Machine Learning Applications in Pharmacovigilance: Scoping Review

Background: Pharmacovigilance (PV) is the activity to identify comprehensive information on the safety characteristics of the drug after its marketing. The PV data sources are dynamic, large, structured, and unstructured; therefore, the automation of data processing is essential. Purpose: This review aims to identify the machine learning applications in PV activities. Methods: Nine (9) studies that were published within the period from 2016 to 2020 were reviewed. The studies were extracted from two databases; PubMed and web of science. The review and analysis were done in December 2020. Results: The supervised and semi-supervised learning techniques are applied in the main three PV group activities; adverse drug reactions (ADRs) and signal detection, individual case safety reports (ICSRs) identification, and ADRs prediction. Future research is needed to identify the applicability of unsupervised learning in PV and to formulate the legal framework of the false positive predicted data

Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching

BACKGROUND: Knowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available. OBJECTIVE: The aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN). METHODS: Dutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype. RESULTS: The ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline. CONCLUSIONS: The ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs

Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

An Examination Of Clinical Decision Support For Discharge Planning: Systematic Review, Simulation, And Natural Language Processing To Elucidate Referral Decision Making

Statement of the Problem: As healthcare data becomes increasingly prolific and older adult patient needs become more complex, there is opportunity for evidence-based technology such as clinical decision support systems (CDSS) to improve decision making at the point of care. Although CDSS for discharge planning is available, few published tools have been translated to new settings. Existing studies have not explored discordance between recommended and actual discharge disposition. Understanding the reasons why patients do not receive optimal post-acute care referrals is critical to improving the discharge planning process for older adults and their families. Methods: Three-paper dissertation examining CDSS. Paper 1 is a systematic review of studies with prediction models for post-acute care (PAC) destination. Paper 2 is a retrospective simulation of a discharge planning CDSS on electronic health record (EHR) data from two hospitals to examine differences in patient characteristics and 30-day readmission rates based on a CDSS recommendation among patients discharged home to self-care. Paper 3 is a natural language processing (NLP) study including retrospective analysis of narrative clinical notes to identify barriers to PAC among hospitalized older adults and create an NLP system to identify sentences containing negative patient preferences. Results: Most prediction models in the literature were developed for specific surgical populations using retrospective structured EHR data. Most models demonstrated high risk of bias and few published follow-up studies. In the simulation study, surgical patients identified by the CDSS as needing PAC but discharged home to self-care experienced adjusted 51.8% higher odds of 30-day readmission compared to those not identified. In the NLP study, the top three barriers were patient has a caregiver, negative preferences, and case management clinical reasoning. Most patients experienced multiple barriers. The negative preferences NLP system achieved an F1-Score of 0.916 using a deep learning model after internal validation. Conclusions: Future prediction modeling studies should follow TRIPOD guidelines to ensure rigorous reporting. Findings from the simulation and NLP studies suggest transportability of the CDSS to large urban academic health systems, especially among surgical patients. Incorporating natural language processing variables into CDSS tools may aid the identification of barriers to PAC