{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rotem RS"],"funding":["National Institute of Environmental Health Sciences","NIEHS NIH HHS","NINDS NIH HHS"],"pagination":["367-375"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11075178"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(3-4)"],"pubmed_abstract":["Background
Accumulating evidence suggests that non-genetic factors have important etiologic roles in amyotrophic lateral sclerosis (ALS), yet identification of specific culprit factors has been challenging. Many medications target biological pathways implicated in ALS pathogenesis, and screening large pharmacologic datasets for signals could greatly accelerate the identification of risk-modulating pharmacologic factors for ALS.Method
We conducted a high-dimensional screening of patients' history of medication use and ALS risk using an advanced machine learning approach based on gradient-boosted decision trees coupled with Bayesian model optimization and repeated data sampling. Clinical and medication dispensing data were obtained from a large Israeli health fund for 501 ALS"],"journal":["Amyotrophic lateral sclerosis & frontotemporal degeneration"],"pubmed_title":["Medication use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database."],"pmcid":["PMC11075178"],"funding_grant_id":["P30 ES000002","R21 NS099910"],"pubmed_authors":["Paganoni S","Rotem RS","Bellavia A","Weisskopf MG"],"additional_accession":[]},"is_claimable":false,"name":"Medication use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database.","description":"Background
Accumulating evidence suggests that non-genetic factors have important etiologic roles in amyotrophic lateral sclerosis (ALS), yet identification of specific culprit factors has been challenging. Many medications target biological pathways implicated in ALS pathogenesis, and screening large pharmacologic datasets for signals could greatly accelerate the identification of risk-modulating pharmacologic factors for ALS.Method
We conducted a high-dimensional screening of patients' history of medication use and ALS risk using an advanced machine learning approach based on gradient-boosted decision trees coupled with Bayesian model optimization and repeated data sampling. Clinical and medication dispensing data were obtained from a large Israeli health fund for 501 ALS","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 May","modification":"2025-07-12T03:04:14.039Z","creation":"2025-07-12T03:04:14.039Z"},"accession":"S-EPMC11075178","cross_references":{"pubmed":["38426489"],"doi":["10.1080/21678421.2024.2320878"]}}