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Botsis T, Kreimeyer K. Improving drug safety with adverse event detection using natural language processing. Expert Opin Drug Saf 2023; 22:659-668. [PMID: 37339273 DOI: 10.1080/14740338.2023.2228197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023]
Abstract
INTRODUCTION Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making. AREAS COVERED We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic. EXPERT OPINION New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.
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Affiliation(s)
- Taxiarchis Botsis
- Department of Oncology, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kory Kreimeyer
- Department of Oncology, the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Nashed A, Zhang S, Chiang CW, Zitu M, Otterson GA, Presley CJ, Kendra K, Patel SH, Johns A, Li M, Grogan M, Lopez G, Owen DH, Li L. Comparative assessment of manual chart review and ICD claims data in evaluating immunotherapy-related adverse events. Cancer Immunol Immunother 2021; 70:2761-2769. [PMID: 33625533 PMCID: PMC10992210 DOI: 10.1007/s00262-021-02880-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND The aim of this retrospective study was to demonstrate that irAEs, specifically gastrointestinal and pulmonary, examined through International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs, thereby concluding that ICD claims data are a poor approach to electronic health record (EHR) data mining for irAEs in immunotherapy clinical research. METHODS This retrospective analysis was conducted in 1,063 cancer patients who received ICIs between 2011 and 2017. We identified irAEs by manual review of medical records to determine the incidence of each of our endpoints, namely colitis, hepatitis, pneumonitis, other irAE, or no irAE. We then performed a secondary analysis utilizing ICD claims data alone using a broad range of symptom and disease-specific ICD codes representative of irAEs. RESULTS 16% (n = 174/1,063) of the total study population was initially found to have either pneumonitis 3% (n = 37), colitis 7% (n = 81) or hepatitis 5% (n = 56) on manual review. Of these patients, 46% (n = 80/174) did not have ICD code evidence in the EHR reflecting their irAE. Of the total patients not found to have any irAEs during manual review, 61% (n = 459/748) of patients had ICD codes suggestive of possible irAE, yet were not identified as having an irAE during manual review. DISCUSSION Examining gastrointestinal and pulmonary irAEs through the International Classification of Disease (ICD) data leads to underrepresentation of true irAEs and overrepresentation of false irAEs.
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Affiliation(s)
- Andrew Nashed
- Department of Internal Medicine, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA.
| | - Shijun Zhang
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Chien-Wei Chiang
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - M Zitu
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Gregory A Otterson
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Carolyn J Presley
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Kari Kendra
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Sandip H Patel
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Andrew Johns
- Department of Internal Medicine, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Mingjia Li
- Department of Internal Medicine, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Madison Grogan
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Gabrielle Lopez
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Dwight H Owen
- Division of Medical Oncology, The Ohio State University, A450B Starling Loving Hall ColumbusA450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
| | - Lang Li
- Department of Biomedical Informatics and Center for Biostatistics, The Ohio State University, A450B Starling Loving Hall Columbus, Columbus, OH, 43210, USA
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Parolo S, Tomasoni D, Bora P, Ramponi A, Kaddi C, Azer K, Domenici E, Neves-Zaph S, Lombardo R. Reconstruction of the Cytokine Signaling in Lysosomal Storage Diseases by Literature Mining and Network Analysis. Front Cell Dev Biol 2021; 9:703489. [PMID: 34490253 PMCID: PMC8417786 DOI: 10.3389/fcell.2021.703489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/30/2021] [Indexed: 11/13/2022] Open
Abstract
Lysosomal storage diseases (LSDs) are characterized by the abnormal accumulation of substrates in tissues due to the deficiency of lysosomal proteins. Among the numerous clinical manifestations, chronic inflammation has been consistently reported for several LSDs. However, the molecular mechanisms involved in the inflammatory response are still not completely understood. In this study, we performed text-mining and systems biology analyses to investigate the inflammatory signals in three LSDs characterized by sphingolipid accumulation: Gaucher disease, Acid Sphingomyelinase Deficiency (ASMD), and Fabry Disease. We first identified the cytokines linked to the LSDs, and then built on the extracted knowledge to investigate the inflammatory signals. We found numerous transcription factors that are putative regulators of cytokine expression in a cell-specific context, such as the signaling axes controlled by STAT2, JUN, and NR4A2 as candidate regulators of the monocyte Gaucher disease cytokine network. Overall, our results suggest the presence of a complex inflammatory signaling in LSDs involving many cellular and molecular players that could be further investigated as putative targets of anti-inflammatory therapies.
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Affiliation(s)
- Silvia Parolo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Danilo Tomasoni
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Pranami Bora
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alan Ramponi
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Enrico Domenici
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, United States
| | - Rosario Lombardo
- Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
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The 2011–2020 Trends of Data-Driven Approaches in Medical Informatics for Active Pharmacovigilance. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11052249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pharmacovigilance, the scientific discipline pertaining to drug safety, has been studied extensively and is progressing continuously. In this field, medical informatics techniques and interpretation play important roles, and appropriate approaches are required. In this study, we investigated and analyzed the trends of pharmacovigilance systems, especially the data collection, detection, assessment, and monitoring processes. We used PubMed to collect papers on pharmacovigilance published over the past 10 years, and analyzed a total of 40 significant papers to determine the characteristics of the databases and data analysis methods used to identify drug safety indicators. Through systematic reviews, we identified the difficulty of standardizing data and terminology and establishing an adverse drug reactions (ADR) evaluation system in pharmacovigilance, and their corresponding implications. We found that appropriate methods and guidelines for active pharmacovigilance using medical big data are still required and should continue to be developed.
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Chen R, Ryan P, Natarajan K, Falconer T, Crew KD, Reich CG, Vashisht R, Randhawa G, Shah NH, Hripcsak G. Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network. JCO Clin Cancer Inform 2020; 4:171-183. [PMID: 32134687 PMCID: PMC7113074 DOI: 10.1200/cci.19.00107] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2020] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have been described for the general noncancer population, less is known about real-world treatment patterns in patients with cancer. We sought to characterize the prevalence and distribution of initial treatment patterns across a large-scale data network for depression, hypertension, and type II diabetes mellitus (T2DM) among patients with cancer. METHODS We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. For this study, we used 8 databases across 3 countries-the United States, France, and Germany-with 295,529,655 patient records. We identified patients with cancer using SNOMED (Systematized Nomenclature of Medicine) codes validated via manual review. We then characterized the treatment patterns of these patients initiating treatment of depression, hypertension, or T2DM with persistent treatment and at least 365 days of observation. RESULTS Across databases, wide variations exist in treatment patterns for depression (n = 1,145,510), hypertension (n = 3,178,944), and T2DM (n = 886,766). When limited to 6-node (6-drug) sequences, we identified 61,052 unique sequences for depression, 346,067 sequences for hypertension, and 40,629 sequences for T2DM. These variations persisted across sites, databases, countries, and conditions, with the exception of metformin (73.8%) being the most common initial T2DM treatment. The most common initial medications were sertraline (17.5%) and escitalopram (17.5%) for depression and hydrochlorothiazide (20.5%) and lisinopril (19.6%) for hypertension. CONCLUSION We identified wide variations in the treatment of common comorbidities in patients with cancer, similar to the general population, and demonstrate the feasibility of conducting research on patients with cancer across a large-scale observational data network using a common data model.
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Affiliation(s)
- Ruijun Chen
- Department of Biomedical Informatics, Columbia University, New York, NY
- Observational Health Data Sciences and Informatics, New York, NY
- Department of Medicine, Weill Cornell Medical College, New York, NY
| | - Patrick Ryan
- Department of Biomedical Informatics, Columbia University, New York, NY
- Observational Health Data Sciences and Informatics, New York, NY
- Epidemiology Analytics, Janssen Research and Development, Titusville, NJ
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY
- Observational Health Data Sciences and Informatics, New York, NY
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY
- Observational Health Data Sciences and Informatics, New York, NY
| | - Katherine D. Crew
- Department of Medicine and the Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY
| | - Christian G. Reich
- Observational Health Data Sciences and Informatics, New York, NY
- IQVIA, Cambridge, MA
| | - Rohit Vashisht
- Observational Health Data Sciences and Informatics, New York, NY
- Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA
| | - Gurvaneet Randhawa
- Health Systems and Interventions Research Branch, Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD
| | - Nigam H. Shah
- Observational Health Data Sciences and Informatics, New York, NY
- Center for Biomedical Informatics Research, Stanford University, Palo Alto, CA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY
- Observational Health Data Sciences and Informatics, New York, NY
- Medical Informatics Services, New York-Presbyterian Hospital, New York, NY
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