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oai:figshare.com:article/34048775 2026-10-01T17:36:36Z category_1 category_13 category_21 category_734 category_931 category_811 category_69 category_128 portal_5 item_type_1 month_year_10_2026

<p>STREAM clinical workflow.</p> Ali Namvar (9279350) Sundaresh Ram (10428439) Wassim W. Labaki (10704446) Stefanie Galban (10428445) Njira L. Lugogo (13119569) Craig J. Galban (10428469) Biophysics Genetics Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Inorganic Chemistry Hematology track physiological instability support clinical assessment provide limited insight multidimensional physiological space monitoring systems face method &# 8217 existing approaches rely distinct clinical signatures applies geometric analysis 6 %, 16 movement toward higher state trajectory representation physiological state monitoring linking state dynamics derived physiological states aware monitoring ), mortality prediction achieved evaluated stream using fold higher mortality icu stay within expected calibration error routine icu data fold higher expected state icu mortality state outliers risk states respectively ), remained within outcome prediction nearest state assigned states vital signs strong discrimination static thresholds spent less severity scores laboratory values interpretable patterns external validation excellent calibration driven framework discover data developed stream critical gap 8 hours 72 hours 002 ). <p>Schematic representation of the STREAM framework for processing new patient data. (1) Patient Data Input: laboratory values (Labs), vital signs (Vitals) are collected and processed at each time window (8h, 24h, 48h, etc.). (2) State Assignment: each patient snapshot is assigned to one of five population states (S1-S5) using Mahalanobis distance (MD) to state centroids; the state with minimum MD is selected (example shows S2 selected with MD = 1.8). (3) State Movement and Feature Importance: At each snapshot, the patient's state assignment (example: S4 → S1 → S2 → S5) and feature importance (e.g., GCS, lactate, creatinine at 48hrs) are determined. (4) Downstream Applications: STREAM outputs support three clinical functions: risk alerts based on state outlier detection, mortality prediction via XGBoost models using 34 features, and interpretability through feature importance analysis. Abbreviations: MD, Mahalanobis distance; S1-S5, data-derived physiological states 1-5; GCS, Glasgow Coma Scale; Lac, lactate; Cr, creatinine.</p> 2026-10-01T17:36:17Z Image Figure 10.1371/journal.pdig.0001753.g001 https://figshare.com/articles/figure/_p_STREAM_clinical_workflow_p_/34048775 CC BY 4.0