| Autor: |
Klang E; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Kummer BR; Department of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1137, New York, NY, USA. benjamin.kummer@mountsinai.org.; Clinical Informatics, Mount Sinai Health System, New York, NY, USA. benjamin.kummer@mountsinai.org., Dangayach NS; Department of Neurology, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, Box 1137, New York, NY, USA.; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Zhong A; Icahn School of Medicine at Mount Sinai, New York, NY, USA., Kia MA; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Timsina P; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Cossentino I; Icahn School of Medicine at Mount Sinai, New York, NY, USA., Costa AB; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Levin MA; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Oermann EK; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA. |
| Abstrakt: |
Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200-256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80-324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87-0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91-0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92-0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings. |
