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Natural Language Processing clinical trials

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NCT ID: NCT04660422 Completed - Covid19 Clinical Trials

Advance Care Planning: Communicating With Outpatients for Vital Informed Decision

ACP-COVID
Start date: December 15, 2020
Phase:
Study type: Observational

This Pre-Post, open-cohort design, pragmatic trial with 150 clinicians and will evaluate the effectiveness of the use of telehealth Advanced Care Planning (ACP) Program by comparing ACP documentation among 13,000 patients over 65

NCT ID: NCT04192175 Active, not recruiting - Machine Learning Clinical Trials

Identification of Patients Admitted With COPD Exacerbations and Predicting Readmission Risk Using Machine Learning

Start date: June 1, 2019
Phase:
Study type: Observational

Patients with Chronic Obstructive Pulmonary Disease (COPD) who are admitted to hospital are at high risk of readmission. While therapies have improved and there are evidence-based guidelines to reduce readmissions, there are significant challenges to implementation including 1) identifying all patients with COPD early in admission to ensure evidence-based, high value care is provided and 2) identifying those who are at high risk of readmission in order to effectively target resources. Using machine learning and natural language processing, we want to develop models to 1) identify all patients with COPD exacerbations admitted to hospital and 2) stratify them to distinguish those who are at high risk of readmission b) How will you undertake your work? From Toronto hospitals, we will develop a very large dataset of patient admissions for all medical conditions including exacerbations of COPD from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with COPD exacerbation and identify those patients who will be at high risk of readmission within 30 days. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.