1
|
Irrgang C, Eckmanns T, V Kleist M, Antão EM, Ladewig K, Wieler LH, Körber N. [Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2023:10.1007/s00103-023-03707-2. [PMID: 37140603 PMCID: PMC10157576 DOI: 10.1007/s00103-023-03707-2] [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: 11/30/2022] [Accepted: 04/21/2023] [Indexed: 05/05/2023]
Abstract
Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.
Collapse
Affiliation(s)
- Christopher Irrgang
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland.
| | - Tim Eckmanns
- FG 37: Nosokomiale Infektionen, Surveillance von Antibiotikaresistenz und -verbrauch, Robert Koch-Institut, Berlin, Deutschland
| | - Max V Kleist
- Fachbereich für Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland
- P5: Systemmedizin von Infektionskrankheiten, Robert Koch-Institut, Berlin, Deutschland
| | - Esther-Maria Antão
- Fachgebiet Digital Global Public Health, Hasso-Plattner-Institut, Potsdam, Deutschland
| | - Katharina Ladewig
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland
| | - Lothar H Wieler
- Robert Koch-Institut, Berlin, Deutschland
- Fachgebiet Digital Global Public Health, Hasso-Plattner-Institut, Potsdam, Deutschland
| | - Nils Körber
- Zentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland
| |
Collapse
|
2
|
Sakagianni A, Koufopoulou C, Feretzakis G, Kalles D, Verykios VS, Myrianthefs P, Fildisis G. Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review. Antibiotics (Basel) 2023; 12:antibiotics12030452. [PMID: 36978319 PMCID: PMC10044642 DOI: 10.3390/antibiotics12030452] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/30/2023] Open
Abstract
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.
Collapse
Affiliation(s)
| | - Christina Koufopoulou
- 1st Anesthesiology Department, Aretaieio Hospital, National and Kapodistrian University of Athens Medical School, 11528 Athens, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
- Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, 15126 Marousi, Greece
| | - Dimitris Kalles
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Vassilios S Verykios
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Pavlos Myrianthefs
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios Fildisis
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
| |
Collapse
|
3
|
Rabaan AA, Alhumaid S, Mutair AA, Garout M, Abulhamayel Y, Halwani MA, Alestad JH, Bshabshe AA, Sulaiman T, AlFonaisan MK, Almusawi T, Albayat H, Alsaeed M, Alfaresi M, Alotaibi S, Alhashem YN, Temsah MH, Ali U, Ahmed N. Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates. Antibiotics (Basel) 2022; 11:antibiotics11060784. [PMID: 35740190 PMCID: PMC9220767 DOI: 10.3390/antibiotics11060784] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/31/2022] [Accepted: 06/07/2022] [Indexed: 11/16/2022] Open
Abstract
Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.
Collapse
Affiliation(s)
- Ali A. Rabaan
- Molecular Diagnostic Laboratory, Johns Hopkins Aramco Healthcare, Dhahran 31311, Saudi Arabia
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
- Department of Public Health and Nutrition, The University of Haripur, Haripur 22610, Pakistan
- Correspondence: (A.A.R.); (N.A.)
| | - Saad Alhumaid
- Administration of Pharmaceutical Care, Al-Ahsa Health Cluster, Ministry of Health, Al-Ahsa 31982, Saudi Arabia;
| | - Abbas Al Mutair
- Research Center, Almoosa Specialist Hospital, Alhassa, Al-Ahsa 36342, Saudi Arabia;
- Almoosa College of Health Sciences, Alhassa, Al-Ahsa 36342, Saudi Arabia
- School of Nursing, Wollongong University, Wollongong, NSW 2522, Australia
- Nursing Department, Prince Sultan Military College of Health Sciences, Dhahran 34313, Saudi Arabia
| | - Mohammed Garout
- Department of Community Medicine and Health Care for Pilgrims, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia;
| | - Yem Abulhamayel
- Specialty Internal Medicine Department, Johns Hopkins Aramco Healthcare, Dhahran 34465, Saudi Arabia;
| | - Muhammad A. Halwani
- Department of Medical Microbiology, Faculty of Medicine, Al Baha University, Al Baha 4781, Saudi Arabia;
| | - Jeehan H. Alestad
- Immunology and Infectious Microbiology Department, University of Glasgow, Glasgow G1 1XQ, UK;
- Microbiology Department, Collage of Medicine, Jabriya 46300, Kuwait
| | - Ali Al Bshabshe
- Adult Critical Care Department of Medicine, Division of Adult Critical Care, College of Medicine, King Khalid University, Abha 62561, Saudi Arabia;
| | - Tarek Sulaiman
- Infectious Diseases Section, Medical Specialties Department, King Fahad Medical City, Riyadh 12231, Saudi Arabia;
| | | | - Tariq Almusawi
- Infectious Disease and Critical Care Medicine Department, Dr. Sulaiman Alhabib Medical Group, Alkhobar 34423, Saudi Arabia;
- Department of Medicine, Royal College of Surgeons in Ireland-Medical University of Bahrain, Manama 15503, Bahrain
| | - Hawra Albayat
- Infectious Disease Department, King Saud Medical City, Riyadh 7790, Saudi Arabia;
| | - Mohammed Alsaeed
- Infectious Disease Division, Department of Medicine, Prince Sultan Military Medical City, Riyadh 11159, Saudi Arabia;
| | - Mubarak Alfaresi
- Department of Pathology and Laboratory Medicine, Sheikh Khalifa General Hospital, Umm Al Quwain 499, United Arab Emirates;
- Department of Pathology, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai 505055, United Arab Emirates
| | - Sultan Alotaibi
- Molecular Microbiology Department, King Fahad Medical City, Riyadh 11525, Saudi Arabia;
| | - Yousef N. Alhashem
- Department of Clinical Laboratory Sciences, Mohammed AlMana College of Health Sciences, Dammam 34222, Saudi Arabia;
| | - Mohamad-Hani Temsah
- Pediatric Department, College of Medicine, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Urooj Ali
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore 54000, Pakistan;
| | - Naveed Ahmed
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia
- Correspondence: (A.A.R.); (N.A.)
| |
Collapse
|
4
|
Fadelelmoula AA. Specifications of a Queuing Model-Driven Decision Support System for Predicting the Healthcare Performance Indicators Pertaining to the Patient Flow. INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2022. [DOI: 10.4018/ijdsst.286676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article has developed specifications for a new model-driven decision support system (DSS) that aids the key stakeholders of public hospitals in estimating and tracking a set of crucial performance indicators pertaining to the patients flow. The developed specifications have considered several requirements for ensuring an effective system, including tracking the performance indicator on the level of the entire patients flow system, paying attention to the dynamic change of the values of the indicator’s parameters, and considering the heterogeneity of the patients. According to these requirements, the major components of the proposed system, which include a comprehensive object-based queuing model and an object-oriented database, have been specified. In addition to these components, the system comprises the equations that produce the required predictions. From the system output perspective, these predictions act as a foundation for evaluating the performance indicators as well as developing policies for managing the patients flow in the public hospitals.
Collapse
|
5
|
|
6
|
Methodical Aspects of MCDM Based E-Commerce Recommender System. JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH 2021. [DOI: 10.3390/jtaer16060122] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The aim of this paper is to present the use of an innovative approach based on MCDM methods as the main component of a consumer Decision Support System (DSS) by recommending the most suitable products among a given set of alternatives. This system provides a reliable recommendation to the consumer in the form of a compromise ranking constructed from the five MCDM methods: the hybrid approach TOPSIS-COMET, COCOSO, EDAS, MAIRCA, and MABAC. Each of the methods used contributes significantly to the final compromise ranking built with the Copeland strategy. Chosen MCDM methods were combined with the objective CRITIC weighting method, and their performance was presented on the illustrative example of choosing the most suitable mobile phone. A sensitivity analysis involving the rw and WS correlation coefficients was performed to determine the match between the compromise ranking of the candidates and the rankings provided by each MCDM method. Sensitivity analysis demonstrated that all investigated compromise candidate rankings show high convergence with the rankings provided by the particular MCDM methods. Thus, the performed study proved that the proposed approach shows high potential to be successfully used as a central component of DSS for recommending the most suitable product. Such DSS could be a universal and future-proof solution for e-commerce sites and websites, providing advanced product comparison capabilities in delivering a recommendation to the user as a final ranking of alternatives.
Collapse
|
7
|
Vandenberg O, Martiny D, Rochas O, van Belkum A, Kozlakidis Z. Considerations for diagnostic COVID-19 tests. Nat Rev Microbiol 2021; 19:171-183. [PMID: 33057203 PMCID: PMC7556561 DOI: 10.1038/s41579-020-00461-z] [Citation(s) in RCA: 447] [Impact Index Per Article: 149.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2020] [Indexed: 02/07/2023]
Abstract
During the early phase of the coronavirus disease 2019 (COVID-19) pandemic, design, development, validation, verification and implementation of diagnostic tests were actively addressed by a large number of diagnostic test manufacturers. Hundreds of molecular tests and immunoassays were rapidly developed, albeit many still await clinical validation and formal approval. In this Review, we summarize the crucial role of diagnostic tests during the first global wave of COVID-19. We explore the technical and implementation problems encountered during this early phase in the pandemic, and try to define future directions for the progressive and better use of (syndromic) diagnostics during a possible resurgence of COVID-19 in future global waves or regional outbreaks. Continuous global improvement in diagnostic test preparedness is essential for more rapid detection of patients, possibly at the point of care, and for optimized prevention and treatment, in both industrialized countries and low-resource settings.
Collapse
Affiliation(s)
- Olivier Vandenberg
- Innovation and Business Development Unit, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel, Université Libre de Bruxelles, Brussels, Belgium.
- Center for Environmental Health and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium.
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, UK.
| | - Delphine Martiny
- Department of Microbiology, Laboratoire Hospitalier Universtaire de Bruxelles - Universitair Laboratorium Brussel, Université Libre de Bruxelles, Brussels, Belgium
| | - Olivier Rochas
- Strategic Intelligence, Corporate Business Development, bioMérieux, Chemin de L'Orme, France
| | - Alex van Belkum
- Open Innovation and Partnerships, bioMérieux, La Balme Les Grottes, France.
| | - Zisis Kozlakidis
- Laboratory Services and Biobank Group, International Agency for Research on Cancer, World Health Organization, Lyon, France
| |
Collapse
|
8
|
Dutey-Magni PF, Gill MJ, McNulty D, Sohal G, Hayward A, Shallcross L, Anderson N, Crayton E, Forbes G, Jhass A, Richardson E, Richardson M, Rockenschaub P, Smith C, Sutton E, Traina R, Atkins L, Conolly A, Denaxas S, Fragaszy E, Horne R, Kostkova P, Lorencatto F, Michie S, Mindell J, Robson J, Royston C, Tarrant C, Thomas J, West J, Williams H, Elsay N, Fuller C. Feasibility study of hospital antimicrobial stewardship analytics using electronic health records. JAC Antimicrob Resist 2021; 3:dlab018. [PMID: 34223095 PMCID: PMC8210026 DOI: 10.1093/jacamr/dlab018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 01/27/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Hospital antimicrobial stewardship (AMS) programmes are multidisciplinary initiatives to optimize antimicrobial use. Most hospitals depend on time-consuming manual audits to monitor clinicians' prescribing. But much of the information needed could be sourced from electronic health records (EHRs). OBJECTIVES To develop an informatics methodology to analyse characteristics of hospital AMS practice using routine electronic prescribing and laboratory records. METHODS Feasibility study using electronic prescribing, laboratory and clinical coding records from adult patients admitted to six specialities at Queen Elizabeth Hospital, Birmingham, UK (September 2017-August 2018). The study involved: (i) a review of AMS standards of care; (ii) their translation into concepts measurable from commonly available EHRs; and (iii) a pilot application in an EHR cohort study (n = 61679 admissions). RESULTS We developed data modelling methods to characterize antimicrobial use (antimicrobial therapy episode linkage methods, therapy table, therapy changes). Prescriptions were linked into antimicrobial therapy episodes (mean 2.4 prescriptions/episode; mean length of therapy 5.8 days), enabling several actionable findings. For example, 22% of therapy episodes for low-severity community-acquired pneumonia were congruent with prescribing guidelines, with a tendency to use broader-spectrum antibiotics. Analysis of therapy changes revealed IV to oral therapy switching was delayed by an average 3.6 days (95% CI: 3.4-3.7). Microbial cultures were performed prior to treatment initiation in just 22% of antibacterial prescriptions. The proposed methods enabled fine-grained monitoring of AMS practice down to specialities, wards and individual clinical teams by case mix, enabling more meaningful peer comparison. CONCLUSIONS It is feasible to use hospital EHRs to construct rapid, meaningful measures of prescribing quality with potential to support quality improvement interventions (audit/feedback to prescribers), engagement with front-line clinicians on optimizing prescribing, and AMS impact evaluation studies.
Collapse
Affiliation(s)
- P F Dutey-Magni
- Institute of Health Informatics, University College London, London, UK
| | - M J Gill
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - D McNulty
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - G Sohal
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - A Hayward
- Institute of Epidemiology & Health Care, University College London, London, UK
| | - L Shallcross
- Institute of Health Informatics, University College London, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Lv J, Deng S, Zhang L. A review of artificial intelligence applications for antimicrobial resistance. BIOSAFETY AND HEALTH 2021. [DOI: 10.1016/j.bsheal.2020.08.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
|
10
|
AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10031040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.
Collapse
|
11
|
Comprehensive analysis of rule formalisms to represent clinical guidelines: Selection criteria and case study on antibiotic clinical guidelines. Artif Intell Med 2020; 103:101741. [PMID: 31928849 DOI: 10.1016/j.artmed.2019.101741] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 10/02/2019] [Accepted: 10/04/2019] [Indexed: 12/29/2022]
Abstract
BACKGROUND The over-use of antibiotics in clinical domains is causing an alarming increase in bacterial resistance, thus endangering their effectiveness as regards the treatment of highly recurring severe infectious diseases. Whilst Clinical Guidelines (CGs) focus on the correct prescription of antibiotics in a narrative form, Clinical Decision Support Systems (CDSS) operationalize the knowledge contained in CGs in the form of rules at the point of care. Despite the efforts made to computerize CGs, there is still a gap between CGs and the myriad of rule technologies (based on different logic formalisms) that are available to implement CDSSs in real clinical settings. OBJECTIVE To helpCDSS designers to determine the most suitable rule-based technology (medical-oriented rules, production rules and semantic web rules) with which to model knowledge from CGs for the prescription of antibiotics. We propose a framework of criteria for this purpose that is extensible to more generic CGs. MATERIALS AND METHODS Our proposal is based on the identification of core technical requirements extracted from both literature and the analysis of CGs for antibiotics, establishing three dimensions for analysis: language expressivity, interoperability and industrial aspects. We present a case study regarding the John Hopkins Hospital (JHH) Antibiotic Guidelines for Urinary Tract Infection (UTI), a highly recurring hospital acquired infection. We have adopted our framework of criteria in order to analyse and implement these CGs using various rule technologies: HL7 Arden Syntax, general-purpose Production Rules System (Drools), HL7 standard Rule Interchange Format (RIF), Semantic Web Rule Language (SWRL) and SParql Inference Notation (SPIN) rule extensions (implementing our own ontology for UTI). RESULTS We have identified the main criteria required to attain a maintainable and cost-affordable computable knowledge representation for CGs. We have represented the JHH UTI CGs knowledge in a total of 12 Arden Syntax MLMs, 81 Drools rules and 154 ontology classes, properties and individuals. Our experiments confirm the relevance of the proposed set of criteria and show the level of compliance of the different rule technologies with the JHH UTI CGs knowledge representation. CONCLUSIONS The proposed framework of criteria may help clinical institutions to select the most suitable rule technology for the representation of CGs in general, and for the antibiotic prescription domain in particular, depicting the main aspects that lead to Computer Interpretable Guidelines (CIGs), such as Logic expressivity (Open/Closed World Assumption, Negation-As-Failure), Temporal Reasoning and Interoperability with existing HIS and clinical workflow. Future work will focus on providing clinicians with suggestions regarding new potential steps for CGs, considering process mining approaches and CGs Process Workflows, the use of HL7 FHIR for HIS interoperability and the representation of Knowledge-as- a-Service (KaaS).
Collapse
|
12
|
Cánovas-Segura B, Morales A, Juarez JM, Campos M, Palacios F. Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems. J Biomed Inform 2019; 94:103200. [DOI: 10.1016/j.jbi.2019.103200] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 11/29/2022]
|
13
|
A lightweight acquisition of expert rules for interoperable clinical decision support systems. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.01.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
14
|
Martinez-Carrasco AL, Juarez JM, Campos M, Morales A, Palacios F, Lopez-Rodriguez L. Interpretable Patient Subgrouping Using Trace-Based Clustering. Artif Intell Med 2019. [DOI: 10.1007/978-3-030-21642-9_33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
15
|
Morales A, Campos M, Juarez JM, Canovas-Segura B, Palacios F, Marin R. A decision support system for antibiotic prescription based on local cumulative antibiograms. J Biomed Inform 2018; 84:114-122. [PMID: 29981885 DOI: 10.1016/j.jbi.2018.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 06/12/2018] [Accepted: 07/04/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Local cumulative antibiograms are useful tools with which to select appropriate empiric or directed therapies when treating infectious diseases at a hospital. However, data represented in traditional antibiograms are static, incomplete and not well adapted to decision-making. METHODS We propose a decision support method for empiric antibiotic therapy based on the Number Needed to Fail (NNF) measure. NNF indicates the number of patients that would need to be treated with a specific antibiotic for one to be inadequately treated. We define two new measures, Accumulated Efficacy and Weighted Accumulated Efficacy in order to determine the efficacy of an antibiotic. We carried out two experiments: the first during which there was a suspicion of infection and the patient had empiric therapy, and the second by considering patients with confirmed infection and directed therapy. The study was performed with 15,799 cultures with 356,404 susceptibility tests carried out over a four-year period. RESULTS The most efficient empiric antibiotics are Linezolid and Vancomycin for blood samples and Imipenem and Meropenem for urine samples. In both experiments, the efficacies of recommended antibiotics are all significantly greater than the efficacies of the antibiotics actually administered (P < 0.001). The highest efficacy is obtained when considering 2 years of antibiogram data and 80% of the cumulated prevalence of microorganisms. CONCLUSION This extensive study on real empiric therapies shows that the proposed method is a valuable alternative to traditional antibiograms as regards developing clinical decision support systems for antimicrobial stewardship.
Collapse
Affiliation(s)
- Antonio Morales
- Department of Informatics and Systems, University of Murcia, Facultad de Informatica, Campus de Espinardo, 30100 Murcia, Spain.
| | - Manuel Campos
- Department of Informatics and Systems, University of Murcia, Facultad de Informatica, Campus de Espinardo, 30100 Murcia, Spain.
| | - Jose M Juarez
- Department of Information and Communications Engineering, University of Murcia, Facultad de Informatica, Campus de Espinardo, 30100 Murcia, Spain.
| | - Bernardo Canovas-Segura
- Department of Informatics and Systems, University of Murcia, Facultad de Informatica, Campus de Espinardo, 30100 Murcia, Spain.
| | - Francisco Palacios
- Intensive Care Unit, University Hospital of Getafe. Carretera de Toledo Km 12, 500, 28905 Getafe (Madrid), Spain.
| | - Roque Marin
- Department of Information and Communications Engineering, University of Murcia, Facultad de Informatica, Campus de Espinardo, 30100 Murcia, Spain.
| |
Collapse
|
16
|
A Methodological Framework for the Integrated Design of Decision-Intensive Care Pathways—an Application to the Management of COPD Patients. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2017; 1:157-217. [DOI: 10.1007/s41666-017-0007-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 09/21/2017] [Accepted: 10/02/2017] [Indexed: 12/23/2022]
|