{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:16:35Z","timestamp":1763885795177,"version":"3.45.0"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>The overuse of laboratory tests is a persistent challenge in healthcare, driving unnecessary costs, patient discomfort, and low-value care. Glucose testing, one of the most common diagnostics, exemplifies this issue in hospital settings. We present a deep learning framework that integrates structured and unstructured electronic medical record data to predict whether a glucose test will be ordered in the next AM\/PM time bin. Using multi-hospital data from the GEMINI dataset, we combine Long Short-Term Memory models with Clinical BioBERT embeddings to capture both the timing and clinical context of testing. On held-out test data, our best model achieved ROC-AUC of 0.92 and PR-AUC of 0.67, and generalized across sites in leave-one-hospital-out evaluation (ROC-AUC 0.84). Embedding-based models outperformed traditional feature representations, though adding more tests and vitals did not always yield further gains. By contrast, introducing a simple temporal recency cue (bin counter) improved performance. An exploratory regression task for predicting glucose values performed worse, likely due to class imbalance and reliance on forward-filled values; Random Forest achieved R^2 of 0.80 under masked evaluation, indicating a need for more frequent or diverse test data. Predicting laboratory test ordering is the first step toward evaluating the usefulness of laboratory test use and establishes a foundation for future real-time decision support to reduce unnecessary lab use in hospitals.<\/jats:p>","DOI":"10.1609\/aaaiss.v7i1.36924","type":"journal-article","created":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:21Z","timestamp":1763885721000},"page":"501-505","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Glucose Test Ordering in Hospitalized Patients Using Temporal Models of Clinical Context Embeddings"],"prefix":"10.1609","volume":"7","author":[{"given":"Joud","family":"El-Shawa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elham","family":"Bagheri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amol","family":"Verma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalda","family":"Mohsenzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,11,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36924\/39062","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/36924\/39062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T08:15:21Z","timestamp":1763885721000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/36924"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,11,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v7i1.36924","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,23]]}}}