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Hauben M, Rafi M, Abdelaziz I, Hassanzadeh O. Knowledge Graphs in Pharmacovigilance: A Scoping Review. Clin Ther 2024:S0149-2918(24)00144-9. [PMID: 38981792 DOI: 10.1016/j.clinthera.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/08/2024] [Accepted: 06/05/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE To critically assess the role and added value of knowledge graphs in pharmacovigilance, focusing on their ability to predict adverse drug reactions. METHODS A systematic scoping review was conducted in which detailed information, including objectives, technology, data sources, methodology, and performance metrics, were extracted from a set of peer-reviewed publications reporting the use of knowledge graphs to support pharmacovigilance signal detection. FINDINGS The review, which included 47 peer-reviewed articles, found knowledge graphs were utilized for detecting/predicting single-drug adverse reactions and drug-drug interactions, with variable reported performance and sparse comparisons to legacy methods. IMPLICATIONS Research to date suggests that knowledge graphs have the potential to augment predictive signal detection in pharmacovigilance, but further research using more reliable reference sets of adverse drug reactions and comparison with legacy pharmacovigilance methods are needed to more clearly define best practices and to establish their place in holistic pharmacovigilance systems.
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Affiliation(s)
- Manfred Hauben
- Department of Family and Community Medicine, New York Medical College, Valhalla, New York; Truliant Consulting, Baltimore, Maryland
| | - Mazin Rafi
- Department of Statistics, Rutgers University, Piscataway, New Jersey.
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Hasselgren C, Oprea TI. Artificial Intelligence for Drug Discovery: Are We There Yet? Annu Rev Pharmacol Toxicol 2024; 64:527-550. [PMID: 37738505 DOI: 10.1146/annurev-pharmtox-040323-040828] [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] [Indexed: 09/24/2023]
Abstract
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
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Affiliation(s)
- Catrin Hasselgren
- Safety Assessment, Genentech, Inc., South San Francisco, California, USA
| | - Tudor I Oprea
- Expert Systems Inc., San Diego, California, USA;
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Simms AM, Kanakia A, Sipra M, Dutta B, Southall N. A patient safety knowledge graph supporting vaccine product development. BMC Med Inform Decis Mak 2024; 24:10. [PMID: 38178113 PMCID: PMC10768450 DOI: 10.1186/s12911-023-02409-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 12/14/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Knowledge graphs are well-suited for modeling complex, unstructured, and multi-source data and facilitating their analysis. During the COVID-19 pandemic, adverse event data were integrated into a knowledge graph to support vaccine safety surveillance and nimbly respond to urgent health authority questions. Here, we provide details of this post-marketing safety system using public data sources. In addition to challenges with varied data representations, adverse event reporting on the COVID-19 vaccines generated an unprecedented volume of data; an order of magnitude larger than adverse events for all previous vaccines. The Patient Safety Knowledge Graph (PSKG) is a robust data store to accommodate the volume of adverse event data and harmonize primary surveillance data sources. METHODS We designed a semantic model to represent key safety concepts. We built an extract-transform-load (ETL) data pipeline to parse and import primary public data sources; align key elements such as vaccine names; integrated the Medical Dictionary for Regulatory Activities (MedDRA); and applied quality metrics. PSKG is deployed in a Neo4J graph database, and made available via a web interface and Application Programming Interfaces (APIs). RESULTS We import and align adverse event data and vaccine exposure data from 250 countries on a weekly basis, producing a graph with 4,340,980 nodes and 30,544,475 edges as of July 1, 2022. PSKG is used for ad-hoc analyses and periodic reporting for several widely available COVID-19 vaccines. Analysis code using the knowledge graph is 80% shorter than an equivalent implementation written entirely in Python, and runs over 200 times faster. CONCLUSIONS Organizing safety data into a concise model of nodes, properties, and edge relationships has greatly simplified analysis code by removing complex parsing and transformation algorithms from individual analyses and instead managing these centrally. The adoption of the knowledge graph transformed how the team answers key scientific and medical questions. Whereas previously an analysis would involve aggregating and transforming primary datasets from scratch to answer a specific question, the team can now iterate easily and respond as quickly as requests evolve (e.g., "Produce vaccine-X safety profile for adverse event-Y by country instead of age-range").
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Affiliation(s)
- Andrew M Simms
- Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
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Liu T, Feenstra KA, Huang Z, Heringa J. Mining literature and pathway data to explore the relations of ketamine with neurotransmitters and gut microbiota using a knowledge-graph. Bioinformatics 2024; 40:btad771. [PMID: 38147362 PMCID: PMC10769815 DOI: 10.1093/bioinformatics/btad771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 11/06/2023] [Accepted: 12/25/2023] [Indexed: 12/27/2023] Open
Abstract
MOTIVATION Up-to-date pathway knowledge is usually presented in scientific publications for human reading, making it difficult to utilize these resources for semantic integration and computational analysis of biological pathways. We here present an approach to mining knowledge graphs by combining manual curation with automated named entity recognition and automated relation extraction. This approach allows us to study pathway-related questions in detail, which we here show using the ketamine pathway, aiming to help improve understanding of the role of gut microbiota in the antidepressant effects of ketamine. RESULTS The thus devised ketamine pathway 'KetPath' knowledge graph comprises five parts: (i) manually curated pathway facts from images; (ii) recognized named entities in biomedical texts; (iii) identified relations between named entities; (iv) our previously constructed microbiota and pre-/probiotics knowledge bases; and (v) multiple community-accepted public databases. We first assessed the performance of automated extraction of relations between named entities using the specially designed state-of-the-art tool BioKetBERT. The query results show that we can retrieve drug actions, pathway relations, co-occurring entities, and their relations. These results uncover several biological findings, such as various gut microbes leading to increased expression of BDNF, which may contribute to the sustained antidepressant effects of ketamine. We envision that the methods and findings from this research will aid researchers who wish to integrate and query data and knowledge from multiple biomedical databases and literature simultaneously. AVAILABILITY AND IMPLEMENTATION Data and query protocols are available in the KetPath repository at https://dx.doi.org/10.5281/zenodo.8398941 and https://github.com/tingcosmos/KetPath.
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Affiliation(s)
- Ting Liu
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
- Learning & Reasoning Group, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - K Anton Feenstra
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Zhisheng Huang
- Learning & Reasoning Group, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jaap Heringa
- Integrative Bioinformatics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
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Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar T. Artificial intelligence in the field of pharmacy practice: A literature review. EXPLORATORY RESEARCH IN CLINICAL AND SOCIAL PHARMACY 2023; 12:100346. [PMID: 37885437 PMCID: PMC10598710 DOI: 10.1016/j.rcsop.2023.100346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores various AI applications in the field of pharmacy practice. The incorporation of AI technologies provides pharmacists with tools and systems that help them make accurate and evidence-based clinical decisions. By using AI algorithms and Machine Learning, pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations tailored to individual patient requirements. Various AI models have been developed to predict and detect adverse drug events, assist clinical decision support systems with medication-related decisions, automate dispensing processes in community pharmacies, optimize medication dosages, detect drug-drug interactions, improve adherence through smart technologies, detect and prevent medication errors, provide medication therapy management services, and support telemedicine initiatives. By incorporating AI into clinical practice, health care professionals can augment their decision-making processes and provide patients with personalized care. AI allows for greater collaboration between different healthcare services provided to a single patient. For patients, AI may be a useful tool for providing guidance on how and when to take a medication, aiding in patient education, and promoting medication adherence and AI may be used to know how and where to obtain the most cost-effective healthcare and how best to communicate with healthcare professionals, optimize the health monitoring using wearables devices, provide everyday lifestyle and health guidance, and integrate diet and exercise.
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Affiliation(s)
- Sri Harsha Chalasani
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Jehath Syed
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Madhan Ramesh
- Dept. of Pharmacy Practice, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
| | - Vikram Patil
- Dept. of Radiology, JSS Medical College & Hospital, JSS Academy of Higher Education & Research, Mysuru 15, Karnataka, India
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王 彩, 郑 增, 蔡 晓, 黄 继, 苏 前. [Overview of the application of knowledge graphs in the medical field]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:1040-1044. [PMID: 37879936 PMCID: PMC10600424 DOI: 10.7507/1001-5515.202204016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/27/2023] [Indexed: 10/27/2023]
Abstract
With the booming development of medical information technology and computer science, the medical services industry is gradually transiting from information technology to intelligence. The medical knowledge graph plays an important role in intelligent medical applications such as knowledge questions and answers and intelligent diagnosis, and is a key technology for promoting wise medical care and the basis for intelligent management of medical information. In order to fully exploit the great potential of knowledge graphs in the medical field, this paper focuses on five aspects: inter-drug relationship discovery, assisted diagnosis, personalized recommendation, decision support and intelligent prediction. The latest research progress on medical knowledge graphs is introduced, and relevant suggestions are made in light of the current challenges and problems faced by medical knowledge graphs to provide reference for promoting the wide application of medical knowledge graphs.
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Affiliation(s)
- 彩云 王
- 上海工程技术大学 电子电气工程学院(上海 201620)College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China
| | - 增亮 郑
- 上海工程技术大学 电子电气工程学院(上海 201620)College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China
| | - 晓琼 蔡
- 上海工程技术大学 电子电气工程学院(上海 201620)College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China
| | - 继汉 黄
- 上海工程技术大学 电子电气工程学院(上海 201620)College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China
| | - 前敏 苏
- 上海工程技术大学 电子电气工程学院(上海 201620)College of Electrical and Electronic Engineering, Shanghai University Of Engineering Science, Shanghai 201620, P. R. China
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Jamrat S, Sukasem C, Sratthaphut L, Hongkaew Y, Samanchuen T. A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making. Comput Biol Med 2023; 165:107329. [PMID: 37611418 DOI: 10.1016/j.compbiomed.2023.107329] [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: 06/06/2023] [Revised: 07/14/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Screening potential drug-drug interactions, drug-gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making. The approach focuses on raising the performance of four potential interaction screenings in the prescribing process, including drug-drug interactions, drug-gene interactions, drug-herb interactions, drug-social lifestyle interactions, and two potential considerations for patients with liver or renal impairment. The work describes the design of a curated knowledge-based model called the knowledge model for potential interaction and consideration screening, the screening logic for both the detection module and inference module, and the personalized prescribing report. Three case studies have demonstrated the proof-of-concept and effectiveness of this approach. The proposed approach aims to reduce decision-making processes for healthcare professionals, reduce medication-related harm, and enhance treatment effectiveness. Additionally, the recommendation with a semantic network is suggested to assist in risk-benefit analysis when health professionals plan therapeutic interventions with new medicines that have insufficient evidence to establish explicit recommendations. This approach offers a promising solution to implementing precision medicine in clinical practice.
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Affiliation(s)
- Samart Jamrat
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand; Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand; Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center, Ramathibodi Hospital, Bangkok, 10400, Thailand; Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Lawan Sratthaphut
- Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand; Department of Biomedicine and Health Informatics, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Yaowaluck Hongkaew
- Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand; Research and Development Laboratory, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Taweesak Samanchuen
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand.
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Schiano di Cola V, Chiaro D, Prezioso E, Izzo S, Giampaolo F. Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning. IEEE J Biomed Health Inform 2023; 27:4649-4659. [PMID: 37018305 DOI: 10.1109/jbhi.2022.3233498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional government. In this perspective, structuring e-health data through a Knowledge Graph (KG) approach can provide a feasible method to quickly and simply organize data and/or retrieve new information. Starting from raw health bookings data from the public healthcare system in Italy, a KG method is presented to support e-health services through the extraction of medical knowledge and novel insights. By exploiting graph embedding which arranges the various attributes of the entities into the same vector space, we are able to apply Machine Learning (ML) techniques to the embedded vectors. The findings suggest that KGs could be used to assess patients' medical booking patterns, either from unsupervised or supervised ML. In particular, the former can determine possible presence of hidden groups of entities that is not immediately available through the original legacy dataset structure. The latter, although the performance of the used algorithms is not very high, shows encouraging results in predicting a patient's likelihood to undergo a particular medical visit within a year. However, many technological advances remain to be made, especially in graph database technologies and graph embedding algorithms.
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Krix S, DeLong LN, Madan S, Domingo-Fernández D, Ahmad A, Gul S, Zaliani A, Fröhlich H. MultiGML: Multimodal graph machine learning for prediction of adverse drug events. Heliyon 2023; 9:e19441. [PMID: 37681175 PMCID: PMC10481305 DOI: 10.1016/j.heliyon.2023.e19441] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023] Open
Abstract
Adverse drug events constitute a major challenge for the success of clinical trials. Several computational strategies have been suggested to estimate the risk of adverse drug events in preclinical drug development. While these approaches have demonstrated high utility in practice, they are at the same time limited to specific information sources. Thus, many current computational approaches neglect a wealth of information which results from the integration of different data sources, such as biological protein function, gene expression, chemical compound structure, cell-based imaging and others. In this work we propose an integrative and explainable multi-modal Graph Machine Learning approach (MultiGML), which fuses knowledge graphs with multiple further data modalities to predict drug related adverse events and general drug target-phenotype associations. MultiGML demonstrates excellent prediction performance compared to alternative algorithms, including various traditional knowledge graph embedding techniques. MultiGML distinguishes itself from alternative techniques by providing in-depth explanations of model predictions, which point towards biological mechanisms associated with predictions of an adverse drug event. Hence, MultiGML could be a versatile tool to support decision making in preclinical drug development.
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Affiliation(s)
- Sophia Krix
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
- Fraunhofer Center for Machine Learning, Germany
| | - Lauren Nicole DeLong
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, 10 Crichton Street, EH8 9AB, UK
| | - Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Department of Computer Science, University of Bonn, 53115, Bonn, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
- Enveda Biosciences, Boulder, CO, 80301, USA
| | - Ashar Ahmad
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
- Grunenthal GmbH, 52099, Aachen, Germany
| | - Sheraz Gul
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases CIMD, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Schnackenburgallee 114, 22525, Hamburg, Germany
- Fraunhofer Cluster of Excellence for Immune-Mediated Diseases CIMD, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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Jackson DB, Racz R, Kim S, Brock S, Burkhart K. Rewiring Drug Research and Development through Human Data-Driven Discovery (HD 3). Pharmaceutics 2023; 15:1673. [PMID: 37376121 DOI: 10.3390/pharmaceutics15061673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.
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Affiliation(s)
| | - Rebecca Racz
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Sarah Kim
- Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, College of Pharmacy, University of Florida, Orlando, FL 32827, USA
| | | | - Keith Burkhart
- Division of Applied Regulatory Science, Office of Clinical Pharmacology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA
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Abu-Salih B, AL-Qurishi M, Alweshah M, AL-Smadi M, Alfayez R, Saadeh H. Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. JOURNAL OF BIG DATA 2023; 10:81. [PMID: 37274445 PMCID: PMC10225120 DOI: 10.1186/s40537-023-00774-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 05/17/2023] [Indexed: 06/06/2023]
Abstract
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.
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Affiliation(s)
| | | | | | - Mohammad AL-Smadi
- Jordan University of Science and Technology, Irbid, Jordan
- Qatar University, Doha, Qatar
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Khan Z, Karatas Y, Hamid SM. Evaluation of health care professionals' knowledge, attitudes, practices and barriers to pharmacovigilance and adverse drug reaction reporting: A cross-sectional multicentral study. PLoS One 2023; 18:e0285811. [PMID: 37224133 DOI: 10.1371/journal.pone.0285811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/29/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Healthcare professionals' involvement and reporting of adverse drug reactions are essential for the success of a pharmacovigilance program. The aim of this study was to assess healthcare professionals (medical doctors, pharmacists, nurses, dentists, midwives, and paramedics) current knowledge, attitude, practices, and barriers regarding pharmacovigilance and adverse drug reactions reporting in multicentral healthcare settings. METHODS A cross-sectional face-to-face survey was conducted among currently working healthcare professionals in various hospitals in ten districts of Adana province, Türkiye from March to October 2022. A self-administered, pretested questionnaire (Cronbach's alpha = 0.894 for knowledge, attitudes and practices variables) was used for data collection. The questionnaire's final draft included five sections (sociodemographic/general information, knowledge, attitude, practices, and barriers) with 58 questions. The collected data was analyzed in SPSS (version 25) using descriptive statistics, the chi-square test, and logistic regression. RESULTS Of the total 435 distributed questionnaires, 412 completed the entire questionnaire, yielding a 94% response rate. The majority of healthcare professionals (60.4%; n = 249) had never received pharmacovigilance training. Among healthcare professionals 51.9% (n = 214), 71.1% (n = 293) and 92.5% (n = 381) had poor knowledge, positive attitudes and poor practices, respectively. Only 32.5% of healthcare professionals kept the record of an adverse drug reaction and only 13.1% reported adverse drug reactions. The profession (medical doctors, pharmacists, nurses, dentists, midwives, and paramedics) of healthcare professionals and a lack of training were predictors of poor adverse drug reaction reporting (p < 0.05). A statistically significant difference in healthcare professionals and knowledge, attitude and practices scores was also observed (p < 0.05). The main barriers which were supposed to discourage adverse drug reactions reporting by the healthcare professionals were higher workload (63.8%) followed by thinking that a single adverse drug reaction report makes no impact (63.6%) and lack of a professional atmosphere (51.9%). CONCLUSION In the current study, most healthcare professionals had poor knowledge and practice, but they had a positive attitude toward pharmacovigilance and adverse drug reactions reporting. Barriers to under-reporting of adverse drug reactions were also highlighted. Periodic training programs, educational interventions, systematic follow-up of healthcare professionals by local healthcare authorities, interprofessional links between all healthcare professionals, and the implementation of mandatory reporting policies are critical for improving healthcare professionals knowledge, practices, patient safety and pharmacovigilance activities.
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Affiliation(s)
- Zakir Khan
- Department of Medical Pharmacology, Faculty of Medicines, Çukurova University, Adana, Türkiye
| | - Yusuf Karatas
- Department of Medical Pharmacology, Faculty of Medicines, Çukurova University, Adana, Türkiye
- Faculty of Medicines, Balcali Hospital, Adana, Türkiye
| | - Syed Muhammad Hamid
- Department of Community Medicine, Khyber Medical College, Peshawar, Pakistan
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Murali L, Gopakumar G, Viswanathan DM, Nedungadi P. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. J Biomed Inform 2023:104403. [PMID: 37230406 DOI: 10.1016/j.jbi.2023.104403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 05/27/2023]
Abstract
With the growth of data and intelligent technologies, the healthcare sector opened numerous technology that enabled services for patients, clinicians, and researchers. One major hurdle in achieving state-of-the-art results in health informatics is domain-specific terminologies and their semantic complexities. A knowledge graph crafted from medical concepts, events, and relationships acts as a medical semantic network to extract new links and hidden patterns from health data sources. Current medical knowledge graph construction studies are limited to generic techniques and opportunities and focus less on exploiting real-world data sources in knowledge graph construction. A knowledge graph constructed from Electronic Health Records (EHR) data obtains real-world data from healthcare records. It ensures better results in subsequent tasks like knowledge extraction and inference, knowledge graph completion, and medical knowledge graph applications such as diagnosis predictions, clinical recommendations, and clinical decision support. This review critically analyses existing works on medical knowledge graphs that used EHR data as the data source at (i) representation level, (ii) extraction level (iii) completion level. In this investigation, we found that EHR-based knowledge graph construction involves challenges such as high complexity and dimensionality of data, lack of knowledge fusion, and dynamic update of the knowledge graph. In addition, the study presents possible ways to tackle the challenges identified. Our findings conclude that future research should focus on knowledge graph integration and knowledge graph completion challenges.
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Affiliation(s)
- Lino Murali
- Center for Research in Analytics and Technologies for Education (CREATE), Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India; Division of Information technology, School of Engineering, Cochin University of Science and Technology, Kochi, 682022, Kerala, India
| | - G Gopakumar
- Department of Computer Science and Engineering, School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India
| | - Daleesha M Viswanathan
- Division of Information technology, School of Engineering, Cochin University of Science and Technology, Kochi, 682022, Kerala, India
| | - Prema Nedungadi
- Center for Research in Analytics and Technologies for Education (CREATE), Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India; Department of Computer Science and Engineering, School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India.
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14
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Hänsel K, Dudgeon SN, Cheung KH, Durant TJS, Schulz WL. From Data to Wisdom: Biomedical Knowledge Graphs for Real-World Data Insights. J Med Syst 2023; 47:65. [PMID: 37195430 PMCID: PMC10191934 DOI: 10.1007/s10916-023-01951-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/15/2023] [Indexed: 05/18/2023]
Abstract
Graph data models are an emerging approach to structure clinical and biomedical information. These models offer intriguing opportunities for novel approaches in healthcare, such as disease phenotyping, risk prediction, and personalized precision care. The combination of data and information in a graph model to create knowledge graphs has rapidly expanded in biomedical research, but the integration of real-world data from the electronic health record has been limited. To broadly apply knowledge graphs to EHR and other real-world data, a deeper understanding of how to represent these data in a standardized graph model is needed. We provide an overview of the state-of-the-art research for clinical and biomedical data integration and summarize the potential to accelerate healthcare and precision medicine research through insight generation from integrated knowledge graphs.
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Affiliation(s)
- Katrin Hänsel
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Sarah N Dudgeon
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Kei-Hoi Cheung
- Section of Biomedical Informatics, Department of Emergency Medicine, Yale School of Medicine, 55 Park Street, PS 210, New Haven, CT, 06510, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Thomas J S Durant
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Wade L Schulz
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
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15
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Johnson M, Patel M, Phipps A, van der Schaar M, Boulton D, Gibbs M. The potential and pitfalls of artificial intelligence in clinical pharmacology. CPT Pharmacometrics Syst Pharmacol 2023; 12:279-284. [PMID: 36717763 PMCID: PMC10014043 DOI: 10.1002/psp4.12902] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 02/01/2023] Open
Affiliation(s)
- Martin Johnson
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Science, R&D, AstraZeneca, Cambridge, UK
| | - Mishal Patel
- Clinical Pharmacology and Quantitative Pharmacology, Artificial Intelligence & Data Analytics, R&D, AstraZeneca, Cambridge, UK
| | - Alex Phipps
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Science, R&D, AstraZeneca, Cambridge, UK
| | - Mihaela van der Schaar
- Cambridge Centre for Artificial Intelligence in Medicine, Department of Applied Mathematics and Theoretical Physics and Department of Population Health, University of Cambridge, Cambridge, UK
| | - Dave Boulton
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
| | - Megan Gibbs
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gaithersburg, Maryland, USA
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16
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Khan W, Zaki N, Ahmad A, Bian J, Ali L, Mehedy Masud M, Ghenimi N, Ahmed LA. Infant Low Birth Weight Prediction Using Graph Embedding Features. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1317. [PMID: 36674072 PMCID: PMC9859143 DOI: 10.3390/ijerph20021317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Amir Ahmad
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, USA
| | - Luqman Ali
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Mohammad Mehedy Masud
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nadirah Ghenimi
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Luai A. Ahmed
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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17
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iADRGSE: A Graph-Embedding and Self-Attention Encoding for Identifying Adverse Drug Reaction in the Earlier Phase of Drug Development. Int J Mol Sci 2022; 23:ijms232416216. [PMID: 36555858 PMCID: PMC9786008 DOI: 10.3390/ijms232416216] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/15/2022] [Accepted: 12/16/2022] [Indexed: 12/23/2022] Open
Abstract
Adverse drug reactions (ADRs) are a major issue to be addressed by the pharmaceutical industry. Early and accurate detection of potential ADRs contributes to enhancing drug safety and reducing financial expenses. The majority of the approaches that have been employed to identify ADRs are limited to determining whether a drug exhibits an ADR, rather than identifying the exact type of ADR. By introducing the "multi-level feature-fusion deep-learning model", a new predictor, called iADRGSE, has been developed, which can be used to identify adverse drug reactions at the early stage of drug discovery. iADRGSE integrates a self-attentive module and a graph-network module that can extract one-dimensional sub-structure sequence information and two-dimensional chemical-structure graph information of drug molecules. As a demonstration, cross-validation and independent testing were performed with iADRGSE on a dataset of ADRs classified into 27 categories, based on SOC (system organ classification). In addition, experiments comparing iADRGSE with approaches such as NPF were conducted on the OMOP dataset, using the jackknife test method. Experiments show that iADRGSE was superior to existing state-of-the-art predictors.
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18
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Liu T, Lan G, Feenstra KA, Huang Z, Heringa J. Towards a knowledge graph for pre-/probiotics and microbiota-gut-brain axis diseases. Sci Rep 2022; 12:18977. [PMID: 36347868 PMCID: PMC9643397 DOI: 10.1038/s41598-022-21735-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/30/2022] [Indexed: 11/09/2022] Open
Abstract
Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota-gut-brain axis diseases. To this end, we created (i) a knowledge base, dubbed ppstatement, containing manually curated detailed annotations, and (ii) a knowledge base, called ppconcept, containing automatically annotated concepts. The resulting Pre-/Probiotics Knowledge Graph (PPKG) combines these two knowledge bases with three other public databases (i.e. MeSH, UMLS and SNOMED CT). To validate the performance of PPKG and to demonstrate the added value of integrating two knowledge bases, we created four biological query cases. The query cases demonstrate that we can retrieve co-occurring concepts of interest, and also that combining the two knowledge bases leads to more comprehensive query results than utilizing them separately. The PPKG enables users to pose research queries such as "which pre-/probiotics combinations may benefit depression?", potentially leading to novel biological insights.
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Affiliation(s)
- Ting Liu
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Knowledge Representation and Reasoning Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Gongjin Lan
- grid.263817.90000 0004 1773 1790Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055 China
| | - K. Anton Feenstra
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Zhisheng Huang
- grid.12380.380000 0004 1754 9227Knowledge Representation and Reasoning Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Jaap Heringa
- grid.12380.380000 0004 1754 9227Department of Computer Science, Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
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19
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Alpay BA, Gosink M, Aguiar D. Evaluating molecular fingerprint-based models of drug side effects against a statistical control. Drug Discov Today 2022; 27:103364. [PMID: 36115633 DOI: 10.1016/j.drudis.2022.103364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/24/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022]
Abstract
There are many machine learning models that use molecular fingerprints of drugs to predict side effects. Characterizing their skill is necessary for understanding their usefulness in pharmaceutical development. Here, we analyze a statistical control of side effect prediction skill, develop a pipeline for benchmarking models, and evaluate how well existing models predict side effects identified in pharmaceutical documentation. We demonstrate that molecular fingerprints are useful for ranking drugs by their likelihood to cause a given side effect. However, the predictions for one or more drugs overall benefit only marginally from molecular fingerprints when ranking the likelihoods of many possible side effects, and display at most modest overall skill at identifying the side effects that do and do not occur.
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Affiliation(s)
- Berk A Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA 02138, USA.
| | | | - Derek Aguiar
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
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20
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Turki H, Jemielniak D, Hadj Taieb MA, Labra Gayo JE, Ben Aouicha M, Banat M, Shafee T, Prud’hommeaux E, Lubiana T, Das D, Mietchen D. Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata. PeerJ Comput Sci 2022; 8:e1085. [PMID: 36262159 PMCID: PMC9575845 DOI: 10.7717/peerj-cs.1085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 08/15/2022] [Indexed: 06/16/2023]
Abstract
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
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Affiliation(s)
- Houcemeddine Turki
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Dariusz Jemielniak
- Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Masovia, Poland
| | - Mohamed A. Hadj Taieb
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Jose E. Labra Gayo
- Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Oviedo, Asturias, Spain
| | - Mohamed Ben Aouicha
- Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
| | - Mus’ab Banat
- Faculty of Medicine, Hashemite University, Zarqa, Jordan
| | - Thomas Shafee
- La Trobe University, Melbourne, Victoria, Australia
- Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Eric Prud’hommeaux
- World Wide Web Consortium, Cambridge, Massachusetts, United States of America
| | - Tiago Lubiana
- Computational Systems Biology Laboratory, University of São Paulo, São Paulo, Brazil
| | - Diptanshu Das
- Institute of Child Health (ICH), Kolkata, West Bengal, India
- Medica Superspecialty Hospital, Kolkata, West Bengal, India
| | - Daniel Mietchen
- Ronin Institute, Montclair, New Jersey, United States of America
- Department of Evolutionary and Integrative Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
- School of Data Science, University of Virginia, Charlottesville, Virginia, United States
- Institute for Globally Distributed Open Research and Education (IGDORE), Jena, Germany
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21
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Verman S, Anjankar A. A Narrative Review of Adverse Event Detection, Monitoring, and Prevention in Indian Hospitals. Cureus 2022; 14:e29162. [PMID: 36258971 PMCID: PMC9564564 DOI: 10.7759/cureus.29162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022] Open
Abstract
An adverse event is any abnormal clinical finding associated with the use of a therapy. Adverse events are classified by reporting an event's seriousness, expectedness, and relatedness. Monitoring patient safety is of utmost importance as more and more data becomes available. In reality, very low numbers of adverse events are reported via the official path. Chart review, voluntary reporting, computerized surveillance, and direct observation can detect adverse drug events. Medication errors are commonly seen in hospitals and need provider and system-based interventions to prevent them. The need of the hour in India is to develop and implement medication safety best practices to avoid adverse events. The utility of artificial intelligence techniques in adverse event detection remains unexplored, and their accuracy and precision need to be studied in a controlled setting. There is a need to develop predictive models to assess the likelihood of adverse reactions while testing novel pharmaceutical drugs.
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22
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Pitoglou S, Filntisi A, Anastasiou A, Matsopoulos GK, Koutsouris D. Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project. Front Digit Health 2022; 4:841853. [PMID: 36120716 PMCID: PMC9474677 DOI: 10.3389/fdgth.2022.841853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.
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Affiliation(s)
- Stavros Pitoglou
- Computer Solutions SA, Research & Development Dpt., Athens, Greece
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Correspondence: Stavros Pitoglou
| | - Arianna Filntisi
- Computer Solutions SA, Research & Development Dpt., Athens, Greece
| | - Athanasios Anastasiou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Dimitrios Koutsouris
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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23
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Salas M, Petracek J, Yalamanchili P, Aimer O, Kasthuril D, Dhingra S, Junaid T, Bostic T. The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature. Pharmaceut Med 2022; 36:295-306. [PMID: 35904529 DOI: 10.1007/s40290-022-00441-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. OBJECTIVE The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. METHODS Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. RESULTS Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. CONCLUSION Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
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Affiliation(s)
- Maribel Salas
- Daiichi Sankyo, Inc. & Center for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 211 Mount Airy Rd, Basking Ridge, NJ, USA
| | - Jan Petracek
- Institute of Pharmacovigilance, Hvezdova 2b, 14000, Prague, Czech Republic
| | - Priyanka Yalamanchili
- Daiichi Sankyo, Inc. & Rutgers University, 211 Mount Airy Rd, Basking Ridge, NJ, USA.
| | | | | | - Sameer Dhingra
- Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, India
| | | | - Tina Bostic
- PPD, part of Thermo Fisher Scientific, Wilmington, NC, USA
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24
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Kim HR, Sung M, Park JA, Jeong K, Kim HH, Lee S, Park YR. Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review. Medicine (Baltimore) 2022; 101:e29387. [PMID: 35758373 PMCID: PMC9276413 DOI: 10.1097/md.0000000000029387] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 04/12/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs. METHODS A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines. RESULTS We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest. CONCLUSIONS Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.
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Affiliation(s)
- Hae Reong Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - MinDong Sung
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Ji Ae Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Kyeongseob Jeong
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Ho Heon Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
| | - Suehyun Lee
- Department of Biomedical Informatics, Konyang University College of Medicine, Daejeon, South Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, South Korea
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25
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Paci P, Fiscon G, Conte F, Wang RS, Handy DE, Farina L, Loscalzo J. Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects. NPJ Syst Biol Appl 2022; 8:12. [PMID: 35443763 PMCID: PMC9021283 DOI: 10.1038/s41540-022-00221-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 03/19/2022] [Indexed: 12/28/2022] Open
Abstract
Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.
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Affiliation(s)
- Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy. .,Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
| | - Giulia Fiscon
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.,Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Federica Conte
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Rui-Sheng Wang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Diane E Handy
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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Interdisciplinary analysis of drugs: Structural features and clinical data. J Clin Transl Sci 2022; 6:e43. [PMID: 35651960 PMCID: PMC9108004 DOI: 10.1017/cts.2022.375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/10/2022] [Accepted: 03/16/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Chemical structure is a vital consideration early in the drug development process. Its role in analysis of safety and efficacy is relatively diminished after drugs are approved for clinical use. This interdisciplinary study explores a strategy by which readily available clinical data may be used along with structural features of drugs to identify associations with potential utility for both clinical decision-making and drug development. Methods: Chemical functional groups and structural groups (SGs) of 261 drugs were manually classified in tiers, and their incidence of gastrointestinal (GI) and central nervous system (CNS) adverse drug reactions (ADRs) were obtained from a clinical database. Drugs with an GI or CNS ADR incidence of at least 10% were analyzed for correlations with their functional and SGs. Results: Eight statistically significant associations were detected by preliminary analysis: piperazine and methylene groups were associated with higher rate of CNS ADRs; while amides, secondary alcohols, and di-substituted phenyl groups were associated with lower rates of GI or CNS ADRs or both. Conclusions: Although further study is necessary to understand these associations and build upon this strategy, this exploratory analysis establishes a methodology by which chemical properties of drugs may be used to aid in clinical decision-making when choosing between otherwise equivalent drug therapy options, as the presence of specific groups on drugs may be associated with increased or decreased risks of specific ADRs.
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Martinez-Aguero S, Marques AG, Mora-Jimenez I, Alvarez-Rodriguez J, Soguero-Ruiz C. Data and Network Analytics for COVID-19 ICU Patients: A Case Study for a Spanish Hospital. IEEE J Biomed Health Inform 2021; 25:4340-4353. [PMID: 34591775 DOI: 10.1109/jbhi.2021.3116804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic presents unprecedented challenges to the healthcare systems around the world. In 2020, Spain was among the countries with the highest Intensive Care Unit (ICU) hospitalization and mortality rates. This work analyzes data of COVID-19 patients admitted to a Spanish ICU during the first wave of the pandemic. The patients in our study either died (deceased patients) or were discharged from the ICU (non-deceased patients) and underwent the following landmarks: beginning of symptoms; arrival at the emergency department; beginning of the hospital stay; and ICU admission. Our goal is to create a graph-based data-science methodology to find associations among patients' comorbidities, previous medication, symptoms, and the COVID-19 treatment, and to analyze their evolution across landmarks. Towards that end, we first perform a hypothesis test based on bootstrap to identify discriminative features among deceased and non-deceased patients. Then, we leverage graph-based representations and network analytics to determine pairwise associations and complex relations among clinical features. The descriptive statistical analysis confirms that deceased patients exhibit multiple comorbidities with stronger levels of association and are treated with a wider range of drugs during the ICU stay. We also observe that the most common treatment was the simultaneous administration of lopinavir/ritonavir with hydroxychloroquine, regardless of the patients' outcome. Our results illustrate how graph tools and representations yield insights on the relations among comorbidities, drug treatments, and patients' evolution. All in all, the approach puts forth a new data-analysis tool for clinicians that can be applied to analyze (post-COVID) symptom/patient evolution.
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Yang X, Wu C, Nenadic G, Wang W, Lu K. Mining a stroke knowledge graph from literature. BMC Bioinformatics 2021; 22:387. [PMID: 34325669 PMCID: PMC8319697 DOI: 10.1186/s12859-021-04292-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 07/06/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Stroke has an acute onset and a high mortality rate, making it one of the most fatal diseases worldwide. Its underlying biology and treatments have been widely studied both in the "Western" biomedicine and the Traditional Chinese Medicine (TCM). However, these two approaches are often studied and reported in insolation, both in the literature and associated databases. RESULTS To aid research in finding effective prevention methods and treatments, we integrated knowledge from the literature and a number of databases (e.g. CID, TCMID, ETCM). We employed a suite of biomedical text mining (i.e. named-entity) approaches to identify mentions of genes, diseases, drugs, chemicals, symptoms, Chinese herbs and patent medicines, etc. in a large set of stroke papers from both biomedical and TCM domains. Then, using a combination of a rule-based approach with a pre-trained BioBERT model, we extracted and classified links and relationships among stroke-related entities as expressed in the literature. We construct StrokeKG, a knowledge graph includes almost 46 k nodes of nine types, and 157 k links of 30 types, connecting diseases, genes, symptoms, drugs, pathways, herbs, chemical, ingredients and patent medicine. CONCLUSIONS Our Stroke-KG can provide practical and reliable stroke-related knowledge to help with stroke-related research like exploring new directions for stroke research and ideas for drug repurposing and discovery. We make StrokeKG freely available at http://114.115.208.144:7474/browser/ (Please click "Connect" directly) and the source structured data for stroke at https://github.com/yangxi1016/Stroke.
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Affiliation(s)
- Xi Yang
- College of Computer, National University of Defence Technology, Changsha, 410073 China
- State Key Laboratory of High-Performance Computing, National University of Defence Technology, Changsha, 410073 China
- Department of Computer Science, University of Manchester, Manchester, M13 9PL UK
| | - Chengkun Wu
- State Key Laboratory of High-Performance Computing, National University of Defence Technology, Changsha, 410073 China
| | - Goran Nenadic
- Department of Computer Science, University of Manchester, Manchester, M13 9PL UK
| | - Wei Wang
- College of Computer, National University of Defence Technology, Changsha, 410073 China
| | - Kai Lu
- College of Computer, National University of Defence Technology, Changsha, 410073 China
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Hu J, Lepore R, Dobson RJB, Al-Chalabi A, M. Bean D, Iacoangeli A. DGLinker: flexible knowledge-graph prediction of disease-gene associations. Nucleic Acids Res 2021; 49:W153-W161. [PMID: 34125897 PMCID: PMC8262728 DOI: 10.1093/nar/gkab449] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/30/2021] [Accepted: 05/17/2021] [Indexed: 11/14/2022] Open
Abstract
As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.
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Affiliation(s)
- Jiajing Hu
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, London, UK
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 9RT, UK
| | - Rosalba Lepore
- BSC-CNS Barcelona Supercomputing Center, Barcelona, 08034, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, London, UK
- Health Data Research UK London, University College London, London, WC1E 6BT, UK
- Institute of Health Informatics, University College London, London, NW1 2DA, UK
| | - Ammar Al-Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 9RT, UK
- King′s College Hospital, Bessemer Road, Denmark Hill, London, SE5 9RS, UK
| | - Daniel M. Bean
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, London, UK
- Health Data Research UK London, University College London, London, WC1E 6BT, UK
| | - Alfredo Iacoangeli
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, SE5 8AF, London, UK
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 9RT, UK
- National Institute for Health Research Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and King's College London, London, SE5 8AF, UK
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Bresso E, Monnin P, Bousquet C, Calvier FE, Ndiaye NC, Petitpain N, Smaïl-Tabbone M, Coulet A. Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining. BMC Med Inform Decis Mak 2021; 21:171. [PMID: 34039343 PMCID: PMC8157660 DOI: 10.1186/s12911-021-01518-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 05/05/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. METHODS We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. RESULTS Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. CONCLUSION Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.
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Affiliation(s)
- Emmanuel Bresso
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
- Centre d’Investigations Cliniques Plurithématique 1433, Inserm 1116, CHRU de Nancy, Université de Lorraine, Nancy, France
| | - Pierre Monnin
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
- Orange, Belfort, France
| | - Cédric Bousquet
- Service de santé publique et information médicale, CHU de Saint Etienne, Saint Etienne, France
- Sorbonne Université, Inserm, Université Paris 13, LIMICS, Paris, France
| | - François-Elie Calvier
- Service de santé publique et information médicale, CHU de Saint Etienne, Saint Etienne, France
| | | | - Nadine Petitpain
- Centre Régional de Pharmacovigilance, CHRU of Nancy, Nancy, France
| | | | - Adrien Coulet
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, France
- Inria Paris, Paris, France
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, Université de Paris, Paris, France
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Joshi P, Vedhanayagam M, Ramesh R. An Ensembled SVM Based Approach for Predicting Adverse Drug Reactions. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200707141420] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Preventing adverse drug reactions (ADRs) is imperative for the safety of
the people. The problem of under-reporting the ADRs has been prevalent across the world, making it
difficult to develop the prediction models, which are unbiased. As a result, most of the models are
skewed to the negative samples leading to high accuracy but poor performance in other metrics such
as precision, recall, F1 score, and AUROC score.
Objective:
In this work, we have proposed a novel way of predicting the ADRs by balancing the dataset.
Method:
The whole data set has been partitioned into balanced smaller data sets. SVMs with
optimal kernel have been learned using each of the balanced data sets and the prediction of given
ADR for the given drug has been obtained by voting from the ensembled optimal SVMs learned.
Results:
We have found that results are encouraging and comparable with the competing methods in
the literature and obtained the average sensitivity of 0.97 for all the ADRs. The model has been
interpreted and explained with SHAP values by various plots.
Conclusion:
A novel way of predicting ADRs by balancing the dataset has been proposed thereby
reducing the effect of unbalanced datasets.
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Affiliation(s)
- Pratik Joshi
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai, India
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Munir S, Jami SI, Wasi S. Towards the Modelling of Veillance based Citizen Profiling using Knowledge Graphs. OPEN COMPUTER SCIENCE 2021. [DOI: 10.1515/comp-2020-0209] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.
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Affiliation(s)
- Siraj Munir
- Department of Computer Science , Mohammad Ali Jinnah University , Karachi , Pakistan
| | - Syed Imran Jami
- Department of Computer Science , Mohammad Ali Jinnah University , Karachi , Pakistan
| | - Shaukat Wasi
- Department of Computer Science , Mohammad Ali Jinnah University , Karachi , Pakistan
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Zhang F, Sun B, Diao X, Zhao W, Shu T. Prediction of adverse drug reactions based on knowledge graph embedding. BMC Med Inform Decis Mak 2021; 21:38. [PMID: 33541342 PMCID: PMC7863488 DOI: 10.1186/s12911-021-01402-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/19/2021] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Adverse drug reactions (ADRs) are an important concern in the medication process and can pose a substantial economic burden for patients and hospitals. Because of the limitations of clinical trials, it is difficult to identify all possible ADRs of a drug before it is marketed. We developed a new model based on data mining technology to predict potential ADRs based on available drug data. METHOD Based on the Word2Vec model in Nature Language Processing, we propose a new knowledge graph embedding method that embeds drugs and ADRs into their respective vectors and builds a logistic regression classification model to predict whether a given drug will have ADRs. RESULT First, a new knowledge graph embedding method was proposed, and comparison with similar studies showed that our model not only had high prediction accuracy but also was simpler in model structure. In our experiments, the AUC of the classification model reached a maximum of 0.87, and the mean AUC was 0.863. CONCLUSION In this paper, we introduce a new method to embed knowledge graph to vectorize drugs and ADRs, then use a logistic regression classification model to predict whether there is a causal relationship between them. The experiment showed that the use of knowledge graph embedding can effectively encode drugs and ADRs. And the proposed ADRs prediction system is also very effective.
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Affiliation(s)
- Fei Zhang
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Bo Sun
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Xiaolin Diao
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Wei Zhao
- Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037 China
| | - Ting Shu
- National Institute of Hospital Administration, National Health Commission, Building 3, Yard 6, Shouti South Road, Haidian, Beijing, 100044 China
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Mantripragada AS, Teja SP, Katasani RR, Joshi P, V M, Ramesh R. Prediction of adverse drug reactions using drug convolutional neural networks. J Bioinform Comput Biol 2021; 19:2050046. [PMID: 33472571 DOI: 10.1142/s0219720020500468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in need of the drug. The in silico prediction of Adverse Drug Reactions can help speed up the aforementioned process. The challenges involved are lack of negative data present and predicting ADR from just the chemical structure. Although many models are already available to predict ADR, most of the models use biological activities identifiers, chemical and physical properties in addition to chemical structures of the drugs. But for most of the new drugs to be tested, only chemical structures will be available. The performance of the existing models predicting ADR only using chemical structures is not efficient. Therefore, an efficient prediction of ADRs from just the chemical structure has been proposed in this paper. The proposed method involves a separate model for each ADR, making it a binary classification problem. This paper presents a novel CNN model called Drug Convolutional Neural Network (DCNN) to predict ADRs using chemical structures of the drugs. The performance is measured using the metrics such as Accuracy, Recall, Precision, Specificity, F1 score, AUROC and MCC. The results obtained by the proposed DCNN model outperform the competing models on the SIDER4.1 database in terms of all the metrics. A case study has been performed on a COVID-19 recommended drugs, where the proposed model predicted the ADRs that are well aligned with the observations made by medical professionals using conventional methods.
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Affiliation(s)
| | - Sai Phani Teja
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India
| | - Rohith Reddy Katasani
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India
| | - Pratik Joshi
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India
| | - Masilamani V
- Department of Computer Science and Engineering, IIITDM Kancheepuram, Chennai 600127, India
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Wang M, Ma X, Si J, Tang H, Wang H, Li T, Ouyang W, Gong L, Tang Y, He X, Huang W, Liu X. Adverse Drug Reaction Discovery Using a Tumor-Biomarker Knowledge Graph. Front Genet 2021; 11:625659. [PMID: 33584816 PMCID: PMC7873847 DOI: 10.3389/fgene.2020.625659] [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: 11/03/2020] [Accepted: 12/09/2020] [Indexed: 12/14/2022] Open
Abstract
Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node: Tumor, Biomarker, Drug, and ADR using biomedical literatures. Based on this knowledge graph, we not only discovered potential ADRs of antitumor drugs but also provided explanations. Experiments on real-world data show that our model can achieve 0.81 accuracy of three cross-validation and the ADRs discovery of Osimertinib was chosen for the clinical validation. Calculated ADRs of Osimertinib by our model consisted of the known ADRs which were in line with the official manual and some unreported rare ADRs in clinical cases. Results also showed that our model outperformed traditional co-occurrence methods. Moreover, each calculated ADRs were attached with the corresponding paths of “tumor-biomarker-drug” in the knowledge graph which could help to obtain in-depth insights into the underlying mechanisms. In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable method for potential ADRs discovery based on biomarkers and might be valuable to the community working on the emerging field of biomedical literature mining and provide impetus for the mechanism research of ADRs.
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Affiliation(s)
- Meng Wang
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Xinyu Ma
- School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Jingwen Si
- Department of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Hongjia Tang
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Haofen Wang
- College of Design and Innovation, Tongji University, Shanghai, China
| | - Tunliang Li
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Wen Ouyang
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Liying Gong
- Department of Intensive Care Unit, Third Xiangya Hospital, Central South University, Changsha, China
| | - Yongzhong Tang
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Xi He
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Wei Huang
- Department of Cardiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Xing Liu
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, China
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Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020; 10:E247. [PMID: 33256133 PMCID: PMC7712791 DOI: 10.3390/jpm10040247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, UK;
| | - George Giannakopoulos
- Institute of Informatics and Telecommunications, NCSR Demokritos, 153 10 Aghia Paraskevi, Greece;
- Science For You (SciFY) PNPC, TEPA Lefkippos-NCSR Demokritos, 27, Neapoleos, 153 41 Ag. Paraskevi, Greece
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
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Xiu X, Qian Q, Wu S. Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study. JMIR Med Inform 2020; 8:e18287. [PMID: 33026359 PMCID: PMC7578820 DOI: 10.2196/18287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 08/23/2020] [Accepted: 09/22/2020] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND With the increasing incidences and mortality of digestive system tumor diseases in China, ways to use clinical experience data in Chinese electronic medical records (CEMRs) to determine potentially effective relationships between diagnosis and treatment have become a priority. As an important part of artificial intelligence, a knowledge graph is a powerful tool for information processing and knowledge organization that provides an ideal means to solve this problem. OBJECTIVE This study aimed to construct a semantic-driven digestive system tumor knowledge graph (DSTKG) to represent the knowledge in CEMRs with fine granularity and semantics. METHODS This paper focuses on the knowledge graph schema and semantic relationships that were the main challenges for constructing a Chinese tumor knowledge graph. The DSTKG was developed through a multistep procedure. As an initial step, a complete DSTKG construction framework based on CEMRs was proposed. Then, this research built a knowledge graph schema containing 7 classes and 16 kinds of semantic relationships and accomplished the DSTKG by knowledge extraction, named entity linking, and drawing the knowledge graph. Finally, the quality of the DSTKG was evaluated from 3 aspects: data layer, schema layer, and application layer. RESULTS Experts agreed that the DSTKG was good overall (mean score 4.20). Especially for the aspects of "rationality of schema structure," "scalability," and "readability of results," the DSTKG performed well, with scores of 4.72, 4.67, and 4.69, respectively, which were much higher than the average. However, the small amount of data in the DSTKG negatively affected its "practicability" score. Compared with other Chinese tumor knowledge graphs, the DSTKG can represent more granular entities, properties, and semantic relationships. In addition, the DSTKG was flexible, allowing personalized customization to meet the designer's focus on specific interests in the digestive system tumor. CONCLUSIONS We constructed a granular semantic DSTKG. It could provide guidance for the construction of a tumor knowledge graph and provide a preliminary step for the intelligent application of knowledge graphs based on CEMRs. Additional data sources and stronger research on assertion classification are needed to gain insight into the DSTKG's potential.
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Affiliation(s)
- Xiaolei Xiu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Qing Qian
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Sizhu Wu
- Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Galeano D, Li S, Gerstein M, Paccanaro A. Predicting the frequencies of drug side effects. Nat Commun 2020; 11:4575. [PMID: 32917868 PMCID: PMC7486409 DOI: 10.1038/s41467-020-18305-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 08/07/2020] [Indexed: 12/25/2022] Open
Abstract
A central issue in drug risk-benefit assessment is identifying frequencies of side effects in humans. Currently, frequencies are experimentally determined in randomised controlled clinical trials. We present a machine learning framework for computationally predicting frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug for which a small number of side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We show that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration. Currently, the frequencies of drug side effects are determined in randomised controlled clinical trials. Here the authors develop an interpretable machine learning approach to predict the frequencies of unknown side effects for drugs with a small number of determined side effect frequencies.
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Affiliation(s)
- Diego Galeano
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK.,School of Applied Mathematics, Fundação Getulio Vargas, Rio de Janeiro, Brazil
| | - Shantao Li
- Department of Computer Science and Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Department of Computer Science, and Department of Statistics and Data Science, Yale University, New Haven, CT, 06520, USA
| | - Alberto Paccanaro
- Department of Computer Science, Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham Hill, Egham, UK. .,School of Applied Mathematics, Fundação Getulio Vargas, Rio de Janeiro, Brazil.
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40
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Chen WJ, Yang SY, Chang JC, Cheng WC, Lu TP, Wang YN, Juan MH, Hsu RT, Huang SR, Tu JJ, Wang PC, Feng VWS, Chang PZ. Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform. Biomed J 2020; 43:318-324. [PMID: 32654885 PMCID: PMC7305507 DOI: 10.1016/j.bj.2020.06.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/10/2020] [Accepted: 06/16/2020] [Indexed: 12/17/2022] Open
Abstract
Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.
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Affiliation(s)
- Wei J Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
| | | | | | | | - Tzu-Pin Lu
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Neng Wang
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Ming-Hao Juan
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Ruey-Tzer Hsu
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
| | - Song-Ren Huang
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Jia-Jang Tu
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Pang-Chieh Wang
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Vincent W-S Feng
- Computational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Pei-Zen Chang
- Industrial Technology Research Institute, Hsinchu, Taiwan
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Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020; 8:e18599. [PMID: 32706688 PMCID: PMC7414411 DOI: 10.2196/18599] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/26/2020] [Accepted: 06/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
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42
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Bean DM, Al-Chalabi A, Dobson RJB, Iacoangeli A. A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis. Genes (Basel) 2020; 11:E668. [PMID: 32575372 PMCID: PMC7349022 DOI: 10.3390/genes11060668] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 06/13/2020] [Accepted: 06/16/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.
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Affiliation(s)
- Daniel M. Bean
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
| | - Ammar Al-Chalabi
- King′s College Hospital, Bessemer Road, Denmark Hill, Brixton, London SE5 9RS, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
| | - Richard J. B. Dobson
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Health Data Research UK London, University College London, 16 De Crespigny Park, London SE5 8AF, UK
- Institute of Health Informatics, University College London, 222 Euston Rd, London NW1 2DA, UK
| | - Alfredo Iacoangeli
- Department of Biostatistics & Health Informatics, King′s College London, 16 De Crespigny Park, London SE5 8AF, UK;
- Maurice Wohl Clinical Neuroscience Institute, Department of Basic and Clinical Neuroscience, King′s College London, London, 5 Cutcombe Rd, Brixton, London SE5 9RT, UK
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Davis KAS, Farooq S, Hayes JF, John A, Lee W, MacCabe JH, McIntosh A, Osborn DPJ, Stewart RJ, Woelbert E. Pharmacoepidemiology research: delivering evidence about drug safety and effectiveness in mental health. Lancet Psychiatry 2020; 7:363-370. [PMID: 31780306 DOI: 10.1016/s2215-0366(19)30298-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 07/19/2019] [Accepted: 07/29/2019] [Indexed: 12/13/2022]
Abstract
Research that provides an evidence base for the pharmacotherapy of people with mental disorders is needed. The abundance of digital data has facilitated pharmacoepidemiology and, in particular, observational research on the effectiveness of real-world medication. Advantages of pharmacoepidemiological research are the availability of large patient samples, and coverage of under-researched subpopulations in their naturalistic conditions. Such research is also cheaper and quicker to do than randomised controlled trials, meaning that issues regarding generic medication, stopping medication (deprescribing), and long-term outcomes are more likely to be addressed. Pharmacoepidemiological methods can also be extended to pharmacovigilance and to aid the development of new purposes for existing drugs. Drawbacks of observational pharmacoepidemiological studies come from the non-randomised nature of treatment selection, leading to confounding by indication. Potential methods for managing this drawback include active comparison groups, within-individual designs, and propensity scoring. Many of the more rigorous pharmacoepidemiology studies have been strengthened through multiple analytical approaches triangulated to improve confidence in inferred causal relationships. With developments in data resources and analytical techniques, it is encouraging that guidelines are beginning to include evidence from robust observational pharmacoepidemiological studies alongside randomised controlled trials. Collaboration between guideline writers and researchers involved in pharmacoepidemiology could help researchers to answer the questions that are important to policy makers and ensure that results are integrated into the evidence base. Further development of statistical and data science techniques, alongside public engagement and capacity building (data resources and researcher base), will be necessary to take full advantage of future opportunities.
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Affiliation(s)
- Katrina A S Davis
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK.
| | - Saeed Farooq
- Primary Care Centre Versus Arthritis, School of Primary, Community and Social Care, Keele University, Staffordshire, UK
| | - Joseph F Hayes
- Camden and Islington NHS Foundation Trust, London, UK; Division of Psychiatry, University College London, London, UK
| | - Ann John
- Health Data Research UK Institute of Health Informatics Research, Swansea University Medical School, Swansea, UK
| | - William Lee
- University of Exeter Medical School, Exeter, UK
| | - James H MacCabe
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Andrew McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - David P J Osborn
- Camden and Islington NHS Foundation Trust, London, UK; Division of Psychiatry, University College London, London, UK
| | - Robert J Stewart
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
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Trends and Features of the Applications of Natural Language Processing Techniques for Clinical Trials Text Analysis. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10062157] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Natural language processing (NLP) is an effective tool for generating structured information from unstructured data, the one that is commonly found in clinical trial texts. Such interdisciplinary research has gradually grown into a flourishing research field with accumulated scientific outputs available. In this study, bibliographical data collected from Web of Science, PubMed, and Scopus databases from 2001 to 2018 had been investigated with the use of three prominent methods, including performance analysis, science mapping, and, particularly, an automatic text analysis approach named structural topic modeling. Topical trend visualization and test analysis were further employed to quantify the effects of the year of publication on topic proportions. Topical diverse distributions across prolific countries/regions and institutions were also visualized and compared. In addition, scientific collaborations between countries/regions, institutions, and authors were also explored using social network analysis. The findings obtained were essential for facilitating the development of the NLP-enhanced clinical trial texts processing, boosting scientific and technological NLP-enhanced clinical trial research, and facilitating inter-country/region and inter-institution collaborations.
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Schrodt J, Dudchenko A, Knaup-Gregori P, Ganzinger M. Graph-Representation of Patient Data: a Systematic Literature Review. J Med Syst 2020; 44:86. [PMID: 32166501 PMCID: PMC7067737 DOI: 10.1007/s10916-020-1538-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 02/07/2020] [Indexed: 11/30/2022]
Abstract
Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.
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Affiliation(s)
- Jens Schrodt
- Institute for Medical Biometry and Informatics, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Aleksei Dudchenko
- Institute for Medical Biometry and Informatics, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.,School of Translational Information Technologies, ITMO University, Kronverksky Pr. 49, 197101, Saint-Petersburg, Russia
| | - Petra Knaup-Gregori
- Institute for Medical Biometry and Informatics, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Matthias Ganzinger
- Institute for Medical Biometry and Informatics, Heidelberg University, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Abstract
IMPORTANCE Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry. OBSERVATIONS The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given. CONCLUSIONS AND RELEVANCE Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.
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Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,OASIS Service, South London and Maudsley National Health Service Foundation Trust, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Ziad Hijazi
- Department of Medical Sciences, Cardiology, and Uppsala Clinical Research Center, Uppsala University, Uppsala University Hospital, Uppsala, Sweden
| | - Daniel Stahl
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Medical Statistics and Medical Decision Making, Leiden University Medical Center, Leiden, the Netherlands
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