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Michalowski M, Wilk S, Michalowski W, Rao M, Carrier M. Provision and evaluation of explanations within an automated planning-based approach to solving the multimorbidity problem. J Biomed Inform 2024; 156:104681. [PMID: 38960273 DOI: 10.1016/j.jbi.2024.104681] [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: 01/18/2024] [Revised: 05/28/2024] [Accepted: 06/22/2024] [Indexed: 07/05/2024]
Abstract
The multimorbidity problem involves the identification and mitigation of adverse interactions that occur when multiple computer interpretable guidelines are applied concurrently to develop a treatment plan for a patient diagnosed with multiple diseases. Solving this problem requires decision support approaches which are difficult to comprehend for physicians. As such, the rationale for treatment plans generated by these approaches needs to be provided. OBJECTIVE To develop an explainability component for an automated planning-based approach to the multimorbidity problem, and to assess the fidelity and interpretability of generated explanations using a clinical case study. METHODS The explainability component leverages the task-network model for representing computer interpretable guidelines. It generates post-hoc explanations composed of three aspects that answer why specific clinical actions are in a treatment plan, why specific revisions were applied, and how factors like medication cost, patient's adherence, etc. influence the selection of specific actions. The explainability component is implemented as part of MitPlan, where we revised our planning-based approach to support explainability. We developed an evaluation instrument based on the system causability scale and other vetted surveys to evaluate the fidelity and interpretability of its explanations using a two dimensional comparison study design. RESULTS The explainability component was implemented for MitPlan and tested in the context of a clinical case study. The fidelity and interpretability of the generated explanations were assessed using a physician-focused evaluation study involving 21 participants from two different specialties and two levels of experience. Results show that explanations provided by the explainability component in MitPlan are of acceptable fidelity and interpretability, and that the clinical justification of the actions in a treatment plan is important to physicians. CONCLUSION We created an explainability component that enriches an automated planning-based approach to solving the multimorbidity problem with meaningful explanations for actions in a treatment plan. This component relies on the task-network model to represent computer interpretable guidelines and as such can be ported to other approaches that also use the task-network model representation. Our evaluation study demonstrated that explanations that support a physician's understanding of the clinical reasons for the actions in a treatment plan are useful and important.
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Affiliation(s)
| | - Szymon Wilk
- Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
| | - Wojtek Michalowski
- Telfer School of Management, University of Ottawa, 55 Laurier Ave East, Ottawa, ON K1N 6N5, Canada
| | - Malvika Rao
- Telfer School of Management, University of Ottawa, 55 Laurier Ave East, Ottawa, ON K1N 6N5, Canada
| | - Marc Carrier
- The Ottawa Hospital, 725 Parkdale Ave, Ottawa, ON K1Y 4E9, Canada
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Li C, Qiang X. Advancing reliability and efficiency of urban communication: Unmanned aerial vehicles, intelligent reflection surfaces, and deep learning techniques. Heliyon 2024; 10:e32472. [PMID: 38912507 PMCID: PMC11193030 DOI: 10.1016/j.heliyon.2024.e32472] [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: 02/29/2024] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/25/2024] Open
Abstract
Unmanned aerial vehicles (UAVs) have garnered attention for their potential to improve wireless communication networks by establishing line-of-sight (LoS) connections. However, urban environments pose challenges such as tall buildings and trees, impacting communication pathways. Intelligent reflection surfaces (IRSs) offer a solution by creating virtual LoS routes through signal reflection, enhancing reliability and coverage. This paper presents a three-dimensional dynamic channel model for UAV-assisted communication systems with IRSs. Additionally, it proposes a novel channel-tracking approach using deep learning and artificial intelligence techniques, comprising preliminary estimation with a deep neural network and continuous monitoring with a Stacked Bidirectional Long and Short-Term Memory (Bi-LSTM) model. Simulation results demonstrate faster convergence and superior performance compared to benchmarks, highlighting the effectiveness of integrating IRSs into UAV-enabled communication for enhanced reliability and efficiency.
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Affiliation(s)
- Chongyang Li
- Hunan Post And Telecommunication College, Hunan Changsha, 410015, China
| | - Xiaohu Qiang
- Hunan Post And Telecommunication College, Hunan Changsha, 410015, China
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3
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Nicora G, Catalano M, Bortolotto C, Achilli MF, Messana G, Lo Tito A, Consonni A, Cutti S, Comotto F, Stella GM, Corsico A, Perlini S, Bellazzi R, Bruno R, Preda L. Bayesian Networks in the Management of Hospital Admissions: A Comparison between Explainable AI and Black Box AI during the Pandemic. J Imaging 2024; 10:117. [PMID: 38786571 PMCID: PMC11122655 DOI: 10.3390/jimaging10050117] [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: 03/06/2024] [Revised: 04/24/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are "black box" to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffers from poor generalization ability in the presence of dataset shifts. Here, we present a comparison between an explainable-by-design ("white box") model (Bayesian Network (BN)) versus a black box model (Random Forest), both studied with the aim of supporting clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.
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Affiliation(s)
- Giovanna Nicora
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy; (G.N.); (R.B.)
| | - Michele Catalano
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
| | - Chandra Bortolotto
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Marina Francesca Achilli
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
| | - Gaia Messana
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
| | - Antonio Lo Tito
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
| | - Alessio Consonni
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
| | - Sara Cutti
- Medical Direction, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy;
| | | | - Giulia Maria Stella
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (G.M.S.); (A.C.); (S.P.)
- Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Angelo Corsico
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (G.M.S.); (A.C.); (S.P.)
- Unit of Respiratory Diseases, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Stefano Perlini
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy; (G.M.S.); (A.C.); (S.P.)
- Department of Emergency, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy; (G.N.); (R.B.)
| | - Raffaele Bruno
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
- Unit of Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Lorenzo Preda
- Diagnostic Imaging and Radiotherapy Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; (M.C.); (M.F.A.); (G.M.); (A.L.T.); (A.C.); (L.P.)
- Radiology Institute, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
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Dos Santos AL, Pinhati C, Perdigão J, Galante S, Silva L, Veloso I, Simões E Silva AC, Oliveira EA. Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review. Artif Intell Med 2024; 150:102824. [PMID: 38553164 DOI: 10.1016/j.artmed.2024.102824] [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: 07/10/2023] [Revised: 11/10/2023] [Accepted: 02/21/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVES We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19. METHODS We searched four databases for articles published between 01/01/2020 and 10/25/2023, describing the development of multivariable prediction models using any machine learning technique for predicting several outcomes in children and adolescents who had COVID-19. RESULTS We included ten articles, six (60 % [95 % confidence interval (CI) 0.31 - 0.83]) were predictive diagnostic models and four (40% [95 % CI 0.170.69]) were prognostic models. All models were developed to predict a binary outcome (n= 10/10, 100 % [95 % CI 0.72-1]). The most frequently predicted outcome was disease detection (n=3/10, 30% [95 % CI 0.11-0.60]). The most commonly used machine learning models in the studies were tree-based (n=12/33, 36.3% [95 % CI 0.17-0.47]) and neural networks (n=9/27, 33.2% [95% CI 0.15-0.44]). CONCLUSION Our review revealed that attention is required to address problems including small sample sizes, inconsistent reporting practices on data preparation, biases in data sources, lack of reporting metrics such as calibration and discrimination, hyperparameters and other aspects that allow reproducibility by other researchers and might improve the methodology.
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Affiliation(s)
- Adriano Lages Dos Santos
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil; Federal Institute of Education, Science and Technology of Minas Gerais (IFMG), Belo Horizonte, Brazil.
| | - Clara Pinhati
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Jonathan Perdigão
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Stella Galante
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Ludmilla Silva
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Isadora Veloso
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Ana Cristina Simões E Silva
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Eduardo Araújo Oliveira
- Department of Pediatrics, Health Sciences Postgraduate Program, School of Medicine, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
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Pogány D, Antal P. Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space. PLoS One 2024; 19:e0300906. [PMID: 38512848 PMCID: PMC10956837 DOI: 10.1371/journal.pone.0300906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/06/2024] [Indexed: 03/23/2024] Open
Abstract
Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.
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Affiliation(s)
- Domonkos Pogány
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Péter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
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Pandit JJ. "The Future Ain't What It Used to Be": Anesthesia Research, Practice, and Management in 2050. Anesth Analg 2024; 138:233-235. [PMID: 38215701 DOI: 10.1213/ane.0000000000006844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- Jaideep J Pandit
- From the Nuffield Department of Anaesthesia, University of Oxford, Oxford, United Kingdom
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7
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Lanotte F, O’Brien MK, Jayaraman A. AI in Rehabilitation Medicine: Opportunities and Challenges. Ann Rehabil Med 2023; 47:444-458. [PMID: 38093518 PMCID: PMC10767220 DOI: 10.5535/arm.23131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
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Affiliation(s)
- Francesco Lanotte
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Megan K. O’Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
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