we employ inference methods to derive statistically and mathematically grounded insights into how the network is structured from a community perspective. This approach contributes to advancing the field by demonstrating the potential of AI techniques, particularly network science, in medical research. Overall, this thesis serves as a modest contribution to the field, showcasing the transformative impact of AI methodologies like network science on understanding disease comorbidities and fostering innovative approaches in medical research. |
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