{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yew PY"],"funding":["NIA NIH HHS","the NIA-funded ADRCs"],"pagination":["4818-4827"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11247699"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(7)"],"pubmed_abstract":["<h4>Introduction</h4>Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.<h4>Methods</h4>We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.<h4>Results</h4>Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictiv"],"journal":["Alzheimer's & dementia : the journal of the Alzheimer's Association"],"pubmed_title":["Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions."],"pmcid":["PMC11247699"],"funding_grant_id":["P30 AG062677","P30 AG066514","P30 AG066515","P30 AG066518","P30 AG066519","P30 AG062715","P20 AG068053","P30 AG066462","P30 AG072931","P30 AG072975","P30 AG072976","P20 AG068077","P30 AG072977","P30 AG066444","P30 AG072972","P30 AG066468","P30 AG072973","P30 AG072978","P30 AG072979","R01 AG079280","P30 AG072958","P30 AG072959","P30 AG079280","P30 AG062421","P30 AG062422","P30 AG066546","P30 AG066506","P30 AG066507","P30 AG062429","P30 AG066508","P30 AG066509","P30 AG066530","P20 AG068082","P30 AG066511","P30 AG066512","U24 AG072122","P20 AG068024","P30 AG072946","P30 AG072947"],"pubmed_authors":["Devera R","Khalifa RAE","Chou YC","Liang Y","Yew PY","Chi CL","Tonellato PJ","Sun J","Chi NC"],"additional_accession":[]},"is_claimable":false,"name":"Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.","description":"<h4>Introduction</h4>Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.<h4>Methods</h4>We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.<h4>Results</h4>Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictiv","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-04-25T19:21:06.259Z","creation":"2025-04-06T07:54:02.954Z"},"accession":"S-EPMC11247699","cross_references":{"pubmed":["38859733"],"doi":["10.1002/alz.13923"]}}