{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["3(1)"],"submitter":["Somani SS"],"pubmed_abstract":["Aims
Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection.Methods and results
We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating c"],"journal":["European heart journal. Digital health"],"pagination":["56-66"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8946569"],"repository":["biostudies-literature"],"pubmed_title":["Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening."],"pmcid":["PMC8946569"],"pubmed_authors":["Lee S","De Freitas JK","Kim A","Glicksberg BS","Jaladanki S","Teng S","Somani SS","Rehmani A","Honarvar H","Kagen AC","Zhao SP","Khachatoorian Y","Levin MA","Russak A","Freeman R","Landi I","Argulian E","Narula S","Kumar A","Nadkarni GN"],"additional_accession":[]},"is_claimable":false,"name":"Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening.","description":"Aims
Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection.Methods and results
We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating c","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-19T09:18:22.154Z","creation":"2025-04-19T09:18:22.154Z"},"accession":"S-EPMC8946569","cross_references":{"pubmed":["35355847"],"doi":["10.1093/ehjdh/ztab101"]}}