PMID- 31933254
OWN - NLM
STAT- MEDLINE
DCOM- 20200427
LR  - 20210110
IS  - 1179-1993 (Electronic)
IS  - 1178-2595 (Linking)
VI  - 33
IP  - 2
DP  - 2019 Apr
TI  - Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive 
      Services and the Framework for Their Validation.
PG  - 109-120
LID - 10.1007/s40290-019-00269-0 [doi]
AB  - INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse 
      events (AEs) and other drug-related problems by collecting, evaluating, and 
      acting upon AEs. The volume of individual case safety reports (ICSRs) increases 
      yearly, but it is estimated that more than 90% of AEs go unreported. In this 
      landscape, embracing assistive technologies at scale becomes necessary to obtain 
      a higher yield of AEs, to maintain compliance, and transform the PV professional 
      work life. AIM: The aim of this study was to identify areas across the PV value 
      chain that can be augmented by cognitive service solutions using the 
      methodologies of contextual analysis and cognitive load theory. It will also 
      provide a framework of how to validate these PV cognitive services leveraging the 
      acceptable quality limit approach. METHODS: The data used to train the cognitive 
      service were an annotated corpus consisting of 20,000 ICSRS from which we 
      developed a framework to identify and validate 40 cognitive services ranging from 
      information extraction to complex decision making. This framework addresses the 
      following shortcomings: (1) needing subject-matter expertise (SME) to match the 
      artificial intelligence (AI) model predictions to the gold standard, commonly 
      referred to as 'ground truth' in the AI space, (2) ground truth inconsistencies, 
      (3) automated validation of prediction missing context, and (4) auto-labeling 
      causing inaccurate test accuracy. The method consists of (1) conducting 
      contextual analysis, (2) assessing human cognitive workload, (3) determining 
      decision points for applying artificial intelligence (AI), (4) defining the scope 
      of the data, or annotated corpus required for training and validation of the 
      cognitive services, (5) identifying and standardizing PV knowledge elements, (6) 
      developing cognitive services, and (7) reviewing and validating cognitive 
      services. RESULTS: By applying the framework, we (1) identified 51 decision 
      points as candidates for AI use, (2) standardized the process to make PV 
      knowledge explicit, (3) embedded SMEs in the process to preserve PV knowledge and 
      context, (4) standardized acceptability by using established quality inspection 
      principles, and (5) validated a total of 126 cognitive services. CONCLUSION: The 
      value of using AI methodologies in PV is compelling; however, as PV is highly 
      regulated, acceptability will require assurances of quality, consistency, and 
      standardization. We are proposing a foundational framework that the industry can 
      use to identify and validate services to better support the gathering of quality 
      data and to better serve the PV professional.
FAU - Mockute, Ruta
AU  - Mockute R
AUID- ORCID: 0000-0002-0947-8014
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA. 
      Rmockute@celgene.com.
FAU - Desai, Sameen
AU  - Desai S
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Perera, Sujan
AU  - Perera S
AD  - IBM Watson Health, Almaden Research Center, San Jose, CA, USA.
FAU - Assuncao, Bruno
AU  - Assuncao B
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Danysz, Karolina
AU  - Danysz K
AD  - Celgene Corporation, Boudry, Switzerland.
FAU - Tetarenko, Niki
AU  - Tetarenko N
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Gaddam, Darpan
AU  - Gaddam D
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Abatemarco, Danielle
AU  - Abatemarco D
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Widdowson, Mark
AU  - Widdowson M
AD  - Celgene Corporation, Stockley Park, UK.
FAU - Beauchamp, Sheryl
AU  - Beauchamp S
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
FAU - Cicirello, Salvatore
AU  - Cicirello S
AD  - Celgene Corporation, Boudry, Switzerland.
FAU - Mingle, Edward
AU  - Mingle E
AD  - Celgene Corporation, 86 Morris Avenue, Summit, NJ, 07901, USA.
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - New Zealand
TA  - Pharmaceut Med
JT  - Pharmaceutical medicine
JID - 101471195
SB  - IM
MH  - Adverse Drug Reaction Reporting Systems/*instrumentation
MH  - Algorithms
MH  - Artificial Intelligence/*trends
MH  - Cognition/*physiology
MH  - Databases, Factual
MH  - Decision Making/physiology
MH  - Drug-Related Side Effects and Adverse Reactions/*prevention & control
MH  - Guideline Adherence/statistics & numerical data
MH  - Humans
MH  - Machine Learning
MH  - Patient Safety/standards
MH  - Pharmacovigilance
MH  - Workload/statistics & numerical data
EDAT- 2020/01/15 06:00
MHDA- 2020/04/28 06:00
CRDT- 2020/01/15 06:00
PHST- 2020/01/15 06:00 [entrez]
PHST- 2020/01/15 06:00 [pubmed]
PHST- 2020/04/28 06:00 [medline]
AID - 10.1007/s40290-019-00269-0 [pii]
AID - 10.1007/s40290-019-00269-0 [doi]
PST - ppublish
SO  - Pharmaceut Med. 2019 Apr;33(2):109-120. doi: 10.1007/s40290-019-00269-0.