Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 2 of 2
Full-Text Articles in Medicine and Health Sciences
Validation Of Accelerometer-Based Energy Expenditure Prediction Models In Structured And Simulated Free-Living Settings, Alexander H. K. Montoye, Scott A. Conger, Christopher P. Connolly, Mary T. Imboden, M. Benjamin Nelson, Josh M. Bock, Leonard A. Kaminsky
Validation Of Accelerometer-Based Energy Expenditure Prediction Models In Structured And Simulated Free-Living Settings, Alexander H. K. Montoye, Scott A. Conger, Christopher P. Connolly, Mary T. Imboden, M. Benjamin Nelson, Josh M. Bock, Leonard A. Kaminsky
Faculty Publications - Department of Kinesiology
This study compared accuracy of energy expenditure (EE) prediction models from accelerometer data collected in structured and simulated free-living settings. Twenty-four adults (mean age 45.8 years, 50% female) performed two sessions of 11 to 21 activities, wearing four ActiGraph GT9X Link activity monitors (right hip, ankle, both wrists) and a metabolic analyzer (EE criterion). Visit 1 (V1) involved structured, 5-min activities dictated by researchers; Visit 2 (V2) allowed participants activity choice and duration (simulated free-living). EE prediction models were developed incorporating data from one setting (V1/V2; V2/V2) or both settings (V1V2/V2). The V1V2/V2 method had the lowest root mean square …
A Multi-Model Approach In Developing An Intelligent Assistant For Diagnosis Recommendation In Clinical Health Systems, Christian E. Pulmano, Ma. Regina Justina E. Estuar
A Multi-Model Approach In Developing An Intelligent Assistant For Diagnosis Recommendation In Clinical Health Systems, Christian E. Pulmano, Ma. Regina Justina E. Estuar
Department of Information Systems & Computer Science Faculty Publications
Clinical health information systems capture massive amounts of unstructured data from various health and medical facilities. This study utilizes unstructured patient clinical text data to develop an intelligent assistant that can identify possible related diagnoses based on a given text input. The approach applies a one-vs-rest binary classification technique wherein given an input text data, it is identified whether it can be positively or negatively classified for a given diagnosis. Multi-layer Feed-Forward Neural Network models were developed for each individual diagnosis case. The task of the intelligent assistant is to iterate over all the different models and return those that …