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Wang Y, Zhao W, Ross A, You L, Wang H, Zhou X. Revealing chronic disease progression patterns using Gaussian process for stage inference. J Am Med Inform Assoc 2024; 31:396-405. [PMID: 38055638 PMCID: PMC10797260 DOI: 10.1093/jamia/ocad230] [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: 08/11/2023] [Revised: 11/06/2023] [Accepted: 11/20/2023] [Indexed: 12/08/2023] Open
Abstract
OBJECTIVE The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. MATERIALS AND METHODS We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). RESULTS First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. CONCLUSION Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.
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Affiliation(s)
- Yanfei Wang
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Weiling Zhao
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Angela Ross
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Lei You
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Hongyu Wang
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
- Cizik School of Nursing, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
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Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Med Inform Decis Mak 2023; 23:271. [PMID: 38012655 PMCID: PMC10680172 DOI: 10.1186/s12911-023-02341-x] [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: 01/24/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Depression is one of the most significant health conditions in personal, social, and economic impact. The aim of this review is to summarize existing literature in which machine learning methods have been used in combination with Electronic Health Records for prediction of depression. METHODS Systematic literature searches were conducted within arXiv, PubMed, PsycINFO, Science Direct, SCOPUS and Web of Science electronic databases. Searches were restricted to information published after 2010 (from 1st January 2011 onwards) and were updated prior to the final synthesis of data (27th January 2022). RESULTS Following the PRISMA process, the initial 744 studies were reduced to 19 eligible for detailed evaluation. Data extraction identified machine learning methods used, types of predictors used, the definition of depression, classification performance achieved, sample size, and benchmarks used. Area Under the Curve (AUC) values more than 0.9 were claimed, though the average was around 0.8. Regression methods proved as effective as more developed machine learning techniques. LIMITATIONS The categorization, definition, and identification of the numbers of predictors used within models was sometimes difficult to establish, Studies were largely Western Educated Industrialised, Rich, Democratic (WEIRD) in demography. CONCLUSION This review supports the potential use of machine learning techniques with Electronic Health Records for the prediction of depression. All the selected studies used clinically based, though sometimes broad, definitions of depression as their classification criteria. The reported performance of the studies was comparable to or even better than that found in primary care. There are concerns with generalizability and interpretability.
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Affiliation(s)
| | - Caroline Meyer
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Lukasz Walasek
- Department of Psychology, University of Warwick, Coventry, UK
| | - Carla Toro
- Warwick Medical School, University of Warwick, Coventry, UK
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Fitzgerald O, Perez-Concha O, Gallego-Luxan B, Metke-Jimenez A, Rudd L, Jorm L. Continuous time recurrent neural networks: Overview and benchmarking at forecasting blood glucose in the intensive care unit. J Biomed Inform 2023; 146:104498. [PMID: 37699466 DOI: 10.1016/j.jbi.2023.104498] [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: 04/17/2023] [Revised: 09/07/2023] [Accepted: 09/09/2023] [Indexed: 09/14/2023]
Abstract
OBJECTIVE Blood glucose measurements in the intensive care unit (ICU) are typically made at irregular intervals. This presents a challenge in choice of forecasting model. This article gives an overview of continuous time autoregressive recurrent neural networks (CTRNNs) and evaluates how they compare to autoregressive gradient boosted trees (GBT) in forecasting blood glucose in the ICU. METHODS Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations through incorporating continuous evolution of the hidden states between observations. This is achieved using a neural ordinary differential equation (ODE) or neural flow layer. In this manuscript, we give an overview of these models, including the varying architectures that have been proposed to account for issues such as ongoing medical interventions. Further, we demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting using electronic medical record and simulated data and compare with GBT and linear models. RESULTS The experiments confirm that addition of a neural ODE or neural flow layer generally improves the performance of autoregressive recurrent neural networks in the irregular measurement setting. However, several CTRNN architecture are outperformed by a GBT model (Catboost), with only a long short-term memory (LSTM) and neural ODE based architecture (ODE-LSTM) achieving comparable performance on probabilistic forecasting metrics such as the continuous ranked probability score (ODE-LSTM: 0.118 ± 0.001; Catboost: 0.118 ± 0.001), ignorance score (0.152 ± 0.008; 0.149 ± 0.002) and interval score (175 ± 1; 176 ± 1). CONCLUSION The application of deep learning methods for forecasting in situations with irregularly measured time series such as blood glucose shows promise. However, appropriate benchmarking by methods such as GBT approaches (plus feature transformation) are key in highlighting whether novel methodologies are truly state of the art in tabular data settings.
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Affiliation(s)
- Oisin Fitzgerald
- Centre for Big Data Research in Health, Level 2, AGSM Building, UNSW Sydney, NSW 2052, Australia.
| | - Oscar Perez-Concha
- Centre for Big Data Research in Health, Level 2, AGSM Building, UNSW Sydney, NSW 2052, Australia
| | - Blanca Gallego-Luxan
- Centre for Big Data Research in Health, Level 2, AGSM Building, UNSW Sydney, NSW 2052, Australia
| | - Alejandro Metke-Jimenez
- Australian e-Health Research Centre, Level 7, STARS Building - Surgical Treatment and Rehabilitation Service, 296 Herston Road, Herston, QLD 4029, Australia
| | - Lachlan Rudd
- Data and Analytics, eHealth NSW, 1 Reserve Road, St Leonards NSW 2065, Australia
| | - Louisa Jorm
- Centre for Big Data Research in Health, Level 2, AGSM Building, UNSW Sydney, NSW 2052, Australia
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Tassi S, Kigka V, Siogkas P, Rocchiccioli S, Pelosi G, Fotiadis DI, Sakellarios AI. Graph-guided Gaussian Process-based Diagnosis of CVD Severity with Uncertainty Measures. 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: 38082986 DOI: 10.1109/embc40787.2023.10340916] [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
The severity of coronary artery disease can be assessed invasively using the Fractional Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the treatment approach. The present work capitalizes a Gaussian process (GP) framework over graphs for the prediction of FFR index using only non-invasive imaging and clinical features. More specifically, taking the per-node one-hop connectivity vector as input, we employed a regression-based task by applying an ensemble of graph-adapted Gaussian process experts, with a data-adaptive fashion via online training. The main novelty of the work lies in the fact that for the first time in a medical field the inference model considers only the similarity condition of the patients, instead of their features. Our results demonstrate the impressive merits of the proposed medical EGP (MedEGP) method, in comparison to the single GP, and Linear Regression (LR) models to predict the FFR index, with well-calibrated uncertainty.Clinical Relevance- This paper establishes an accurate non-invasive approach to predict the FFR for the diagnosis of coronary artery disease.
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Yarnell CJ, Angriman F, Ferreyro BL, Liu K, De Grooth HJ, Burry L, Munshi L, Mehta S, Celi L, Elbers P, Thoral P, Brochard L, Wunsch H, Fowler RA, Sung L, Tomlinson G. Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts. Crit Care 2023; 27:67. [PMID: 36814287 PMCID: PMC9944781 DOI: 10.1186/s13054-023-04307-x] [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: 12/02/2022] [Accepted: 01/06/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND The optimal thresholds for the initiation of invasive ventilation in patients with hypoxemic respiratory failure are unknown. Using the saturation-to-inspired oxygen ratio (SF), we compared lower versus higher hypoxemia severity thresholds for initiating invasive ventilation. METHODS This target trial emulation included patients from the Medical Information Mart for Intensive Care (MIMIC-IV, 2008-2019) and the Amsterdam University Medical Centers (AmsterdamUMCdb, 2003-2016) databases admitted to intensive care and receiving inspired oxygen fraction ≥ 0.4 via non-rebreather mask, noninvasive ventilation, or high-flow nasal cannula. We compared the effect of using invasive ventilation initiation thresholds of SF < 110, < 98, and < 88 on 28-day mortality. MIMIC-IV was used for the primary analysis and AmsterdamUMCdb for the secondary analysis. We obtained posterior means and 95% credible intervals (CrI) with nonparametric Bayesian G-computation. RESULTS We studied 3,357 patients in the primary analysis. For invasive ventilation initiation thresholds SF < 110, SF < 98, and SF < 88, the predicted 28-day probabilities of invasive ventilation were 72%, 47%, and 19%. Predicted 28-day mortality was lowest with threshold SF < 110 (22.2%, CrI 19.2 to 25.0), compared to SF < 98 (absolute risk increase 1.6%, CrI 0.6 to 2.6) or SF < 88 (absolute risk increase 3.5%, CrI 1.4 to 5.4). In the secondary analysis (1,279 patients), the predicted 28-day probability of invasive ventilation was 50% for initiation threshold SF < 110, 28% for SF < 98, and 19% for SF < 88. In contrast with the primary analysis, predicted mortality was highest with threshold SF < 110 (14.6%, CrI 7.7 to 22.3), compared to SF < 98 (absolute risk decrease 0.5%, CrI 0.0 to 0.9) or SF < 88 (absolute risk decrease 1.9%, CrI 0.9 to 2.8). CONCLUSION Initiating invasive ventilation at lower hypoxemia severity will increase the rate of invasive ventilation, but this can either increase or decrease the expected mortality, with the direction of effect likely depending on baseline mortality risk and clinical context.
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Affiliation(s)
- Christopher J. Yarnell
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.231844.80000 0004 0474 0428Department of Medicine, Division of Respirology, University Health Network and Sinai Health System, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada
| | - Federico Angriman
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada ,grid.413104.30000 0000 9743 1587Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Bruno L. Ferreyro
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.231844.80000 0004 0474 0428Department of Medicine, Division of Respirology, University Health Network and Sinai Health System, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada
| | - Kuan Liu
- grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada
| | - Harm Jan De Grooth
- grid.12380.380000 0004 1754 9227Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Lisa Burry
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.492573.e0000 0004 6477 6457Department of Pharmacy and Medicine, Sinai Health System, Toronto, Canada ,grid.17063.330000 0001 2157 2938Leslie Dan Faculty of Pharmacy and Interdepartmental Division of Critical Care, University of Toronto, Toronto, ON Canada
| | - Laveena Munshi
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.231844.80000 0004 0474 0428Department of Medicine, Division of Respirology, University Health Network and Sinai Health System, Toronto, Canada
| | - Sangeeta Mehta
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.231844.80000 0004 0474 0428Department of Medicine, Division of Respirology, University Health Network and Sinai Health System, Toronto, Canada
| | - Leo Celi
- grid.116068.80000 0001 2341 2786Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142 USA ,grid.239395.70000 0000 9011 8547Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115 USA
| | - Paul Elbers
- grid.12380.380000 0004 1754 9227Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Thoral
- grid.12380.380000 0004 1754 9227Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Laurent Brochard
- grid.415502.7Keenan Research Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Unity Health Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada
| | - Hannah Wunsch
- grid.418647.80000 0000 8849 1617Institute for Clinical Evaluative Sciences, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada ,grid.413104.30000 0000 9743 1587Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Robert A. Fowler
- grid.17063.330000 0001 2157 2938Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Medicine, University of Toronto, Toronto, Canada ,grid.418647.80000 0000 8849 1617Institute for Clinical Evaluative Sciences, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada ,grid.413104.30000 0000 9743 1587Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Lillian Sung
- grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada ,grid.42327.300000 0004 0473 9646Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Canada
| | - George Tomlinson
- grid.231844.80000 0004 0474 0428Department of Medicine, University Health Network and Sinai Health System, Toronto, Canada ,grid.17063.330000 0001 2157 2938Institute of Health Policy, Management and Evaluation, University of Toronto, Medical-Surgical ICU, 10th floor, 585 University Avenue, Toronto, ON M5G 1X5 Canada
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Yarnell CJ, Johnson A, Dam T, Jonkman A, Liu K, Wunsch H, Brochard L, Celi LA, De Grooth HJ, Elbers P, Mehta S, Munshi L, Fowler RA, Sung L, Tomlinson G. Do Thresholds for Invasive Ventilation in Hypoxemic Respiratory Failure Exist? A Cohort Study. Am J Respir Crit Care Med 2023; 207:271-282. [PMID: 36150166 DOI: 10.1164/rccm.202206-1092oc] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Rationale: Invasive ventilation is a significant event for patients with respiratory failure. Physiologic thresholds standardize the use of invasive ventilation in clinical trials, but it is unknown whether thresholds prompt invasive ventilation in clinical practice. Objectives: To measure, in patients with hypoxemic respiratory failure, the probability of invasive ventilation within 3 hours after meeting physiologic thresholds. Methods: We studied patients admitted to intensive care receiving FiO2 of 0.4 or more via nonrebreather mask, noninvasive positive pressure ventilation, or high-flow nasal cannula, using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database (2008-2019) and the Amsterdam University Medical Centers Database (AmsterdamUMCdb) (2003-2016). We evaluated 17 thresholds, including the ratio of arterial to inspired oxygen, the ratio of saturation to inspired oxygen ratio, composite scores, and criteria from randomized trials. We report the probability of invasive ventilation within 3 hours of meeting each threshold and its association with covariates using odds ratios (ORs) and 95% credible intervals (CrIs). Measurements and Main Results: We studied 4,726 patients (3,365 from MIMIC, 1,361 from AmsterdamUMCdb). Invasive ventilation occurred in 28% (1,320). In MIMIC, the highest probability of invasive ventilation within 3 hours of meeting a threshold was 20%, after meeting prespecified neurologic or respiratory criteria while on vasopressors, and 19%, after a ratio of arterial to inspired oxygen of <80 mm Hg. In AmsterdamUMCdb, the highest probability was 34%, after vasopressor initiation, and 25%, after a ratio of saturation to inspired oxygen of <90. The probability after meeting the threshold from randomized trials was 9% (MIMIC) and 13% (AmsterdamUMCdb). In MIMIC, a race/ethnicity of Black (OR, 0.75; 95% CrI, 0.57-0.96) or Asian (OR, 0.6; 95% CrI, 0.35-0.95) compared with White was associated with decreased probability of invasive ventilation after meeting a threshold. Conclusions: The probability of invasive ventilation within 3 hours of meeting physiologic thresholds was low and associated with patient race/ethnicity.
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Affiliation(s)
- Christopher J Yarnell
- Interdepartmental Division of Critical Care Medicine.,Institute of Health Policy, Management and Evaluation, and.,Division of Respirology
| | | | - Tariq Dam
- Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Annemijn Jonkman
- Department of Intensive Care Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Kuan Liu
- Institute of Health Policy, Management and Evaluation, and
| | - Hannah Wunsch
- Interdepartmental Division of Critical Care Medicine.,Institute of Health Policy, Management and Evaluation, and.,Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Laurent Brochard
- Interdepartmental Division of Critical Care Medicine.,Keenan Research Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts.,Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.,Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts; and
| | - Harm-Jan De Grooth
- Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Paul Elbers
- Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Department of Intensive Care Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Sangeeta Mehta
- Interdepartmental Division of Critical Care Medicine.,Division of Respirology
| | - Laveena Munshi
- Interdepartmental Division of Critical Care Medicine.,Division of Respirology
| | - Robert A Fowler
- Interdepartmental Division of Critical Care Medicine.,Institute of Health Policy, Management and Evaluation, and.,Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Division of Haematology/Oncology.,Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Lillian Sung
- Institute of Health Policy, Management and Evaluation, and.,Division of Haematology/Oncology
| | - George Tomlinson
- Institute of Health Policy, Management and Evaluation, and.,Department of Medicine, University Health Network and Sinai Health System, Toronto, Ontario, Canada
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Geng X, He X, Xu L, Yu J. Attention-based gating optimization network for multivariate time series prediction. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Tell Me Something Interesting: Clinical Utility of Machine Learning Prediction Models in the ICU. J Biomed Inform 2022; 132:104107. [DOI: 10.1016/j.jbi.2022.104107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/26/2022] [Accepted: 05/28/2022] [Indexed: 11/18/2022]
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Liu X, Wang M, Aftab R. Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM. Front Bioeng Biotechnol 2022; 10:791424. [PMID: 35309999 PMCID: PMC8924408 DOI: 10.3389/fbioe.2022.791424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/06/2022] [Indexed: 11/20/2022] Open
Abstract
In order to more accurately and comprehensively characterize the changes and development rules of lesion characteristics in pulmonary medical images in different periods, the study was conducted to predict the evolution of pulmonary nodules in the longitudinal dimension of time, and a benign and malignant prediction model of pulmonary lesions in different periods was constructed under multiscale three-dimensional (3D) feature fusion. According to the sequence of computed tomography (CT) images of patients at different stages, 3D interpolation was conducted to generate 3D lung CT images. The 3D features of different size lesions in the lungs were extracted using 3D convolutional neural networks for fusion features. A time-modulated long short-term memory was constructed to predict the benign and malignant lesions by using the improved time-length memory method to learn the feature vectors of lung lesions with temporal and spatial characteristics in different periods. The experiment shows that the area under the curve of the proposed method is 92.71%, which is higher than that of the traditional method.
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Affiliation(s)
- Xindong Liu
- Faculty of Science, Hong Kong Baptist University, Hong Kong, China
| | - Mengnan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Rukhma Aftab
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
- *Correspondence: Rukhma Aftab,
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Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-77211-6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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