PMID- 27506130
OWN - NLM
STAT- MEDLINE
DCOM- 20171117
LR  - 20181202
IS  - 1873-2860 (Electronic)
IS  - 0933-3657 (Linking)
VI  - 71
DP  - 2016 Jul
TI  - Detecting signals of detrimental prescribing cascades from social media.
PG  - 43-56
LID - S0933-3657(16)30074-4 [pii]
LID - 10.1016/j.artmed.2016.06.002 [doi]
AB  - MOTIVATION: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) 
      is misinterpreted as a new medical condition, leading to further prescriptions 
      for treatment. Additional prescriptions, however, may worsen the existing 
      condition or introduce additional adverse effects (AEs). Timely detection and 
      prevention of detrimental PCs is essential as drug AEs are among the leading 
      causes of hospitalization and deaths. Identifying detrimental PCs would enable 
      warnings and contraindications to be disseminated and assist the detection of 
      unknown drug AEs. Nonetheless, the detection is difficult and has been limited to 
      case reports or case assessment using administrative health claims data. Social 
      media is a promising source for detecting signals of detrimental PCs due to the 
      public availability of many discussions regarding treatments and drug AEs. 
      OBJECTIVE: In this paper, we investigate the feasibility of detecting detrimental 
      PCs from social media. METHODS: The detection, however, is challenging due to the 
      data uncertainty and data rarity in social media. We propose a framework to mine 
      sequences of drugs and AEs that signal detrimental PCs, taking into account the 
      data uncertainty and data rarity. RESULTS: We conduct experiments on two 
      real-world datasets collected from Twitter and Patient health forum. Our 
      framework achieves encouraging results in the validation against known 
      detrimental PCs (F1=78% for Twitter and 68% for Patient) and the detection of 
      unknown potential detrimental PCs (Precision@50=72% and NDCG@50=95% for Twitter, 
      Precision@50=86% and NDCG@50=98% for Patient). In addition, the framework is 
      efficient and scalable to large datasets. CONCLUSION: Our study demonstrates the 
      feasibility of generating hypotheses of detrimental PCs from social media to 
      reduce pharmacists' guesswork.
CI  - Copyright (c) 2016 Elsevier B.V. All rights reserved.
FAU - Hoang, Tao
AU  - Hoang T
AD  - School of Information Technology and Mathematical Sciences, University of South 
      Australia, Mawson Lakes, Adelaide, South Australia 5095, Australia. Electronic 
      address: hoatn002@mymail.unisa.edu.au.
FAU - Liu, Jixue
AU  - Liu J
AD  - School of Information Technology and Mathematical Sciences, University of South 
      Australia, Mawson Lakes, Adelaide, South Australia 5095, Australia.
FAU - Pratt, Nicole
AU  - Pratt N
AD  - School of Pharmacy and Medical Sciences, University of South Australia, City East 
      Campus, North Terrace, Adelaide, South Australia 5000, Australia.
FAU - Zheng, Vincent W
AU  - Zheng VW
AD  - Advanced Digital Sciences Center, 1 Fusionopolis Way, #08-10 Connexis North 
      Tower, Singapore 138632, Singapore.
FAU - Chang, Kevin C
AU  - Chang KC
AD  - Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N 
      Goodwin Ave, Urbana, IL 61801, United States.
FAU - Roughead, Elizabeth
AU  - Roughead E
AD  - School of Pharmacy and Medical Sciences, University of South Australia, City East 
      Campus, North Terrace, Adelaide, South Australia 5000, Australia.
FAU - Li, Jiuyong
AU  - Li J
AD  - School of Information Technology and Mathematical Sciences, University of South 
      Australia, Mawson Lakes, Adelaide, South Australia 5095, Australia.
LA  - eng
PT  - Journal Article
DEP - 20160629
PL  - Netherlands
TA  - Artif Intell Med
JT  - Artificial intelligence in medicine
JID - 8915031
SB  - IM
MH  - *Data Mining
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Humans
MH  - Pharmacists
MH  - *Social Media
OTO - NOTNLM
OT  - Adverse effect
OT  - Detrimental prescribing cascade
OT  - Drug
OT  - Existence uncertainty
OT  - Order uncertainty
OT  - Sequence mining
OT  - Social media
EDAT- 2016/08/11 06:00
MHDA- 2017/11/29 06:00
CRDT- 2016/08/11 06:00
PHST- 2016/03/02 00:00 [received]
PHST- 2016/06/02 00:00 [revised]
PHST- 2016/06/07 00:00 [accepted]
PHST- 2016/08/11 06:00 [entrez]
PHST- 2016/08/11 06:00 [pubmed]
PHST- 2017/11/29 06:00 [medline]
AID - S0933-3657(16)30074-4 [pii]
AID - 10.1016/j.artmed.2016.06.002 [doi]
PST - ppublish
SO  - Artif Intell Med. 2016 Jul;71:43-56. doi: 10.1016/j.artmed.2016.06.002. Epub 2016 
      Jun 29.