8 papers:
KDD-2015-FeldmanNPR #mining #online #predict- Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions (RF, ON, AP, BR), pp. 1779–1788.
- KDD-2015-LakkarajuASMBGA #framework #identification #machine learning #student
- A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes (HL, EA, CS, DM, NB, RG, KLA), pp. 1909–1918.
ISSTA-2015-AdamsenMM #android #execution #testing- Systematic execution of Android test suites in adverse conditions (CQA, GM, AM), pp. 83–93.
KDIR-2014-DuvalCCS #mining #twitter- Mining for Adverse Drug Events on Twitter (FD, EC, OGC, FS), pp. 354–359.
ECIR-2013-YatesG #detection #named #social #social media- ADRTrace: Detecting Expected and Unexpected Adverse Drug Reactions from User Reviews on Social Media Sites (AY, NG), pp. 816–819.
- KDD-2013-HarpazDLS #empirical
- Empirical bayes model to combine signals of adverse drug reactions (RH, WD, PL, NHS), pp. 1339–1347.
ICML-2012-DavisCBPPC #clustering #predict #relational- Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events (JD, VSC, EB, DP, PLP, MC), p. 172.
ICEIS-v2-2004-SilvaCSGN #multi #network #using- Multiple Organ Failure Diagnosis Using Adverse Events and Neural Networks (ÁMS, PC, MFS, LG, JN), pp. 401–408.
