Anne F Weidinger, Ricarda Steinmayr, Birgit Spinath
Math grades and intrinsic motivation in elementary school: A longitudinal investigation of their association
The British journal of educational psychology
evaluated methods preparing electronic health record data reduce bias applying artificial intelligence ai created methods transforming raw data data framework applying machine learning natural language processing techniques predicting falls fractures strategies inclusion reporting multiple races mixed data sources outpatient inpatient structured codes unstructured notes addressing missingness applied raw data promote reduction bias raw data carefully curated using validated definitions create data variables age race gender healthcare utilization formation variables clinical statistical data expertise used research team included variety experts diverse professional demographic backgrounds include diverse perspectives prediction falls information extracted radiology reports converted matrix applying machine learning processing data resulted input 5377673 reports machine learning algorithm 45304 flagged positive 5332369 negative falls processed data resulted lower missingness better representation race diagnosis codes fractures specialized algorithms extracted snippets text around keywork femoral dual xray absorptiometry dxa scans identify femoral neck tscores important predicting fracture risk natural language processing algorithms yielded 98 accuracy 2 error rate methods prepare data input artificial intelligence processes reproducible applied studies life cycle data raw analytic form includes data governance cleaning management analysis applying artificial intelligence methods input data must prepared optimally reduce algorithmic bias biased output harmful building aiready data frameworks improve efficiency contribute transparency reproducibility roadmap application ai involves applying specialized techniques input data suggested study highlights data curation aspects considered preparing data application artificial intelligence reduce bias