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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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Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [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: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
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Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
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Bellmann L, Wiederhold AJ, Trübe L, Twerenbold R, Ückert F, Gottfried K. Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data. JMIR Med Inform 2024; 12:e49865. [PMID: 39046780 PMCID: PMC11306949 DOI: 10.2196/49865] [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: 06/12/2023] [Revised: 10/11/2023] [Accepted: 05/04/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. OBJECTIVE This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. METHODS We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. RESULTS We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. CONCLUSIONS The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.
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Affiliation(s)
- Louis Bellmann
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Leona Trübe
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Center for Cardiovascular Research (DZHK) Partner Site Hamburg-Kiel-Lübeck, Hamburg, Germany
- University Center of Cardiovascular Science, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Frank Ückert
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Karl Gottfried
- Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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4
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Bai K, Yang L, Xue J, Zhao L, Hao F. Pathogenicity classification of missense mutations based on deep generative model. Comput Biol Med 2024; 170:107980. [PMID: 38242017 DOI: 10.1016/j.compbiomed.2024.107980] [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: 10/02/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 01/21/2024]
Abstract
Missense mutations affect the function of human proteins and are closely associated with multiple acute and chronic diseases. The identification of disease-associated missense mutations and their classification for pathogenicity can provide insights into the genetic basis of disease and protein function. This paper proposes MLAE (Method based on LSTM-Ladder AutoEncoder), a deep learning classification model for identifying disease-associated missense mutations and classifying their pathogenicity based on the Variational AutoEncoder (VAE) framework. MLAE overcomes the limitations of the VAE framework by introducing the Ladder structure, combined with LSTM networks. This reduces the loss of original information during the transmission process, thereby making the model more effective in learning. In the experiment, MLAE classified all 27572 possible missense variants of the three input proteins with an average classification AUC of 0.941. This result provides evidence that MLAE is effective in predicting pathogenicity. Additionally, MLAE provides results for multi-label classification, with an average Hamming loss of 0.196, supporting the classification of complex variants. The proposed MLAE method provides an insightful approach to effectively capture amino acid sequence information and accurately predict the pathogenicity of mutations, thereby providing an analytical basis for the study and prevention of related diseases.
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Affiliation(s)
- Ke Bai
- Shandong Jianzhu University, Jinan, 250101, PR China
| | - Lu Yang
- Shandong Jianzhu University, Jinan, 250101, PR China
| | - Jian Xue
- Shandong Jianzhu University, Jinan, 250101, PR China
| | - Lin Zhao
- Shandong Jianzhu University, Jinan, 250101, PR China
| | - Fanchang Hao
- Shandong Jianzhu University, Jinan, 250101, PR China.
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5
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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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6
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Piot M, Bertrand F, Guihard S, Clavier JB, Maumy M. Bayesian Network structure learning algorithm for highly missing and non imputable data: Application to breast cancer radiotherapy data. Artif Intell Med 2024; 147:102743. [PMID: 38184350 DOI: 10.1016/j.artmed.2023.102743] [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: 07/25/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
It is not uncommon for real-life data produced in healthcare to have a higher proportion of missing data than in other scopes. To take into account these missing data, imputation is not always desired by healthcare experts, and complete case analysis can lead to a significant loss of data. The algorithm proposed here, allows the learning of Bayesian Network graphs when both imputation and complete case analysis are not possible. The learning process is based on a set of local bootstrap learnings performed on complete sub-datasets which are then aggregated and locally optimized. This learning method presents competitive results compared to other structure learning algorithms, whatever the mechanism of missing data.
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Affiliation(s)
- Mélanie Piot
- University of Technology of Troyes, Troyes, 10004 CEDEX, France; Strasbourg Cancer Institute (ICANS), Strasbourg, 67200, France.
| | | | | | | | - Myriam Maumy
- University of Technology of Troyes, Troyes, 10004 CEDEX, France.
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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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Mabuza LH, Moshabela M. What do medical students and their clinical preceptors understand by primary health care in South Africa? A qualitative study. BMC MEDICAL EDUCATION 2023; 23:785. [PMID: 37864172 PMCID: PMC10589924 DOI: 10.1186/s12909-023-04751-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/05/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND The definition of Primary Health Care (PHC) issued by the World Health Organisation in 1978 indicated that essential health care should be made accessible to individuals and their communities close to where they live and work. In 1992 Starfield articulated the four pillars of PHC: the patient's first contact with healthcare, comprehensive care, coordinated care and continuous care. Using this literature guidance, this study sought to explore what undergraduate medical students and their clinical preceptors understood by PHC in four South African medical schools. METHODS A qualitative study using the phenomenological design was conducted among undergraduate medical students and their clinical preceptors. The setting was four medical schools in South Africa (Sefako Makgatho Health Sciences University, Walter Sisulu University and the University of KwaZulu-Natal and the Witwatersrand University). A total of 27 in-depth interviews were conducted among the clinical preceptors and 16 focus group discussions among the students who were in their clinical years of training (MBChB 4-6). Interviews were digitally recorded and transcribed verbatim, followed by thematic data analysis using the MAXQDA 2020 (Analytics Pro) software. RESULTS Four themes were identified in which there were similarities between the students and their preceptors regarding their understanding of PHC: (1) PHC as the patient's first contact with the healthcare system; (2) comprehensive care; (3) coordination of care and (4) continuity of care. A further two themes were identified in which these two groups were not of similar understanding: (5) PHC as a level or an approach to healthcare and (6) the role of specialist clinical preceptors in PHC. CONCLUSIONS Medical students and their clinical preceptors displayed an understanding of PHC in line with four pillars articulated by Starfield and the WHO definition of PHC. However, there remains areas of divergence, on which the medical schools should follow the guidance provided by the WHO and Starfield for a holistic understanding of PHC.
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Affiliation(s)
- Langalibalele Honey Mabuza
- School of Medicine, Clinical Integrated Programs, Sefako Makgatho Health Sciences University, 0012, Pretoria, South Africa.
| | - Mosa Moshabela
- Research and Innovation, University of KwaZulu-Natal, 4001, Durban, South Africa
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9
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Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, Ibañez A, Duran-Aniotz C, de la Cruz R. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimers Res Ther 2023; 15:176. [PMID: 37838690 PMCID: PMC10576366 DOI: 10.1186/s13195-023-01304-8] [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: 06/02/2023] [Accepted: 09/15/2023] [Indexed: 10/16/2023]
Abstract
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
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Affiliation(s)
- Kevin Blanco
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
| | - Stefanny Salcidua
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile
| | - Paulina Orellana
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tania Sauma-Pérez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tomás León
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Memory and Neuropsychiatric Center (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
| | - Lorena Cecilia López Steinmetz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Technische Universität Berlin, Berlin, Deutschland
- Instituto de Investigaciones Psicológicas (IIPsi), Universidad Nacional de Córdoba (UNC) y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Agustín Ibañez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Global Brain Health Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, & National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Claudia Duran-Aniotz
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Rolando de la Cruz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile.
- Data Observatory Foundation, ANID Technology Center No. DO210001, Santiago, Chile.
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10
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Shinada K, Matsuoka A, Koami H, Sakamoto Y. Bayesian network predicted variables for good neurological outcomes in patients with out-of-hospital cardiac arrest. PLoS One 2023; 18:e0291258. [PMID: 37768915 PMCID: PMC10538776 DOI: 10.1371/journal.pone.0291258] [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: 02/06/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Out-of-hospital cardiac arrest (OHCA) is linked to a poor prognosis and remains a public health concern. Several studies have predicted good neurological outcomes of OHCA. In this study, we used the Bayesian network to identify variables closely associated with good neurological survival outcomes in patients with OHCA. This was a retrospective observational study using the Japan Association for Acute Medicine OHCA registry. Fifteen explanatory variables were used, and the outcome was one-month survival with Glasgow-Pittsburgh cerebral performance category (CPC) 1-2. The 2014-2018 dataset was used as training data. The variables selected were identified and a sensitivity analysis was performed. The 2019 dataset was used for the validation analysis. Four variables were identified, including the motor response component of the Glasgow Coma Scale (GCS M), initial rhythm, age, and absence of epinephrine. Estimated probabilities were increased in the following order: GCS M score: 2-6; epinephrine: non-administered; initial rhythm: spontaneous rhythm and shockable; and age: <58 and 59-70 years. The validation showed a sensitivity of 75.4% and a specificity of 95.4%. We identified GCS M score of 2-6, initial rhythm (spontaneous rhythm and shockable), younger age, and absence of epinephrine as variables associated with one-month survival with CPC 1-2. These variables may help clinicians in the decision-making process while treating patients with OHCA.
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Affiliation(s)
- Kota Shinada
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Saga University, Saga City, Saga Prefecture, Japan
| | - Ayaka Matsuoka
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Saga University, Saga City, Saga Prefecture, Japan
| | - Hiroyuki Koami
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Saga University, Saga City, Saga Prefecture, Japan
| | - Yuichiro Sakamoto
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Saga University, Saga City, Saga Prefecture, Japan
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Su F, Chao J, Liu P, Zhang B, Zhang N, Luo Z, Han J. Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network. BMC Med Inform Decis Mak 2023; 23:120. [PMID: 37443001 PMCID: PMC10347801 DOI: 10.1186/s12911-023-02224-1] [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: 01/04/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND To construct two prognostic models to predict survival in breast cancer patients; to compare the efficacy of the two models in the whole group and the advanced human epidermal growth factor receptor-2-positive (HER2+) subgroup of patients; to conclude whether the Hybrid Bayesian Network (HBN) model outperformed the logistics regression (LR) model. METHODS In this paper, breast cancer patient data were collected from the SEER database. Data processing and analysis were performed using Rstudio 4.2.0, including data preprocessing, model construction and validation. The L_DVBN algorithm in Julia0.4.7 and bnlearn package in R was used to build and evaluate the HBN model. Data with a diagnosis time of 2018(n = 23,384) were distributed randomly as training and testing sets in the ratio of 7:3 using the leave-out method for model construction and internal validation. External validation of the model was done using the dataset of 2019(n = 8128). Finally, the late HER2 + patients(n = 395) was selected for subgroup analysis. Accuracy, calibration and net benefit of clinical decision making were evaluated for both models. RESULTS The HBN model showed that seventeen variables were associated with survival outcome, including age, tumor size, site, histologic type, radiotherapy, surgery, chemotherapy, distant metastasis, subtype, clinical stage, ER receptor, PR receptor, clinical grade, race, marital status, tumor laterality, and lymph node. The AUCs for the internal validation of the LR and HBN models were 0.831 and 0.900; The AUCs for the external validation of the LR and HBN models on the whole population were 0.786 and 0.871; the AUCs for the external validation of the two models on the subgroup population were 0.601 and 0.813. CONCLUSION The accuracy, net clinical benefit, and calibration of the HBN model were better than LR model. The predictive efficacy of both models decreased and the difference was greater in advanced HER2 + patients, which means the HBN model had higher robustness and more stable predictive performance in the subgroup.
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Affiliation(s)
- Fan Su
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Jianqian Chao
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
- Department of Medical Insurance, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Pei Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Bowen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Na Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Zongyu Luo
- Department of Medical Insurance, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
| | - Jiaying Han
- Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu China
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van der Does Y, Turner RJ, Bartels MJH, Hagoort K, Metselaar A, Scheepers F, Grünwald PD, Somers M, van Dellen E. Outcome prediction of electroconvulsive therapy for depression. Psychiatry Res 2023; 326:115328. [PMID: 37429173 DOI: 10.1016/j.psychres.2023.115328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/26/2023] [Accepted: 06/30/2023] [Indexed: 07/12/2023]
Abstract
INTRODUCTION We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome. METHODS We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN. Temporal validation was performed in an independent sample. RESULTS The systematic literature search yielded three meta-analyses, which provided prior knowledge on outcome predictors. The clinical dataset consisted of 248 treatment trajectories in the training set and 44 trajectories in the test set at the same medical center. The AUC for the primary outcome remission estimated on an independent validation set was 0.686 (95%CI 0.513-0.859) (AUC values of 0.505 - 0.763 observed in 5-fold cross validation of the model within the train set). Accuracy 0.73 (balanced accuracy 0.67), sensitivity 0.55, specificity 0.79, after temporal validation in the independent sample. Prior literature information marginally reduced CI width. DISCUSSION A BN model comprised of prior knowledge and clinical data can predict remission of depression after ECT with reasonable performance. This approach can be used to make outcome predictions in psychiatry, and offers a methodological framework to weigh additional information, such as patient characteristics, symptoms and biomarkers. In time, it may be used to improve shared decision-making in clinical practice.
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Affiliation(s)
- Yuri van der Does
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands.
| | - Rosanne J Turner
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands; Machine Learning Group, CWI (national research institute for mathematics and computer science), Amsterdam, the Netherlands
| | - Miel J H Bartels
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Karin Hagoort
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Aäron Metselaar
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Floortje Scheepers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Peter D Grünwald
- Machine Learning Group, CWI (national research institute for mathematics and computer science), Amsterdam, the Netherlands; Department of Mathematics, Leiden University, Leiden, Netherlands
| | - Metten Somers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands; Department of Neurology, UZ Brussel and Vrije Universiteit Brussel, Brussels, Belgium
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Dissanayake S, Krishna R, Pathirana PN, Horne MK, Szmulewicz DJ, Corben LA. A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082810 DOI: 10.1109/embc40787.2023.10340184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.
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Ayadi H, Torjmen-Khemakhem M, Huang JX. Term dependency extraction using rule-based Bayesian Network for medical image retrieval. Artif Intell Med 2023; 140:102551. [PMID: 37210157 DOI: 10.1016/j.artmed.2023.102551] [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: 02/01/2021] [Revised: 07/03/2022] [Accepted: 04/11/2023] [Indexed: 05/22/2023]
Abstract
Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in words, hence negatively affect the retrieval performance. One of the solutions offered in the literature is to form a Bayesian Network thesaurus taking advantage of some medical terms extracted from the image datasets. Despite the interestingness of this solution, it is not efficient as it is highly related to the co-occurrence measure, the layer arrangement and the arc directions. A significant drawback of the co-occurrence measure is the generation of a lot of uninteresting co-occurring terms. Several studies applied the association rules mining and its measures to discover the correlation between the terms. In this paper, we propose a new efficient association Rule Based Bayesian Network (R2BN) model for TBMIR using updated medically-dependent features (MDF) based on Unified Medical Language System (UMLS). The MDF are a set of medical terms that refers to the imaging modalities, the image color, the searched object dimension, etc. The proposed model presents the association rules mined from MDF in the form of Bayesian Network model. Then, it exploits the association rule measures (support, confidence, and lift) to prune the Bayesian Network model for efficient computation. The proposed R2BN model is combined with a literature probabilistic model to predict the relevance of an image to a given query. Experiments are carried out with ImageCLEF medical retrieval task collections from 2009 to 2013. Results show that our proposed model enhances significantly the image retrieval accuracy compared to the state-of-the-art retrieval models.
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Affiliation(s)
- Hajer Ayadi
- Information Retrieval & Knowledge Management Research Lab, York University, Toronto, Ontario, Canada.
| | | | - Jimmy X Huang
- Information Retrieval & Knowledge Management Research Lab, York University, Toronto, Ontario, Canada
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15
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Zhu W, Marchant R, Morris RW, Baur LA, Simpson SJ, Cripps S. Bayesian network modelling to identify on-ramps to childhood obesity. BMC Med 2023; 21:105. [PMID: 36944999 PMCID: PMC10031893 DOI: 10.1186/s12916-023-02789-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/15/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND When tackling complex public health challenges such as childhood obesity, interventions focused on immediate causes, such as poor diet and physical inactivity, have had limited success, largely because upstream root causes remain unresolved. A priority is to develop new modelling frameworks to infer the causal structure of complex chronic disease networks, allowing disease "on-ramps" to be identified and targeted. METHODS The system surrounding childhood obesity was modelled as a Bayesian network, using data from The Longitudinal Study of Australian Children. The existence and directions of the dependencies between factors represent possible causal pathways for childhood obesity and were encoded in directed acyclic graphs (DAGs). The posterior distribution of the DAGs was estimated using the Partition Markov chain Monte Carlo. RESULTS We have implemented structure learning for each dataset at a single time point. For each wave and cohort, socio-economic status was central to the DAGs, implying that socio-economic status drives the system regarding childhood obesity. Furthermore, the causal pathway socio-economic status and/or parental high school levels → parental body mass index (BMI) → child's BMI existed in over 99.99% of posterior DAG samples across all waves and cohorts. For children under the age of 8 years, the most influential proximate causal factors explaining child BMI were birth weight and parents' BMI. After age 8 years, free time activity became an important driver of obesity, while the upstream factors influencing free time activity for boys compared with girls were different. CONCLUSIONS Childhood obesity is largely a function of socio-economic status, which is manifest through numerous downstream factors. Parental high school levels entangle with socio-economic status, and hence, are on-ramp to childhood obesity. The strong and independent causal relationship between birth weight and childhood BMI suggests a biological link. Our study implies that interventions that improve the socio-economic status, including through increasing high school completion rates, may be effective in reducing childhood obesity prevalence.
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Affiliation(s)
- Wanchuang Zhu
- Human Technology Institute, University of Technology, Sydney, Australia.
- Data61, CSIRO, Sydney, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
| | - Roman Marchant
- Data61, CSIRO, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Richard W Morris
- School of Psychology and Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Louise A Baur
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- The Children's Hospital at Westmead, The University of Sydney, Sydney, Australia
| | - Stephen J Simpson
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Sally Cripps
- Human Technology Institute, University of Technology, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- School of Mathematics and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia
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El-Sappagh S, Alonso-Moral JM, Abuhmed T, Ali F, Bugarín-Diz A. Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges. Artif Intell Rev 2023. [DOI: 10.1007/s10462-023-10415-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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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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Huber M, Greif R, Pedersen TH, Theiler L, Kleine-Brueggeney M. Risk patterns of consecutive adverse events in airway management: a Bayesian network analysis. Br J Anaesth 2023; 130:368-378. [PMID: 36564247 DOI: 10.1016/j.bja.2022.11.007] [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: 08/08/2022] [Revised: 10/27/2022] [Accepted: 11/14/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Minor adverse airway events play a pivotal role in the safety of airway management. Changes in airway management strategies can reduce such events, but the broader impact on airway management remains unclear. METHODS Minor, frequently occurring adverse airway events were audited before and after implementation of changes to airway management strategies. We used two Bayesian networks to examine conditional probabilities of subsequent airway events and to compute the likelihood of certain events given that certain previous events occurred. RESULTS Independent of sex, age, and American Society of Anesthesiologists physical status, targeted changes to airway management strategies reduced the risk of a first event. Obese patients were an exception, in whom no risk reduction was achieved. Frequently occurring event sequences were identified, for example the most likely event to follow difficult bag-mask ventilation was a Cormack-Lehane grade ≥3, with a risk of 14.3% (95% credible interval [CI], 11.4-17.2%). An impact of the targeted changes was detected on the likelihood of some event sequences, for example the likelihood of no consecutive event after a tracheal tube-related event increased from 43.3% (95% CI, 39.4-47.6%) to 56.4% (95% CI, 52.0-60.5%). CONCLUSIONS Identification of risk patterns and typical structures of event sequences provides a clinically relevant perspective on airway incidents. It further provides a means to quantify the impact of targeted airway management changes. These targeted changes can influence some event sequences, but overall, the benefit results from the cumulative effect of improvements in multiple events. Targeted airway management changes with knowledge of risk patterns and event sequences can potentially further improve patient safety in airway management. CLINICAL TRIAL REGISTRATION NCT02743767.
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Affiliation(s)
- Markus Huber
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
| | - Robert Greif
- Department of Anaesthesiology and Pain Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; School of Medicine, Sigmund Freud Private University Vienna, Vienna, Austria
| | - Tina H Pedersen
- Department of Anaesthesiology, Nordsjaellands Hospital, University of Copenhagen, Hillerod, Denmark
| | - Lorenz Theiler
- Department of Cardiac Anesthesiology and Intensive Care Medicine, German Heart Center Berlin, Berlin, Germany
| | - Maren Kleine-Brueggeney
- Department of Cardiac Anesthesiology and Intensive Care Medicine, German Heart Center Berlin, Berlin, Germany; Department of Cardiac Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Department of Anaesthesiology Cantonal Hospital Aarau, Aarau, Switzerland
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Gani MO, Kethireddy S, Adib R, Hasan U, Griffin P, Adibuzzaman M. Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU. Artif Intell Med 2023; 137:102493. [PMID: 36868692 PMCID: PMC9992896 DOI: 10.1016/j.artmed.2023.102493] [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: 08/02/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 02/01/2023]
Abstract
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.
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Affiliation(s)
- Md Osman Gani
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
| | | | - Riddhiman Adib
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Uzma Hasan
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
| | - Paul Griffin
- Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, USA.
| | - Mohammad Adibuzzaman
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
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Yuriev S, Rodinkova V, Mokin V, Varchuk I, Sharikadze O, Marushko Y, Halushko B, Kurchenko A. Molecular sensitization pattern to house dust mites is formed from the first years of life and includes group 1, 2, Der p 23, Der p 5, Der p 7 and Der p 21 allergens. Clin Mol Allergy 2023; 21:1. [PMID: 36737770 PMCID: PMC9898923 DOI: 10.1186/s12948-022-00182-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/28/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND As the process and nature of developing sensitivity to house dust mites (HDMs) remain not fully studied, our goal was to establish the pattern, nature and timeframe of house dust mite (HDM) sensitization development in patients in Ukraine as well as the period when treatment of such patients would be most effective. METHODS The data of the multiplex allergy test Alex2 was collected from 20,033 patients. To determine age specifics of sensitization, descriptive statistics were used. Bayesian Network analysis was used to build probabilistic patterns of individual sensitization. RESULTS Patients from Ukraine were most often sensitized to HDM allergens of group 1 (Der p 1, Der f 1) and group 2 (Der p 2, Der f 2) as well as to Der p 23 (55%). A considerable sensitivity to Der p 5, Der p 7 and Der p 21 allergens was also observed. The overall nature of sensitization to HDM allergens among the population of Ukraine is formed within the first year of life. By this time, there is a pronounced sensitization to HDM allergens of groups 1 and 2 as well as to Der p 23. Significance of sensitization to Der p 5, Der p 7 and Der p 21 allergens grows starting from the age of 3-6. Bayesian Network data analysis indicated the leading role of sensitization to Der p 1 and Der f 2. While developing the sensitivity to group 5 allergens, the leading role may belong to Der p 21 allergen. CONCLUSION The results obtained indicate the importance of determining the sensitization profile using the multi-component approach. A more detailed study of the optimal age for AIT prescription is required as the pattern of sensitization to HDMs is formed during the first year of life.
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Affiliation(s)
- Serhii Yuriev
- Medical Centre, DIVERO, Kiev, Ukraine ,grid.412081.eDepartment of Clinical Immunology and Allergology, Bohomolets National Medical University, Kiev, Ukraine
| | - Victoria Rodinkova
- grid.446037.2Department of Pharmacy, National Pirogov Memorial Medical University, 56, Pirogov Street, Vinnytsia, 21018 Ukraine
| | - Vitalii Mokin
- grid.446046.40000 0000 9939 744XDepartment of System Analysis and Information Technologies, Vinnytsia National Technical University, Vinnytsia, Ukraine
| | - Ilona Varchuk
- grid.446046.40000 0000 9939 744XDepartment of System Analysis and Information Technologies, Vinnytsia National Technical University, Vinnytsia, Ukraine
| | - Olena Sharikadze
- Paediatric Department, Shupyk National Healthcare University, Kiev, Ukraine
| | - Yuriy Marushko
- Department of Pediatrics of Postgraduate Education, O.O. Bohomolets Medical University, Kiev, Ukraine
| | - Bohdan Halushko
- Department of Pediatrics of Postgraduate Education, O.O. Bohomolets Medical University, Kiev, Ukraine
| | - Andrii Kurchenko
- grid.412081.eDepartment of Clinical Immunology and Allergology, Bohomolets National Medical University, Kiev, Ukraine
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21
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Albini E, Rago A, Baroni P, Toni F. Achieving descriptive accuracy in explanations via argumentation: The case of probabilistic classifiers. Front Artif Intell 2023; 6:1099407. [PMID: 37091304 PMCID: PMC10117939 DOI: 10.3389/frai.2023.1099407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but one that has thus far been overlooked is that of descriptive accuracy (DA), i.e., that the explanation contents are in correspondence with the internal working of the explained system. Indeed, the violation of this core property would lead to the paradoxical situation of systems producing explanations which are not suitably related to how the system actually works: clearly this may hinder user trust. Further, if explanations violate DA then they can be deceitful, resulting in an unfair behavior toward the users. Crucial as the DA property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalizing DA and of analyzing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature, variants thereof and a novel form of explanation that we propose. We conduct experiments with a varied selection of concrete probabilistic classifiers and highlight the importance, with a user study, of our most demanding notion of dialectical DA, which our novel method satisfies by design and others may violate. We thus demonstrate how DA could be a critical component in achieving trustworthy and fair systems, in line with the principles of human-centric AI.
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Affiliation(s)
- Emanuele Albini
- Department of Computing, Imperial College London, London, United Kingdom
| | - Antonio Rago
- Department of Computing, Imperial College London, London, United Kingdom
- *Correspondence: Antonio Rago
| | - Pietro Baroni
- Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Brescia, Brescia, Italy
| | - Francesca Toni
- Department of Computing, Imperial College London, London, United Kingdom
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22
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Leonelli M, Riccomagno E. A geometric characterization of sensitivity analysis in monomial models. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Romero D, Blanco-Almazán D, Groenendaal W, Lijnen L, Smeets C, Ruttens D, Catthoor F, Jané R. Predicting 6-minute walking test outcomes in patients with chronic obstructive pulmonary disease without physical performance measures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107020. [PMID: 35905697 DOI: 10.1016/j.cmpb.2022.107020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 06/20/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic obstructive pulmonary disease (COPD) requires a multifactorial assessment, evaluating the airflow limitation and symptoms of the patients. The 6-min walk test (6MWT) is commonly used to evaluate the functional exercise capacity in these patients. This study aims to propose a novel predictive model of the major 6MWT outcomes for COPD assessment, without physical performance measurements. METHODS Cardiopulmonary and clinical parameters were obtained from fifty COPD patients. These parameters were used as inputs of a Bayesian network (BN), which integrated three multivariate models including the 6-min walking distance (6MWD), the maximum HR (HRmax) after the walking, and the HR decay 3 min after (HRR3). The use of BN allows the assessment of the patients' status by predicting the 6MWT outcomes, but also inferring disease severity parameters based on actual patient's 6MWT outcomes. RESULTS Firstly, the correlation obtained between the estimated and actual 6MWT measures was strong (R = 0.84, MAPE = 8.10% for HRmax) and moderate (R = 0.58, MAPE = 15.43% for 6MWD and R = 0.58, MAPE = 32.49% for HRR3), improving the classical methods to estimate 6MWD. Secondly, the classification of disease severity showed an accuracy of 78.3% using three severity groups, which increased up to 84.4% for two defined severity groups. CONCLUSIONS We propose a powerful two-way assessment tool for COPD patients, capable of predicting 6MWT outcomes without the need for an actual walking exercise. This model-based tool opens the way to implement a continuous monitoring system for COPD patients at home and to provide more personalized care.
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Affiliation(s)
- Daniel Romero
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain.
| | - Dolores Blanco-Almazán
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain
| | | | | | | | | | | | - Raimon Jané
- Universitat Politecnica de Catalunya · BarcelonaTech (UPC), Barcelona 08019, Spain; Institute for Bioengineering of Catalonia (IBEC-BIST), Barcelona 08019, Spain; Biomedical Research Networking Center of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid 28029, Spain
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24
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Lv Z, Chen Y, Di R, Wang H, Sun X, He C, Li X. Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1354. [PMID: 37420374 DOI: 10.3390/e24101354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 09/09/2022] [Accepted: 09/21/2022] [Indexed: 07/09/2023]
Abstract
The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.
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Affiliation(s)
- Zhigang Lv
- School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China
- School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Yiwei Chen
- School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Ruohai Di
- School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Hongxi Wang
- School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Xiaojing Sun
- General Office, Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China
| | - Chuchao He
- School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China
| | - Xiaoyan Li
- School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China
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25
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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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Ferrer-Miranda E, Fonseca-Rodríguez O, Albuquerque J, Almeida ECD, Tadeu Cristino C, Santoro KR. Assessment of the foot-and-mouth disease surveillance system in Brazil. Prev Vet Med 2022; 205:105695. [PMID: 35772240 DOI: 10.1016/j.prevetmed.2022.105695] [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: 07/22/2021] [Revised: 05/21/2022] [Accepted: 06/12/2022] [Indexed: 11/27/2022]
Abstract
In 2021, the 88th General Session of the World Assembly of National Delegates to the World Organisation for Animal Health (OIE) recognized the estates of Acre, Paraná, the Rio Grande do Sul, and Rondônia as being free of foot-and-mouth disease (FMD) without vaccination. The certification was also extended to some cities in Amazonas and Mato Grosso. The new national strategic plan for 2026, which focuses on creating and maintaining sustainable conditions to expand FMD-free zones without vaccination, imposes new challenges and requires continuous evaluation of the FMD surveillance system. The objective of this research was to evaluate the FMD surveillance system in Brazil using quantitative models through Bayesian network approaches. The research was conducted using the Continental Surveillance and Information System (SivCont) database for Official Veterinary Services in Brazil, which refers to notified vesicular syndromes. The data on states, reported diseases, source of notification, disease confirmation, and timeliness (TL in days) of the delay by owners in notifying (TL.1) after a suspected case of the disease, and the response of Brazilian Veterinary Services after being notified (TL.2), were analysed. The collected data were analysed using Bayesian networks. It was observed that diseases with symptoms identical to FMD are the most notified events. TL.1 was long (mean of 18.96, CI: 18.33-19.59), and a low number of notifications was observed throughout the study period, which increases the chances of disseminating FMD in the population. Meanwhile, TL.2 suggests appropriate effectiveness of the Veterinary Services responding to suspected cases of FMD with interventions in less than 24 h (mean of 1, CI: 0.68-1.31). This study evaluated the performance of Brazilian Veterinary Services facing the report of vesicular diseases in the period 2004-2018. The results can help the states improve the surveillance system and the transition to the vaccination stop. Furthermore, the analytical method presented in the paper could serve as a model for other countries to evaluate the effectiveness of FMD surveillance systems.
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Affiliation(s)
- Edyniesky Ferrer-Miranda
- Federal Rural University of Pernambuco, Postgraduate Program in Biometrics and Applied Statistics (UFRPE/PPGBEA), street Dom Manuel de Medeiros, s/n, Dois Irmãos, 52171-900 Recife, Pernambuco, Brazil; Federal University of Agreste of Pernambuco (UFAPE), Avenida Bom Pastor, s/n.º - Boa Vista, Garanhuns, Pernambuco, Brazil.
| | | | - Jones Albuquerque
- Federal Rural University of Pernambuco, Postgraduate Program in Biometrics and Applied Statistics (UFRPE/PPGBEA), street Dom Manuel de Medeiros, s/n, Dois Irmãos, 52171-900 Recife, Pernambuco, Brazil; Keizo Asami Laboratory of Immunopathology (LIKA/UF PE), avenue Prof. Moraes Rego 1235, Cidade Universitária, 50670-901 Recife, Pernambuco, Brazil.
| | - Erivânia Camelo de Almeida
- Agricultural Defense and Inspection Agency of Pernambuco (ADAGRO), Avenue Caxangá, 2200-Cordeiro, 50711-000 Recife, Pernambuco, Brazil
| | - Claudio Tadeu Cristino
- Federal Rural University of Pernambuco, Postgraduate Program in Biometrics and Applied Statistics (UFRPE/PPGBEA), street Dom Manuel de Medeiros, s/n, Dois Irmãos, 52171-900 Recife, Pernambuco, Brazil.
| | - Kleber Régis Santoro
- Federal Rural University of Pernambuco, Postgraduate Program in Biometrics and Applied Statistics (UFRPE/PPGBEA), street Dom Manuel de Medeiros, s/n, Dois Irmãos, 52171-900 Recife, Pernambuco, Brazil; Federal University of Agreste of Pernambuco (UFAPE), Avenida Bom Pastor, s/n.º - Boa Vista, Garanhuns, Pernambuco, Brazil.
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27
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Qi Z, Yue K, Duan L, Hu K, Liang Z. Dynamic embeddings for efficient parameter learning of Bayesian network with multiple latent variables. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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28
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Yon V, Amirsoleimani A, Alibart F, Melko RG, Drouin D, Beilliard Y. Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning. FRONTIERS IN ELECTRONICS 2022. [DOI: 10.3389/felec.2022.825077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Novel computing architectures based on resistive switching memories (also known as memristors or RRAMs) have been shown to be promising approaches for tackling the energy inefficiency of deep learning and spiking neural networks. However, resistive switch technology is immature and suffers from numerous imperfections, which are often considered limitations on implementations of artificial neural networks. Nevertheless, a reasonable amount of variability can be harnessed to implement efficient probabilistic or approximate computing. This approach turns out to improve robustness, decrease overfitting and reduce energy consumption for specific applications, such as Bayesian and spiking neural networks. Thus, certain non-idealities could become opportunities if we adapt machine learning methods to the intrinsic characteristics of resistive switching memories. In this short review, we introduce some key considerations for circuit design and the most common non-idealities. We illustrate the possible benefits of stochasticity and compression with examples of well-established software methods. We then present an overview of recent neural network implementations that exploit the imperfections of resistive switching memory, and discuss the potential and limitations of these approaches.
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Yung KK, Ardern CL, Serpiello FR, Robertson S. Characteristics of Complex Systems in Sports Injury Rehabilitation: Examples and Implications for Practice. SPORTS MEDICINE - OPEN 2022; 8:24. [PMID: 35192079 PMCID: PMC8864040 DOI: 10.1186/s40798-021-00405-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 12/29/2021] [Indexed: 11/22/2022]
Abstract
Complex systems are open systems consisting of many components that can interact among themselves and the environment. New forms of behaviours and patterns often emerge as a result. There is a growing recognition that most sporting environments are complex adaptive systems. This acknowledgement extends to sports injury and is reflected in the individual responses of athletes to both injury and rehabilitation protocols. Consequently, practitioners involved in return to sport decision making (RTS) are encouraged to view return to sport decisions through the complex systems lens to improve decision-making in rehabilitation. It is important to clarify the characteristics of this theoretical framework and provide concrete examples to which practitioners can easily relate. This review builds on previous literature by providing an overview of the hallmark features of complex systems and their relevance to RTS research and daily practice. An example of how characteristics of complex systems are exhibited is provided through a case of anterior cruciate ligament injury rehabilitation. Alternative forms of scientific inquiry, such as the use of computational and simulation-based techniques, are also discussed-to move the complex systems approach from the theoretical to the practical level.
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Affiliation(s)
- Kate K Yung
- Institute for Health and Sport, Victoria University, Melbourne, Australia.
| | - Clare L Ardern
- Musculoskeletal and Sports Injury Epidemiology Centre, Department of Health Promotion Science, Sophiahemmet University, Stockholm, Sweden
- Sport and Exercise Medicine Research Centre, La Trobe University, Melbourne, Australia
- Department of Family Practice, University of British Columbia, Vancouver, Canada
| | - Fabio R Serpiello
- Institute for Health and Sport, Victoria University, Melbourne, Australia
| | - Sam Robertson
- Institute for Health and Sport, Victoria University, Melbourne, Australia
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Wesołowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS DIGITAL HEALTH 2022; 1:e0000004. [PMID: 35373216 PMCID: PMC8975108 DOI: 10.1371/journal.pdig.0000004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/17/2021] [Indexed: 11/19/2022]
Abstract
Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyses.
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Affiliation(s)
- Sergiusz Wesołowski
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Gordon Lemmon
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Edgar J. Hernandez
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Alex Henrie
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
| | - Thomas A. Miller
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Derek Weyhrauch
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael D. Puchalski
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Bruce E. Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, United States of America
| | - Rashmee U. Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Vikrant G. Deshmukh
- University of Utah Health Care CMIO Office, Salt Lake City, UT, United States of America
| | - Rebecca Delaney
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, United States of America
| | - Karen Eilbeck
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States of America
| | - Martin Tristani-Firouzi
- Division of Pediatric Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Nora Eccles Harrison CVRTI, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Mark Yandell
- Department of Human Genetics and Utah Center for Genetic Discovery, University of Utah, Salt Lake City, UT, United States of America
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Artificial Intelligence in Clinical Immunology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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McLeod C, Norman R, Wood J, Mulrennan S, Morey S, Schultz A, Messer M, Spaapen K, Stoneham M, Wu Y, Smyth A, Blyth C, Webb S, Mascaro S, Woodberry O, Snelling T. Novel method to select meaningful outcomes for evaluation in clinical trials. BMJ Open Respir Res 2021; 8:8/1/e000877. [PMID: 34620699 PMCID: PMC8499339 DOI: 10.1136/bmjresp-2021-000877] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/15/2021] [Indexed: 12/25/2022] Open
Abstract
Background A standardised framework for selecting outcomes for evaluation in trials has been proposed by the Core Outcome Measures in Effectiveness Trials working group. However, this method does not specify how to ensure that the outcomes that are selected are causally related to the disease and the health intervention being studied. Causal network diagrams may help researchers identify outcomes that are both clinically meaningful and likely to be causally dependent on the intervention, and endpoints that are, in turn, causally dependent on those outcomes. We aimed to (1) develop a generalisable method for selecting outcomes and endpoints in trials and (2) apply this method to select outcomes for evaluation in a trial investigating treatment strategies for pulmonary exacerbations of cystic fibrosis (CF). Methods We conducted a series of online surveys and workshops among people affected by CF. We used a modified Delphi approach to develop a consensus list of important outcomes. A workshop involving domain experts elicited how these outcomes were causally related to the underlying pathophysiological processes. Meaningful outcomes were prioritised based on the extent to which each outcome captured separate rather than common aspects of the underlying pathophysiological process. Results The 10 prioritised outcomes were: breathing difficulty/pain, sputum production/clearance, fatigue, appetite, pain (not related to breathing), motivation/demoralisation, fevers/night sweats, treatment burden, inability to meet personal goals and avoidance of gastrointestinal symptoms. Conclusions This proposed method for selecting meaningful outcomes for evaluation in clinical trials may improve the value of research as a basis for clinical decisions.
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Affiliation(s)
- Charlie McLeod
- Infectious Diseases, Perth Children's Hospital, Nedlands, Western Australia, Australia .,Infectious Diseases Implementation Research, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Richard Norman
- School of Population Health, Curtin University Bentley Campus, Bentley, Western Australia, Australia
| | - Jamie Wood
- Abilities Research Center, Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Physiotherapy, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - Siobhain Mulrennan
- Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.,The Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Western Australia, Australia
| | - Sue Morey
- Respiratory Medicine, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
| | - André Schultz
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, Nedlands, Western Australia, Australia.,Department of Respiratory Medicine, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Mitch Messer
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Kate Spaapen
- Consumer advocate, Perth, Western Australia, Australia
| | | | - Yue Wu
- School of Public Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Alan Smyth
- Evidence Based Child Health Group, School of Medicine, University of Nottingham, Nottingham, UK
| | - Christopher Blyth
- Infectious Diseases, Perth Children's Hospital, Nedlands, Western Australia, Australia.,Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, Western Australia, Australia
| | - Steve Webb
- School of Population Health and Preventive Medicine, Monash University, Clayton, Victoria, Australia.,Department of Intensive Care Medicine, St John of God Health Care, West Perth, Western Australia, Australia
| | | | | | - Tom Snelling
- School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.,Menzies School of Health Research, Casuarina, Northern Territory, Australia
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33
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Di R, Wang P, He C, Guo Z. Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters. ENTROPY 2021; 23:e23101283. [PMID: 34682007 PMCID: PMC8534477 DOI: 10.3390/e23101283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022]
Abstract
Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.
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Affiliation(s)
- Ruohai Di
- School of Electronics and Information Engineering, Xi’an Technological University, Xi’an 710021, China; (R.D.); (P.W.); (C.H.)
| | - Peng Wang
- School of Electronics and Information Engineering, Xi’an Technological University, Xi’an 710021, China; (R.D.); (P.W.); (C.H.)
| | - Chuchao He
- School of Electronics and Information Engineering, Xi’an Technological University, Xi’an 710021, China; (R.D.); (P.W.); (C.H.)
| | - Zhigao Guo
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
- Correspondence: ; Tel.: +44-075-0247-6882
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34
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Wu Y, Foley D, Ramsay J, Woodberry O, Mascaro S, Nicholson AE, Snelling T. Bridging the gaps in test interpretation of SARS-CoV-2 through Bayesian network modelling. Epidemiol Infect 2021; 149:1-13. [PMID: 34165071 PMCID: PMC8314199 DOI: 10.1017/s0950268821001357] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/04/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022] Open
Abstract
In the absence of an established gold standard, an understanding of the testing cycle from individual exposure to test outcome report is required to guide the correct interpretation of severe acute respiratory syndrome-coronavirus-2 reverse transcriptase real-time polymerase chain reaction (RT-PCR) results and optimise the testing processes. Bayesian network models have been used within healthcare to bring clarity to complex problems. We use this modelling approach to construct a comprehensive framework for understanding the real-world predictive value of individual RT-PCR results. We elicited knowledge from domain experts to describe the test process through a facilitated group workshop. A preliminary model was derived based on the elicited knowledge, then subsequently refined, parameterised and validated with a second workshop and one-on-one discussions. Causal relationships elicited describe the interactions of pre-testing, specimen collection and laboratory procedures and RT-PCR platform factors, and their impact on the presence and quantity of virus and thus the test result and its interpretation. By setting the input variables as ‘evidence’ for a given subject and preliminary parameterisation, four scenarios were simulated to demonstrate potential uses of the model. The core value of this model is a deep understanding of the total testing cycle, bridging the gap between a person's true infection status and their test outcome. This model can be adapted to different settings, testing modalities and pathogens, adding much needed nuance to the interpretations of results.
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Affiliation(s)
- Yue Wu
- School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
| | - David Foley
- Department of Infectious Diseases, Perth Children's Hospital, Perth, Western Australia, Australia
| | - Jessica Ramsay
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Western Australia, Australia
| | - Owen Woodberry
- Department of Data Science & Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Steven Mascaro
- Department of Data Science & Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Ann E. Nicholson
- Department of Data Science & Artificial Intelligence, Monash University, Clayton, Victoria, Australia
| | - Tom Snelling
- School of Public Health, University of Sydney, Camperdown, New South Wales, Australia
- Department of Infectious Diseases, Perth Children's Hospital, Perth, Western Australia, Australia
- Wesfarmers Centre for Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, Western Australia, Australia
- School of Public Health, Curtin University, Bentley, Western Australia, Australia
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory Australia
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35
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Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18126228. [PMID: 34207560 PMCID: PMC8296041 DOI: 10.3390/ijerph18126228] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 12/21/2022]
Abstract
Background: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. Methods: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case–control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. Results: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.
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A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future. Artif Intell Med 2021; 117:102108. [PMID: 34127238 DOI: 10.1016/j.artmed.2021.102108] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 05/07/2021] [Accepted: 05/10/2021] [Indexed: 12/15/2022]
Abstract
No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.
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Huynh PK, Setty A, Phan H, Le TQ. Probabilistic domain-knowledge modeling of disorder pathogenesis for dynamics forecasting of acute onset. Artif Intell Med 2021; 115:102056. [PMID: 34001316 PMCID: PMC8493977 DOI: 10.1016/j.artmed.2021.102056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 03/01/2021] [Accepted: 03/22/2021] [Indexed: 11/18/2022]
Abstract
Disease pathogenesis, a type of domain knowledge about biological mechanisms leading to diseases, has not been adequately encoded in machine-learning-based medical diagnostic models because of the inter-patient variabilities and complex dependencies of the underlying pathogenetic mechanisms. We propose 1) a novel pathogenesis probabilistic graphical model (PPGM) to quantify the dynamics underpinning patient-specific data and pathogenetic domain knowledge, 2) a Bayesian-based inference paradigm to answer the medical queries and forecast acute onsets. The PPGM model consists of two components: a Bayesian network of patient attributes and a temporal model of pathogenetic mechanisms. The model structure was reconstructed from expert knowledge elicitation, and its parameters were estimated using Variational Expectation-Maximization algorithms. We benchmarked our model with two well-established hidden Markov models (HMMs) - Input-output HMM (IO-HMM) and Switching Auto-Regressive HMM (SAR-HMM) - to evaluate the computational costs, forecasting performance, and execution time. Two case studies on Obstructive Sleep Apnea (OSA) and Paroxysmal Atrial Fibrillation (PAF) were used to validate the model. While the performance of the parameter learning step was equivalent to those of IO-HMM and SAR-HMM models, our model forecasting ability was outperforming those two models. The merits of the PPGM model are its representation capability to capture the dynamics of pathogenesis and perform medical inferences and its interpretability for physicians. The model has been used to perform medical queries and forecast the acute onset of OSA and PAF. Additional applications of the model include prognostic healthcare and preventive personalized treatments.
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Affiliation(s)
- Phat K Huynh
- Department of Industrial and Manufacturing Engineering, North Dakota State University at Fargo, ND, USA
| | | | - Hao Phan
- Pham Ngoc Thach University of Medicine at Ho Chi Minh City, Viet Nam
| | - Trung Q Le
- Department of Industrial and Manufacturing Engineering, North Dakota State University at Fargo, ND, USA; Department of Biomedical Engineering, North Dakota State University at Fargo, ND, USA.
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38
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Kyrimi E, Dube K, Fenton N, Fahmi A, Neves MR, Marsh W, McLachlan S. Bayesian networks in healthcare: What is preventing their adoption? Artif Intell Med 2021; 116:102079. [PMID: 34020755 DOI: 10.1016/j.artmed.2021.102079] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 04/14/2021] [Accepted: 04/20/2021] [Indexed: 12/15/2022]
Abstract
There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.
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Affiliation(s)
- Evangelia Kyrimi
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
| | - Kudakwashe Dube
- Health Informatics and Knowledge Engineering Research (HiKER) Group; School of Fundamental Sciences, Massey University, Palmerston North, 4442, New Zealand
| | - Norman Fenton
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Ali Fahmi
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Mariana Raniere Neves
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - William Marsh
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
| | - Scott McLachlan
- School of Electronic Engineering & Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK; Health Informatics and Knowledge Engineering Research (HiKER) Group
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Artificial Intelligence in Clinical Immunology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_83-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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40
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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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