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Wang F, Ma A, Wu Z, Xie M, Lun P, Sun P. Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia. Front Neurosci 2023; 17:1188590. [PMID: 37877009 PMCID: PMC10591183 DOI: 10.3389/fnins.2023.1188590] [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: 03/17/2023] [Accepted: 09/18/2023] [Indexed: 10/26/2023] Open
Abstract
The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model's diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder.
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Affiliation(s)
- Fuxu Wang
- Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Anbang Ma
- Shanghai Xunshi Technology Co., Ltd., Shanghai, China
| | - Zeyu Wu
- Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mingchen Xie
- Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Peng Lun
- Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Peng Sun
- Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, China
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Selvarajoo K, Giuliani A. Systems Biology and Omics Approaches for Complex Human Diseases. Biomolecules 2023; 13:1080. [PMID: 37509116 PMCID: PMC10377378 DOI: 10.3390/biom13071080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023] Open
Abstract
For many years, there has been general interest in developing virtual cells or digital twin models [...].
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Affiliation(s)
- Kumar Selvarajoo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
- Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore 117456, Singapore
- School of Biological Sciences, Nanyang Technological University (NTU), Singapore 639798, Singapore
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
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Omobolaji Alabi R, Sjöblom A, Carpén T, Elmusrati M, Leivo I, Almangush A, Mäkitie AA. Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL. Int J Med Inform 2023; 175:105064. [PMID: 37094545 DOI: 10.1016/j.ijmedinf.2023.105064] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND In recent years, there has been a surge in machine learning-based models for diagnosis and prognostication of outcomes in oncology. However, there are concerns relating to the model's reproducibility and generalizability to a separate patient cohort (i.e., external validation). OBJECTIVES This study primarily provides a validation study for a recently introduced and publicly available machine learning (ML) web-based prognostic tool (ProgTOOL) for overall survival risk stratification of oropharyngeal squamous cell carcinoma (OPSCC). Additionally, we reviewed the published studies that have utilized ML for outcome prognostication in OPSCC to examine how many of these models were externally validated, type of external validation, characteristics of the external dataset, and diagnostic performance characteristics on the internal validation (IV) and external validation (EV) datasets were extracted and compared. METHODS We used a total of 163 OPSCC patients obtained from the Helsinki University Hospital to externally validate the ProgTOOL for generalizability. In addition, PubMed, OvidMedline, Scopus, and Web of Science databases were systematically searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS The ProgTOOL produced a predictive performance of 86.5% balanced accuracy, Mathew's correlation coefficient of 0.78, Net Benefit (0.7) and Brier score (0.06) for overall survival stratification of OPSCC patients as either low-chance or high-chance. In addition, out of a total of 31 studies found to have used ML for the prognostication of outcomes in OPSCC, only seven (22.6%) reported a form of EV. Three studies (42.9%) each used either temporal EV or geographical EV while only one study (14.2%) used expert as a form of EV. Most of the studies reported a reduction in performance when externally validated. CONCLUSION The performance of the model in this validation study indicates that it may be generalized, therefore, bringing recommendations of the model for clinical evaluation closer to reality. However, the number of externally validated ML-based models for OPSCC is still relatively small. This significantly limits the transfer of these models for clinical evaluation and subsequently reduces the likelihood of the use of these models in daily clinical practice. As a gold standard, we recommend the use of geographical EV and validation studies to reveal biases and overfitting of these models. These recommendations are poised to facilitate the implementation of these models in clinical practice.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Anni Sjöblom
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Timo Carpén
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Pathology, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
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Mehrpour O, Hoyte C, Al Masud A, Biswas A, Schimmel J, Nakhaee S, Nasr MS, Delva-Clark H, Goss F. Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opin Drug Metab Toxicol 2023; 19:367-380. [PMID: 37395108 DOI: 10.1080/17425255.2023.2232724] [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: 09/04/2022] [Accepted: 06/30/2023] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs. RESEARCH DESIGN & METHODS Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied. RESULTS There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively). CONCLUSION Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.
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Affiliation(s)
- Omid Mehrpour
- Michigan Poison & Drug Information Center, Wayne State University School of Medicine, Detroit, MI, USA
| | - Christopher Hoyte
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Ashis Biswas
- Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA
| | - Jonathan Schimmel
- Department of Emergency Medicine, Division of Medical Toxicology, Mount Sinai Hospital Icahn School of Medicine, New York, NY, USA
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Mohammad Sadegh Nasr
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA
| | | | - Foster Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA
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Canosa A, Martino A, Manera U, Vasta R, Grassano M, Palumbo F, Cabras S, Di Pede F, Arena V, Moglia C, Giuliani A, Calvo A, Chiò A, Pagani M. Role of brain 2-[ 18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis. Eur J Nucl Med Mol Imaging 2023; 50:784-791. [PMID: 36308536 PMCID: PMC9852209 DOI: 10.1007/s00259-022-05987-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/29/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[18F]FDG-PET) as an independent predictor of survival in ALS. METHODS A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[18F]FDG-PET at diagnosis and whose survival time was available. We discretized the survival time in a finite number of classes in a data-driven fashion by employing a k-means-like strategy. We identified "hot brain regions" with maximal power in discriminating survival classes, by evaluating the Laplacian scores in a class-aware fashion. We retained the top-m features for each class to train the classification systems (i.e., a support vector machine, SVM), using 10% of the ALS cohort as test set. RESULTS Data were discretized in three survival profiles: 0-2 years, 2-5 years, and > 5 years. SVM resulted in an error rate < 20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three. CONCLUSION Brain 2-[18F]FDG-PET along with artificial intelligence was able to predict with high accuracy the survival time range in our ALS cohort. Healthcare professionals can benefit from this prognostic tool for planning patients' management and follow-up. 2-[18F]FDG-PET represents a promising biomarker for individual patients' stratification in clinical trials. The lack of a multicentre external validation of the model warrants further studies to evaluate its generalization capability.
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Affiliation(s)
- Antonio Canosa
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy
| | - Alessio Martino
- grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.18038.320000 0001 2180 8787Department of Business and Management, LUISS University, Viale Romania 32, 00197 Rome, Italy
| | - Umberto Manera
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Rosario Vasta
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Maurizio Grassano
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Francesca Palumbo
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Sara Cabras
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Francesca Di Pede
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy
| | - Vincenzo Arena
- Positron Emission Tomography Centre AFFIDEA-IRMET S.p.A., Turin, Italy
| | - Cristina Moglia
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy
| | - Andrea Calvo
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.7605.40000 0001 2336 6580Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Adriano Chiò
- grid.7605.40000 0001 2336 6580ALS Centre, “Rita Levi Montalcini” Department of Neuroscience, University of Turin, Via Cherasco 15, 10126 Turin, Italy ,grid.432329.d0000 0004 1789 4477SC Neurologia 1U, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino, Turin, Italy ,grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.7605.40000 0001 2336 6580Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Marco Pagani
- grid.428479.40000 0001 2297 9633Institute of Cognitive Sciences and Technologies, C.N.R., Rome, Italy ,grid.24381.3c0000 0000 9241 5705Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
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Bizzarri F, Giuliani A, Mocenni C. Awareness: An empirical model. Front Psychol 2022; 13:933183. [PMID: 36571049 PMCID: PMC9780538 DOI: 10.3389/fpsyg.2022.933183] [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: 04/30/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022] Open
Abstract
In this work, we face the time-honored problem of the contraposition/integration of analytical and intuitive knowledge, and the impact of such interconnection on the onset of awareness resulting from human decision-making processes. Borrowing the definitions of concepts like intuition, tacit knowledge, uncertainty, metacognition, and emotions from the philosophical, psychological, decision theory, and economic points of view, we propose a skeletonized mathematical model grounded on Markov Decision Processes of these multifaceted concepts. Behavioral patterns that emerged from the solutions of the model enabled us to understand some relevant properties of the interaction between explicit (mainly analytical) and implicit (mainly holistic) knowledge. The impact of the roles played by the same factors for both styles of reasoning and different stages of the decision process has been evaluated. We have found that awareness emerges as a dynamic process allowing the decision-maker to switch from habitual to optimal behavior, resulting from a feedback mechanism of self-observation. Furthermore, emotions are embedded in the model as inner factors, possibly fostering the onset of awareness.
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Affiliation(s)
- Federico Bizzarri
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy
| | - Alessandro Giuliani
- Environment and Health Department, Istituto Superiore di Sanità, Rome, Italy
| | - Chiara Mocenni
- Department of Information Engineering and Mathematics, University of Siena, Siena, Italy,*Correspondence: Chiara Mocenni
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Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12092235. [PMID: 36140636 PMCID: PMC9498096 DOI: 10.3390/diagnostics12092235] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/17/2022] Open
Abstract
Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.
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Martino A, Giuliani A. Editorial: Prediction and explanation in biomedicine using network-based approaches. Front Genet 2022; 13:967936. [PMID: 36118879 PMCID: PMC9479126 DOI: 10.3389/fgene.2022.967936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Alessio Martino
- Department of Business and Management, LUISS University, Rome, Italy
- *Correspondence: Alessio Martino,
| | - Alessandro Giuliani
- Department of Environment and Health, Italian National Institute of Health, Rome, Italy
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Wang LR, Choy XY, Goh WWB. Doppelgänger Spotting in Biomedical Gene Expression Data. iScience 2022; 25:104788. [PMID: 35992056 PMCID: PMC9382272 DOI: 10.1016/j.isci.2022.104788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 05/13/2022] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
Doppelgänger effects (DEs) occur when samples exhibit chance similarities such that, when split across training and validation sets, inflates the trained machine learning (ML) model performance. This inflationary effect causes misleading confidence on the deployability of the model. Thus, so far, there are no tools for doppelgänger identification or standard practices to manage their confounding implications. We present doppelgangerIdentifier, a software suite for doppelgänger identification and verification. Applying doppelgangerIdentifier across a multitude of diseases and data types, we show the pervasive nature of DEs in biomedical gene expression data. We also provide guidelines toward proper doppelgänger identification by exploring the ramifications of lingering batch effects from batch imbalances on the sensitivity of our doppelgänger identification algorithm. We suggest doppelgänger verification as a useful procedure to establish baselines for model evaluation that may inform on whether feature selection and ML on the data set may yield meaningful insights. Doppelgänger effects inflate the machine learning performance Doppelgänger effects exist in RNA-Seq and microarray gene expression data Developed doppelgangerIdentifier, a software to identify and verify doppelgängers Provide guidelines for proper doppelgänger identification
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Affiliation(s)
- Li Rong Wang
- School of Computer Science and Engineering, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
| | - Xin Yun Choy
- School of Computer Science and Engineering, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
| | - Wilson Wen Bin Goh
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
- Centre for Biomedical Informatics, Nanyang Technological University, 60 Nanyang Drive, 637551, Singapore
- Corresponding author
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Obuseh M, Yu D, DeLaurentis P. Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms. Biomed Instrum Technol 2022. [PMID: 35749264 DOI: 10.2345/1943-5967-56.2.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications. MATERIALS AND METHODS We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared. RESULTS The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion. DISCUSSION These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability. CONCLUSION Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.
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Affiliation(s)
- Marian Obuseh
- Marian Obuseh is a PhD student in the School of Industrial Engineering at Purdue University in West Lafayette, IN.
| | - Denny Yu
- Denny Yu, PhD, is an assistant professor in the School of Industrial Engineering at Purdue University in West Lafayette, IN
| | - Poching DeLaurentis
- Poching DeLaurentis, PhD, was a research scientist in the Regenstrief Center for Healthcare Engineering at Purdue University in West Lafayette, IN, at the time this study was conducted
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Obuseh M, Yu D, DeLaurentis P. Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms. Biomed Instrum Technol 2022; 56:58-70. [PMID: 35749264 PMCID: PMC9767430 DOI: 10.2345/0899-8205-56.2.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications. MATERIALS AND METHODS We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared. RESULTS The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion. DISCUSSION These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability. CONCLUSION Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.
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Affiliation(s)
- Marian Obuseh
- Marian Obuseh is a PhD student in the School of Industrial Engineering at Purdue University in West Lafayette, IN.
| | - Denny Yu
- Denny Yu, PhD, is an assistant professor in the School of Industrial Engineering at Purdue University in West Lafayette, IN
| | - Poching DeLaurentis
- Poching DeLaurentis, PhD, was a research scientist in the Regenstrief Center for Healthcare Engineering at Purdue University in West Lafayette, IN, at the time this study was conducted
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Selva KJ, van de Sandt CE, Lemke MM, Lee CY, Shoffner SK, Chua BY, Davis SK, Nguyen THO, Rowntree LC, Hensen L, Koutsakos M, Wong CY, Mordant F, Jackson DC, Flanagan KL, Crowe J, Tosif S, Neeland MR, Sutton P, Licciardi PV, Crawford NW, Cheng AC, Doolan DL, Amanat F, Krammer F, Chappell K, Modhiran N, Watterson D, Young P, Lee WS, Wines BD, Mark Hogarth P, Esterbauer R, Kelly HG, Tan HX, Juno JA, Wheatley AK, Kent SJ, Arnold KB, Kedzierska K, Chung AW. Systems serology detects functionally distinct coronavirus antibody features in children and elderly. Nat Commun 2021; 12:2037. [PMID: 33795692 PMCID: PMC8016934 DOI: 10.1038/s41467-021-22236-7] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 02/26/2021] [Indexed: 02/08/2023] Open
Abstract
The hallmarks of COVID-19 are higher pathogenicity and mortality in the elderly compared to children. Examining baseline SARS-CoV-2 cross-reactive immunological responses, induced by circulating human coronaviruses (hCoVs), is needed to understand such divergent clinical outcomes. Here we show analysis of coronavirus antibody responses of pre-pandemic healthy children (n = 89), adults (n = 98), elderly (n = 57), and COVID-19 patients (n = 50) by systems serology. Moderate levels of cross-reactive, but non-neutralizing, SARS-CoV-2 antibodies are detected in pre-pandemic healthy individuals. SARS-CoV-2 antigen-specific Fcγ receptor binding accurately distinguishes COVID-19 patients from healthy individuals, suggesting that SARS-CoV-2 infection induces qualitative changes to antibody Fc, enhancing Fcγ receptor engagement. Higher cross-reactive SARS-CoV-2 IgA and IgG are observed in healthy elderly, while healthy children display elevated SARS-CoV-2 IgM, suggesting that children have fewer hCoV exposures, resulting in less-experienced but more polyreactive humoral immunity. Age-dependent analysis of COVID-19 patients, confirms elevated class-switched antibodies in elderly, while children have stronger Fc responses which we demonstrate are functionally different. These insights will inform COVID-19 vaccination strategies, improved serological diagnostics and therapeutics.
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Affiliation(s)
- Kevin J Selva
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Carolien E van de Sandt
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Melissa M Lemke
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Christina Y Lee
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Suzanne K Shoffner
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Brendon Y Chua
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Samantha K Davis
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Thi H O Nguyen
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Louise C Rowntree
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Luca Hensen
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Marios Koutsakos
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Chinn Yi Wong
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Francesca Mordant
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - David C Jackson
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Katie L Flanagan
- Department of Infectious Diseases and Tasmanian Vaccine Trial Centre, Launceston General Hospital, Launceston, TAS, Australia
- School of Health Sciences and School of Medicine, University of Tasmania, Launceston, TAS, Australia
- Department of Immunology and Pathology, Monash University, Melbourne, VIC, Australia
- School of Health and Biomedical Science, RMIT University, Melbourne, VIC, Australia
| | - Jane Crowe
- Deepdene Surgery, Deepdene, VIC, Australia
| | - Shidan Tosif
- Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of General Medicine, Royal Children's Hospital Melbourne, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Melanie R Neeland
- Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Philip Sutton
- Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Paul V Licciardi
- Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Nigel W Crawford
- Infection and Immunity, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Immunisation Service, Royal Children's Hospital Melbourne, Melbourne, VIC, Australia
| | - Allen C Cheng
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
- Infection Prevention & Healthcare Epidemiology Unit, Alfred Health, Melbourne, VIC, Australia
| | - Denise L Doolan
- Centre for Molecular Therapeutics, Australian Institute of Tropical Health & Medicine, James Cook University, Cairns, QLD, Australia
| | - Fatima Amanat
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Florian Krammer
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Keith Chappell
- School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Naphak Modhiran
- School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Daniel Watterson
- School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Paul Young
- School of Chemistry and Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Wen Shi Lee
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Bruce D Wines
- Immune Therapies Group, Burnet Institute, Melbourne, VIC, Australia
- Department of Clinical Pathology, University of Melbourne, Melbourne, VIC, Australia
- Department of Immunology and Pathology, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - P Mark Hogarth
- Immune Therapies Group, Burnet Institute, Melbourne, VIC, Australia
- Department of Clinical Pathology, University of Melbourne, Melbourne, VIC, Australia
- Department of Immunology and Pathology, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Robyn Esterbauer
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of Melbourne, Melbourne, VIC, Australia
| | - Hannah G Kelly
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of Melbourne, Melbourne, VIC, Australia
| | - Hyon-Xhi Tan
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of Melbourne, Melbourne, VIC, Australia
| | - Jennifer A Juno
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
| | - Adam K Wheatley
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen J Kent
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia
- ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, University of Melbourne, Melbourne, VIC, Australia
- Melbourne Sexual Health Centre, Department of Infectious Diseases, Alfred Health, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Kelly B Arnold
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia.
| | - Amy W Chung
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia.
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Ho SY, Phua K, Wong L, Bin Goh WW. Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability. PATTERNS (NEW YORK, N.Y.) 2020; 1:100129. [PMID: 33294870 PMCID: PMC7691387 DOI: 10.1016/j.patter.2020.100129] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We discuss the validation of machine learning models, which is standard practice in determining model efficacy and generalizability. We argue that internal validation approaches, such as cross-validation and bootstrap, cannot guarantee the quality of a machine learning model due to potentially biased training data and the complexity of the validation procedure itself. For better evaluating the generalization ability of a learned model, we suggest leveraging on external data sources from elsewhere as validation datasets, namely external validation. Due to the lack of research attractions on external validation, especially a well-structured and comprehensive study, we discuss the necessity for external validation and propose two extensions of the external validation approach that may help reveal the true domain-relevant model from a candidate set. Moreover, we also suggest a procedure to check whether a set of validation datasets is valid and introduce statistical reference points for detecting external data problems.
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Affiliation(s)
- Sung Yang Ho
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Kimberly Phua
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Limsoon Wong
- Department of Computer Science, National University of Singapore, Singapore 117417, Singapore
| | - Wilson Wen Bin Goh
- School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
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August TA, Pescott OL, Joly A, Bonnet P. AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery. PATTERNS (NEW YORK, N.Y.) 2020; 1:100116. [PMID: 33205140 PMCID: PMC7660428 DOI: 10.1016/j.patter.2020.100116] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/04/2020] [Accepted: 09/07/2020] [Indexed: 11/09/2022]
Abstract
The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword "flower" across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis.
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Affiliation(s)
- Tom A. August
- UK Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK
| | - Oliver L. Pescott
- UK Centre for Ecology and Hydrology, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK
| | - Alexis Joly
- INRIA Sophia-Antipolis - ZENITH Team, LIRMM - UMR 5506 - CC 477, 161 Rue Ada, 34095 Montpellier Cedex 5, France
| | - Pierre Bonnet
- AMAP, Univ Montpellier, CIRAD, CNRS, INRA, IRD, Montpellier, France
- CIRAD, UMR AMAP, Montpellier, France
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Ho SY, Tan S, Sze CC, Wong L, Goh WWB. What can Venn diagrams teach us about doing data science better? INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2020. [DOI: 10.1007/s41060-020-00230-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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