DOI: 10.1055/s-00035037

Methods of Information in Medicine

Issue 01/02 · Volume 61 · May 2022 DOI: 10.1055/s-012-54751

Editorial for Focus Theme

001
Flowerday, Stephen V.; Xenakis, Christos: Security and Privacy in Distributed Health Care Environments

Original Article

003
Urbanowicz, Ryan J.; Holmes, John H.; Appleby, Dina; Narasimhan, Vanamala; Durborow, Stephen; Al-Naamani, Nadine; Fernando, Melissa; Kawut, Steven M.: A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials
Supplementary Material
011
Dedhia, Priya H.; Chen, Kallie; Song, Yiqiang; LaRose, Eric; Imbus, Joseph R.; Peissig, Peggy L.; Mendonca, Eneida A.; Schneider, David F.: Ambiguous and Incomplete: Natural Language Processing Reveals Problematic Reporting Styles in Thyroid Ultrasound Reports
Supplementary Material
019
Schnellinger, Erin M.; Yang, Wei; Harhay, Michael O.; Kimmel, Stephen E.: A Comparison of Methods to Detect Changes in Prediction Models
Supplementary Material
029
Hegde, Harshad; Glurich, Ingrid; Panny, Aloksagar; Vedre, Jayanth G.; VanWormer, Jeffrey J.; Berg, Richard; Scannapieco, Frank A.; Miecznikowski, Jeffrey; Acharya, Amit: Identifying Pneumonia Subtypes from Electronic Health Records Using Rule-Based Algorithms
038
Panny, AlokSagar; Hegde, Harshad; Glurich, Ingrid; Scannapieco, Frank A.; Vedre, Jayanth G.; VanWormer, Jeffrey J.; Miecznikowski, Jeffrey; Acharya, Amit: A Methodological Approach to Validate Pneumonia Encounters from Radiology Reports Using Natural Language Processing
Supplementary Material
046
Kandaswamy, Swaminathan; Orenstein, Evan W.; Quincer, Elizabeth; Fernandez, Alfred J.; Gonzalez, Mark D.; Lu, Lydia; Kamaleswaran, Rishikesan; Banerjee, Imon; Jaggi, Preeti: Automated Identification of Immunocompromised Status in Critically Ill Children
Supplementary Material

Short Paper

055
Gerharz, Alexander; Ruff, Carmen; Wirbka, Lucas; Stoll, Felicitas; Haefeli, Walter E.; Groll, Andreas; Meid, Andreas D.: Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing
Supplementary Material