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Filigheddu MT, Leonelli M, Varando G, Gómez-Bermejo MÁ, Ventura-Díaz S, Gorospe L, Fortún J. Using staged tree models for health data: Investigating invasive fungal infections by aspergillus and other filamentous fungi. Comput Struct Biotechnol J 2024; 24:12-22. [PMID: 38144574 PMCID: PMC10746417 DOI: 10.1016/j.csbj.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/07/2023] [Accepted: 11/07/2023] [Indexed: 12/26/2023] Open
Abstract
Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in understanding health outcomes. In this paper, we showcase the use of machine-learned staged tree models for investigating complex asymmetric dependence structures in health data. Staged trees are a specific class of generative, probabilistic graphical models that formally model asymmetric conditional independence and non-regular sample spaces. An investigation of the risk factors in invasive fungal infections demonstrates the insights staged trees provide to support medical decision-making.
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Affiliation(s)
- Maria Teresa Filigheddu
- Infectious Diseases Department, Hospital Ramón y Cajal, IRYCIS (Instituto Ramón y Cajal de Investigación Sanitaria); Universidad de Alcalá, Madrid, Spain
| | | | - Gherardo Varando
- Image Processing Laboratory (IPL), Universitat de València, Valencia, Spain
| | | | - Sofía Ventura-Díaz
- Radiology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Luis Gorospe
- Radiology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
| | - Jesús Fortún
- Infectious Diseases Department, Hospital Ramón y Cajal, IRYCIS (Instituto Ramón y Cajal de Investigación Sanitaria); Universidad de Alcalá, Madrid, Spain
- Microbiology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
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2
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Yirdaw BE, Debusho LK. Multilevel Bayesian network to model child morbidity using Gibbs sampling. Artif Intell Med 2024; 149:102784. [PMID: 38462284 DOI: 10.1016/j.artmed.2024.102784] [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: 07/04/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 03/12/2024]
Abstract
Bayesian networks (BNs) are suitable models for studying complex interdependencies between multiple health outcomes, simultaneously. However, these models fail the assumption of independent observation in the case of hierarchical data. Therefore, this study proposes a two and three-level random intercept multilevel Bayesian network (MBN) models to study the conditional dependencies between multiple outcomes. The structure of MBN was learned using the connected three parent set block Gibbs sampler, where each local network was included based on Bayesian information criteria (BIC) score of multilevel regression. These models were examined using simulated data assuming features of both multilevel models and BNs. The estimated area under the receiver operating characteristics for both models were above 0.8, indicating good fit. The MBN was then applied to real child morbidity data from the 2016 Ethiopian Demographic Health Survey (EDHS). The result shows a complex causal dependencies between malnutrition indicators and child morbidities such as anemia, acute respiratory infection (ARI) and diarrhea. According to this result, families and health professionals should give special attention to children who suffer from malnutrition and also have one of these illnesses, as the co-occurrence of both can worsen the health of a child.
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Affiliation(s)
- Bezalem Eshetu Yirdaw
- Department of Statistics, University of South Africa, c/o Christiaan de Wet Road & Pioneer Avenue, Florida 1709, Johannesburg, South Africa.
| | - Legesse Kassa Debusho
- Department of Statistics, University of South Africa, c/o Christiaan de Wet Road & Pioneer Avenue, Florida 1709, Johannesburg, South Africa.
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3
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Howell MD, Corrado GS, DeSalvo KB. Three Epochs of Artificial Intelligence in Health Care. JAMA 2024; 331:242-244. [PMID: 38227029 DOI: 10.1001/jama.2023.25057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Importance Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content. Conclusions and Relevance Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.
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Howard A, Aston S, Gerada A, Reza N, Bincalar J, Mwandumba H, Butterworth T, Hope W, Buchan I. Antimicrobial learning systems: an implementation blueprint for artificial intelligence to tackle antimicrobial resistance. Lancet Digit Health 2024; 6:e79-e86. [PMID: 38123255 DOI: 10.1016/s2589-7500(23)00221-2] [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: 08/29/2023] [Revised: 10/17/2023] [Accepted: 10/21/2023] [Indexed: 12/23/2023]
Abstract
The proliferation of various forms of artificial intelligence (AI) brings many opportunities to improve health care. AI models can harness complex evolving data, inform and augment human actions, and learn from health outcomes such as morbidity and mortality. The global public health challenge of antimicrobial resistance (AMR) needs large-scale optimisation of antimicrobial use and wider infection care, which could be enabled by carefully constructed AI models. As AI models become increasingly useful and robust, health-care systems remain challenging places for their deployment. An implementation gap exists between the promise of AI models and their use in patient and population care. Here, we outline an adaptive implementation and maintenance framework for AI models to improve antimicrobial use and infection care as a learning system. The roles of AMR problem identification, law and regulation, organisational support, data processing, and AI development, assessment, maintenance, and scalability in the implementation of AMR-targeted AI models are considered.
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Affiliation(s)
- Alex Howard
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
| | - Stephen Aston
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Alessandro Gerada
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Nada Reza
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Jason Bincalar
- Department of Health Data Science, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Henry Mwandumba
- Malawi Liverpool Wellcome Programme, Kamuzu University of Health Sciences, Blantyre, Malawi; Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Tom Butterworth
- Combined Intelligence for Public Health Action, NHS Cheshire and Merseyside, Warrington, UK
| | - William Hope
- Department of Antimicrobial Pharmacodynamics and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Iain Buchan
- Department of Public Health, Policy and Systems, Institute of Population Health, University of Liverpool, Liverpool, UK; Combined Intelligence for Public Health Action, NHS Cheshire and Merseyside, Warrington, UK
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5
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Ma SX, Dhanaliwala AH, Rudie JD, Rauschecker AM, Roberts-Wolfe D, Haddawy P, Kahn CE. Bayesian Networks in Radiology. Radiol Artif Intell 2023; 5:e210187. [PMID: 38074791 PMCID: PMC10698603 DOI: 10.1148/ryai.210187] [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: 07/08/2021] [Revised: 06/13/2023] [Accepted: 09/14/2023] [Indexed: 06/22/2024]
Abstract
A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values. Bayesian networks can learn their structure (nodes and connections) and/or conditional probability values from data. Bayesian networks offer several advantages: (a) they can efficiently perform complex inferences, (b) reason from cause to effect or vice versa, (c) assess counterfactual data, (d) integrate observations with canonical ("textbook") knowledge, and (e) explain their reasoning. Bayesian networks have been employed in a wide variety of applications in radiology, including diagnosis and treatment planning. Unlike deep learning approaches, Bayesian networks have not been applied to computer vision. However, hybrid artificial intelligence systems have combined deep learning models with Bayesian networks, where the deep learning model identifies findings in medical images and the Bayesian network formulates and explains a diagnosis from those findings. One can apply a Bayesian network's probabilistic knowledge to integrate clinical and imaging findings to support diagnosis, treatment planning, and clinical decision-making. This article reviews the fundamental principles of Bayesian networks and summarizes their applications in radiology. Keywords: Bayesian Network, Machine Learning, Abdominal Imaging, Musculoskeletal Imaging, Breast Imaging, Neurologic Imaging, Radiology Education Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Shawn X. Ma
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Ali H. Dhanaliwala
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Jeffrey D. Rudie
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Andreas M. Rauschecker
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Douglas Roberts-Wolfe
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Peter Haddawy
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
| | - Charles E. Kahn
- From the Department of Radiology (S.X.M., A.H.D., D.R.F., C.E.K.) and
Institute for Biomedical Informatics (C.E.K.), University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104; Department of Radiology, Scripps Clinic, La
Jolla, Calif (J.D.R.); Department of Radiology, University of California San
Diego, La Jolla, Calif (J.D.R.); Department of Radiology and Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (A.M.R.); Faculty
of Information and Communication Technology, Mahidol University, Bangkok,
Thailand (P.H.); and Bremen Spatial Cognition Center, University of Bremen,
Bremen, Germany (P.H.)
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6
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Ordovas JM, Rios-Insua D, Santos-Lozano A, Lucia A, Torres A, Kosgodagan A, Camacho JM. A Bayesian network model for predicting cardiovascular risk. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107405. [PMID: 36796167 DOI: 10.1016/j.cmpb.2023.107405] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 01/17/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascular diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations. METHODS We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions. RESULTS The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision- support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners' use. CONCLUSIONS Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.
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Affiliation(s)
- J M Ordovas
- Nutrition and Genomics, JM-USDA-HNRCA, Tufts University, Boston, MASS, USA
| | | | - A Santos-Lozano
- i+Health, Dpt. Health Sciences, European University Miguel de Cervantes, Valladolid, Spain; Physical Activity and Health Research Group, Inst. Inv. Sanitaria Hospital 12 de Octubre, Madrid, Spain
| | - A Lucia
- Fac. Sports Sciences, Universidad Europea de Madrid, Madrid, Spain; Physical Activity and Health Research Group, Inst. Inv. Sanitaria Hospital 12 de Octubre, Madrid, Spain
| | | | - A Kosgodagan
- Inst. de Mathématiques Apliqueés, Université Catholique de L'Ouest, Angers, France
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7
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Wu Y, Mascaro S, Bhuiyan M, Fathima P, Mace AO, Nicol MP, Richmond PC, Kirkham LA, Dymock M, Foley DA, McLeod C, Borland ML, Martin A, Williams PCM, Marsh JA, Snelling TL, Blyth CC. Predicting the causative pathogen among children with pneumonia using a causal Bayesian network. PLoS Comput Biol 2023; 19:e1010967. [PMID: 36913404 PMCID: PMC10035934 DOI: 10.1371/journal.pcbi.1010967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 03/23/2023] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
BACKGROUND Pneumonia remains a leading cause of hospitalization and death among young children worldwide, and the diagnostic challenge of differentiating bacterial from non-bacterial pneumonia is the main driver of antibiotic use for treating pneumonia in children. Causal Bayesian networks (BNs) serve as powerful tools for this problem as they provide clear maps of probabilistic relationships between variables and produce results in an explainable way by incorporating both domain expert knowledge and numerical data. METHODS We used domain expert knowledge and data in combination and iteratively, to construct, parameterise and validate a causal BN to predict causative pathogens for childhood pneumonia. Expert knowledge elicitation occurred through a series of group workshops, surveys and one-on-one meetings involving 6-8 experts from diverse domain areas. The model performance was evaluated based on both quantitative metrics and qualitative expert validation. Sensitivity analyses were conducted to investigate how the target output is influenced by varying key assumptions of a particularly high degree of uncertainty around data or domain expert knowledge. RESULTS Designed to apply to a cohort of children with X-ray confirmed pneumonia who presented to a tertiary paediatric hospital in Australia, the resulting BN offers explainable and quantitative predictions on a range of variables of interest, including the diagnosis of bacterial pneumonia, detection of respiratory pathogens in the nasopharynx, and the clinical phenotype of a pneumonia episode. Satisfactory numeric performance has been achieved including an area under the receiver operating characteristic curve of 0.8 in predicting clinically-confirmed bacterial pneumonia with sensitivity 88% and specificity 66% given certain input scenarios (i.e., information that is available and entered into the model) and trade-off preferences (i.e., relative weightings of the consequences of false positive versus false negative predictions). We specifically highlight that a desirable model output threshold for practical use is very dependent upon different input scenarios and trade-off preferences. Three commonly encountered scenarios were presented to demonstrate the potential usefulness of the BN outputs in various clinical pictures. CONCLUSIONS To our knowledge, this is the first causal model developed to help determine the causative pathogen for paediatric pneumonia. We have shown how the method works and how it would help decision making on the use of antibiotics, providing insight into how computational model predictions may be translated to actionable decisions in practice. We discussed key next steps including external validation, adaptation and implementation. Our model framework and the methodological approach can be adapted beyond our context to broad respiratory infections and geographical and healthcare settings.
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Affiliation(s)
- Yue Wu
- Sydney School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | - Steven Mascaro
- Bayesian Intelligence Pty Ltd, Upwey, Victoria, Australia
- Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Mejbah Bhuiyan
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | - Parveen Fathima
- Sydney School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
| | - Ariel O Mace
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- Department of General Paediaitrics, Perth Children's Hospital, Nedlands, Western Australia, Australia
- Department of Paediatrics, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Mark P Nicol
- School of Biomedical Sciences, University of Western Australia, Crawley, Western Australia, Australia
| | - Peter C Richmond
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- Department of General Paediaitrics, Perth Children's Hospital, Nedlands, Western Australia, Australia
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
| | - Lea-Ann Kirkham
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | - Michael Dymock
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | - David A Foley
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- Microbiology, PathWest Laboratory Medicine QEII Medical Centre, Nedlands, Western Australia, Australia
| | - Charlie McLeod
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- Infectious Diseases Department, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Meredith L Borland
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
- Emergency Department, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Andrew Martin
- Department of General Paediaitrics, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Phoebe C M Williams
- Sydney School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
- Sydney Children's Hospitals Network, New South Wales, Australia
- School of Women's and Children's Health, The University of New South Wales, Kensington, New South Wales, Australia
| | - Julie A Marsh
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
| | - Thomas L Snelling
- Sydney School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- Sydney Children's Hospitals Network, New South Wales, Australia
- School of Public Health, Curtin University, Bentley, Western Australia, Australia
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Christopher C Blyth
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, Western Australia, Australia
- School of Medicine, University of Western Australia, Crawley, Western Australia, Australia
- Microbiology, PathWest Laboratory Medicine QEII Medical Centre, Nedlands, Western Australia, Australia
- Infectious Diseases Department, Perth Children's Hospital, Nedlands, Western Australia, Australia
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8
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Kwisthout J. Motivating explanations in Bayesian networks using MAP-independence. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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9
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Harris CS, Dodd M, Kober KM, Dhruva AA, Hammer M, Conley YP, Miaskowski CA. Advances in Conceptual and Methodological Issues in Symptom Cluster Research: A 20-Year Perspective. ANS Adv Nurs Sci 2022; 45:309-322. [PMID: 35502915 PMCID: PMC9616968 DOI: 10.1097/ans.0000000000000423] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Two conceptual approaches are used to evaluate symptom clusters: "clustering" symptoms (ie, variable-centered analytic approach) and "clustering" patients (ie, person-centered analytic approach). However, these methods are not used consistently and conceptual clarity is needed. Given the emergence of novel methods to evaluate symptom clusters, a review of the conceptual basis for older and newer analytic methods is warranted. Therefore, this article will review the conceptual basis for symptom cluster research; compare and contrast the conceptual basis for using variable-centered versus patient-centered analytic approaches in symptom cluster research; review their strengths and weaknesses; and compare their applications in symptom cluster research.
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Affiliation(s)
| | - Marylin Dodd
- School of Nursing, University of California, San Francisco, CA, USA
| | - Kord M. Kober
- School of Nursing, University of California, San Francisco, CA, USA
| | - Anand A. Dhruva
- School of Medicine, University of California, San Francisco, CA, USA
| | | | - Yvette P. Conley
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christine A. Miaskowski
- School of Nursing, University of California, San Francisco, CA, USA
- School of Medicine, University of California, San Francisco, CA, USA
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10
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Taschler B, Smith SM, Nichols TE. Causal inference on neuroimaging data with Mendelian randomisation. Neuroimage 2022; 258:119385. [PMID: 35714886 PMCID: PMC10933777 DOI: 10.1016/j.neuroimage.2022.119385] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/30/2022] [Accepted: 06/12/2022] [Indexed: 10/18/2022] Open
Abstract
While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods based on Mendelian randomisation, with examples involving imaging-derived phenotypes from UK Biobank to make these methods accessible to neuroimaging researchers. We motivate the use of MR techniques, lay out the underlying assumptions, introduce common MR methods and focus on several scenarios in which modelling assumptions are potentially violated, resulting in biased effect estimates. Importantly, we give a detailed account of necessary steps to increase the reliability of MR results with rigorous sensitivity analyses.
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Affiliation(s)
- Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, City Oxford, UK
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Ramsay JA, Mascaro S, Campbell AJ, Foley DA, Mace AO, Ingram P, Borland ML, Blyth CC, Larkins NG, Robertson T, Williams PCM, Snelling TL, Wu Y. Urinary tract infections in children: building a causal model-based decision support tool for diagnosis with domain knowledge and prospective data. BMC Med Res Methodol 2022; 22:218. [PMID: 35941543 PMCID: PMC9358867 DOI: 10.1186/s12874-022-01695-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/21/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Diagnosing urinary tract infections (UTIs) in children in the emergency department (ED) is challenging due to the variable clinical presentations and difficulties in obtaining a urine sample free from contamination. Clinicians need to weigh a range of observations to make timely diagnostic and management decisions, a difficult task to achieve without support due to the complex interactions among relevant factors. Directed acyclic graphs (DAG) and causal Bayesian networks (BN) offer a way to explicitly outline the underlying disease, contamination and diagnostic processes, and to further make quantitative inference on the event of interest thus serving as a tool for decision support. METHODS We prospectively collected data on children present to ED with suspected UTIs. Through knowledge elicitation workshops and one-on-one meetings, a DAG was co-developed with clinical domain experts (the Expert DAG) to describe the causal relationships among variables relevant to paediatric UTIs. The Expert DAG was combined with prospective data and further domain knowledge to inform the development of an application-oriented BN (the Applied BN), designed to support the diagnosis of UTI. We assessed the performance of the Applied BN using quantitative and qualitative methods. RESULTS We summarised patient background, clinical and laboratory characteristics of 431 episodes of suspected UTIs enrolled from May 2019 to November 2020. The Expert DAG was presented with a narrative description, elucidating how infection, specimen contamination and management pathways causally interact to form the complex picture of paediatric UTIs. Parameterised using prospective data and expert-elicited parameters, the Applied BN achieved an excellent and stable performance in predicting Escherichia coli culture results, with a mean area under the receiver operating characteristic curve of 0.86 and a mean log loss of 0.48 based on 10-fold cross-validation. The BN predictions were reviewed via a validation workshop, and we illustrate how they can be presented for decision support using three hypothetical clinical scenarios. CONCLUSION Causal BNs created from both expert knowledge and data can integrate case-specific information to provide individual decision support during the diagnosis of paediatric UTIs in ED. The model aids the interpretation of culture results and the diagnosis of UTIs, promising the prospect of improved patient care and judicious use of antibiotics.
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Affiliation(s)
- Jessica A Ramsay
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia
| | - Steven Mascaro
- Bayesian Intelligence Pty Ltd, Upwey, VIC, 3158, Australia.,Faculty of Information Technology, Monash University, Clayton, VIC, 3168, Australia
| | - Anita J Campbell
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia.,Department of Infectious Diseases, Perth Children's Hospital, Nedlands, WA, 6009, Australia
| | - David A Foley
- Department of Microbiology, PathWest Laboratory Medicine, Nedlands, WA, 6009, Australia
| | - Ariel O Mace
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia.,Department of General Paediatrics, Perth Children's Hospital, Nedlands, WA, 6009, Australia
| | - Paul Ingram
- Department of Microbiology, PathWest Laboratory Medicine, Nedlands, WA, 6009, Australia.,School of Pathology and Laboratory Medicine, University of Western Australia, Nedlands, WA, 6009, Australia
| | - Meredith L Borland
- Emergency Department, Perth Children's Hospital, Nedlands, WA, 6009, Australia.,Divisions of Emergency Medicine and Paediatrics, School of Medicine, University of Western Australia, Nedlands, WA, 6009, Australia
| | - Christopher C Blyth
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia.,Department of Infectious Diseases, Perth Children's Hospital, Nedlands, WA, 6009, Australia.,Department of Microbiology, PathWest Laboratory Medicine, Nedlands, WA, 6009, Australia.,Faculty of Health and Medical Sciences, University of Western Australia, Crawley, Australia
| | - Nicholas G Larkins
- Department of Nephrology, Perth Children's Hospital, Nedlands, WA, 6009, Australia
| | - Tim Robertson
- Child and Adolescent Health Service, Perth Children's Hospital, Nedlands, WA, 6009, Australia
| | - Phoebe C M Williams
- Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, 2006, Camperdown, NSW , Australia.,Sydney Children's Hospital Network, Randwick, NSW, 2031, Australia.,School of Women's and Children's Health, The University of New South Wales, Sydney, NSW, 2052, Australia
| | - Thomas L Snelling
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia.,Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, 2006, Camperdown, NSW , Australia.,Sydney Children's Hospital Network, Randwick, NSW, 2031, Australia.,School of Public Health, Curtin University, Bentley, WA, 6102, Australia.,Menzies School of Health Research, Charles Darwin University, Darwin, NT, 0815, Australia
| | - Yue Wu
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, University of Western Australia, Nedlands, WA, 6009, Australia. .,Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, 2006, Camperdown, NSW , Australia.
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Kyrimi E, Neves MR, McLachlan S, Neil M, Marsh W, Fenton N. Medical idioms for clinical Bayesian network development. J Biomed Inform 2020; 108:103495. [PMID: 32619692 DOI: 10.1016/j.jbi.2020.103495] [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: 08/09/2019] [Revised: 05/07/2020] [Accepted: 06/24/2020] [Indexed: 01/17/2023]
Abstract
Bayesian Networks (BNs) are graphical probabilistic models that have proven popular in medical applications. While numerous medical BNs have been published, most are presented fait accompli without explanation of how the network structure was developed or justification of why it represents the correct structure for the given medical application. This means that the process of building medical BNs from experts is typically ad hoc and offers little opportunity for methodological improvement. This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs. The proposed method complements and extends the idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We propose instances of their generic idioms that are specific to medical BNs. We refer to the proposed medical reasoning patterns as medical idioms. In addition, we extend the use of idioms to represent interventional and counterfactual reasoning. We believe that the proposed medical idioms are logical reasoning patterns that can be combined, reused and applied generically to help develop medical BNs. All proposed medical idioms have been illustrated using medical examples on coronary artery disease. The method has also been applied to other ongoing BNs being developed with medical experts. Finally, we show that applying the proposed medical idioms to published BN models results in models with a clearer structure.
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Affiliation(s)
- Evangelia Kyrimi
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.
| | - Mariana Raniere Neves
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | - Scott McLachlan
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | - Martin Neil
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | - William Marsh
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
| | - Norman Fenton
- Risk and Information Management Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
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