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Atzil-Slonim D, Eliassaf A, Warikoo N, Paz A, Haimovitz S, Mayer T, Gurevych I. Leveraging natural language processing to study emotional coherence in psychotherapy. Psychotherapy (Chic) 2024; 61:82-92. [PMID: 38236227 DOI: 10.1037/pst0000517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
The association between emotional experience and expression, known as emotional coherence, is considered important for individual functioning. Recent advances in natural language processing (NLP) make it possible to automatically recognize verbally expressed emotions in psychotherapy dialogues and to explore emotional coherence with larger samples and finer granularity than previously. The present study used state-of-the-art emotion recognition models to automatically label clients' emotions at the utterance level, employed these labeled data to examine the coherence between verbally expressed emotions and self-reported emotions, and examined the associations between emotional coherence and clients' improvement in functioning throughout treatment. The data comprised 872 transcribed sessions from 68 clients. Clients self-reported their functioning before each session and their emotions after each. A subsample of 196 sessions were manually coded. A transformer-based approach was used to automatically label the remaining data for a total of 139,061 utterances. Multilevel modeling was used to assess emotional coherence and determine whether it was associated with changes in clients' functioning throughout treatment. The emotion recognition model demonstrated moderate performance. The findings indicated a significant association between verbally expressed emotions and self-reported emotions. Coherence in clients' negative emotions was associated with improvement in functioning. The results suggest an association between clients' subjective experience and their verbal expression of emotions and underscore the importance of this coherence to functioning. NLP may uncover crucial emotional processes in psychotherapy. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | | | - Neha Warikoo
- Department of Computer Science, Technical University of Darmstadt
| | - Adar Paz
- Department of Psychology, Bar-Ilan University
| | | | - Tobias Mayer
- Department of Computer Science, Technical University of Darmstadt
| | - Iryna Gurevych
- Department of Computer Science, Technical University of Darmstadt
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Dulovic A, Kessel B, Harries M, Becker M, Ortmann J, Griesbaum J, Jüngling J, Junker D, Hernandez P, Gornyk D, Glöckner S, Melhorn V, Castell S, Heise JK, Kemmling Y, Tonn T, Frank K, Illig T, Klopp N, Warikoo N, Rath A, Suckel C, Marzian AU, Grupe N, Kaiser PD, Traenkle B, Rothbauer U, Kerrinnes T, Krause G, Lange B, Schneiderhan-Marra N, Strengert M. Comparative Magnitude and Persistence of Humoral SARS-CoV-2 Vaccination Responses in the Adult Population in Germany. Front Immunol 2022; 13:828053. [PMID: 35251012 PMCID: PMC8888837 DOI: 10.3389/fimmu.2022.828053] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 01/17/2022] [Indexed: 12/01/2022] Open
Abstract
Recent increases in SARS-CoV-2 infections have led to questions about duration and quality of vaccine-induced immune protection. While numerous studies have been published on immune responses triggered by vaccination, these often focus on studying the impact of one or two immunisation schemes within subpopulations such as immunocompromised individuals or healthcare workers. To provide information on the duration and quality of vaccine-induced immune responses against SARS-CoV-2, we analyzed antibody titres against various SARS-CoV-2 antigens and ACE2 binding inhibition against SARS-CoV-2 wild-type and variants of concern in samples from a large German population-based seroprevalence study (MuSPAD) who had received all currently available immunisation schemes. We found that homologous mRNA-based or heterologous prime-boost vaccination produced significantly higher antibody responses than vector-based homologous vaccination. Ad26.CoV2S.2 performance was particularly concerning with reduced titres and 91.7% of samples classified as non-responsive for ACE2 binding inhibition, suggesting that recipients require a booster mRNA vaccination. While mRNA vaccination induced a higher ratio of RBD- and S1-targeting antibodies, vector-based vaccines resulted in an increased proportion of S2-targeting antibodies. Given the role of RBD- and S1-specific antibodies in neutralizing SARS-CoV-2, their relative over-representation after mRNA vaccination may explain why these vaccines have increased efficacy compared to vector-based formulations. Previously infected individuals had a robust immune response once vaccinated, regardless of which vaccine they received, which could aid future dose allocation should shortages arise for certain manufacturers. Overall, both titres and ACE2 binding inhibition peaked approximately 28 days post-second vaccination and then decreased.
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Affiliation(s)
- Alex Dulovic
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Barbora Kessel
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Manuela Harries
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Matthias Becker
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Julia Ortmann
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johanna Griesbaum
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Jennifer Jüngling
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Daniel Junker
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Pilar Hernandez
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Daniela Gornyk
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Stephan Glöckner
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Vanessa Melhorn
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Stefanie Castell
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Jana-Kristin Heise
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Yvonne Kemmling
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Torsten Tonn
- German Red Cross Blood Donation Service North East, Dresden, Germany
| | - Kerstin Frank
- German Red Cross Blood Donation Service North East, Dresden, Germany
| | - Thomas Illig
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Norman Klopp
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
| | - Neha Warikoo
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Angelika Rath
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Christina Suckel
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Anne Ulrike Marzian
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Nicole Grupe
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Philipp D. Kaiser
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Bjoern Traenkle
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
| | - Ulrich Rothbauer
- NMI Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
- Pharmaceutical Biotechnology, Department of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Tobias Kerrinnes
- Department of RNA-Biology of Bacterial Infections, Helmholtz Institute for RNA-Based Infection Research, Würzburg, Germany
| | - Gérard Krause
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a Joint Venture of the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Braunschweig, Germany
| | - Berit Lange
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Braunschweig, Germany
| | | | - Monika Strengert
- Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- TWINCORE, Centre for Experimental and Clinical Infection Research, a Joint Venture of the Hannover Medical School and the Helmholtz Centre for Infection Research, Hannover, Germany
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Warikoo N, Chang YC, Hsu WL. LBERT: Lexically aware Transformer-based Bidirectional Encoder Representation model for learning universal bio-entity relations. Bioinformatics 2021; 37:404-412. [PMID: 32810217 DOI: 10.1093/bioinformatics/btaa721] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 06/30/2020] [Accepted: 08/13/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Natural Language Processing techniques are constantly being advanced to accommodate the influx of data as well as to provide exhaustive and structured knowledge dissemination. Within the biomedical domain, relation detection between bio-entities known as the Bio-Entity Relation Extraction (BRE) task has a critical function in knowledge structuring. Although recent advances in deep learning-based biomedical domain embedding have improved BRE predictive analytics, these works are often task selective or use external knowledge-based pre-/post-processing. In addition, deep learning-based models do not account for local syntactic contexts, which have improved data representation in many kernel classifier-based models. In this study, we propose a universal BRE model, i.e. LBERT, which is a Lexically aware Transformer-based Bidirectional Encoder Representation model, and which explores both local and global contexts representations for sentence-level classification tasks. RESULTS This article presents one of the most exhaustive BRE studies ever conducted over five different bio-entity relation types. Our model outperforms state-of-the-art deep learning models in protein-protein interaction (PPI), drug-drug interaction and protein-bio-entity relation classification tasks by 0.02%, 11.2% and 41.4%, respectively. LBERT representations show a statistically significant improvement over BioBERT in detecting true bio-entity relation for large corpora like PPI. Our ablation studies clearly indicate the contribution of the lexical features and distance-adjusted attention in improving prediction performance by learning additional local semantic context along with bi-directionally learned global context. AVAILABILITY AND IMPLEMENTATION Github. https://github.com/warikoone/LBERT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.,Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei 106, Taiwan.,Clinical Big Data Research Center, Taipei Medical University, Taipei 110, Taiwan.,Pervasive AI Research Labs, Ministry of Science and Technology, Hsinchu City 300, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan.,Pervasive AI Research Labs, Ministry of Science and Technology, Hsinchu City 300, Taiwan
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Chen CJ, Warikoo N, Chang YC, Chen JH, Hsu WL. Medical knowledge infused convolutional neural networks for cohort selection in clinical trials. J Am Med Inform Assoc 2019; 26:1227-1236. [PMID: 31390470 DOI: 10.1093/jamia/ocz128] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 06/18/2019] [Accepted: 07/04/2019] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE In this era of digitized health records, there has been a marked interest in using de-identified patient records for conducting various health related surveys. To assist in this research effort, we developed a novel clinical data representation model entitled medical knowledge-infused convolutional neural network (MKCNN), which is used for learning the clinical trial criteria eligibility status of patients to participate in cohort studies. MATERIALS AND METHODS In this study, we propose a clinical text representation infused with medical knowledge (MK). First, we isolate the noise from the relevant data using a medically relevant description extractor; then we utilize log-likelihood ratio based weights from selected sentences to highlight "met" and "not-met" knowledge-infused representations in bichannel setting for each instance. The combined medical knowledge-infused representation (MK) from these modules helps identify significant clinical criteria semantics, which in turn renders effective learning when used with a convolutional neural network architecture. RESULTS MKCNN outperforms other Medical Knowledge (MK) relevant learning architectures by approximately 3%; notably SVM and XGBoost implementations developed in this study. MKCNN scored 86.1% on F1metric, a gain of 6% above the average performance assessed from the submissions for n2c2 task. Although pattern/rule-based methods show a higher average performance for the n2c2 clinical data set, MKCNN significantly improves performance of machine learning implementations for clinical datasets. CONCLUSION MKCNN scored 86.1% on the F1 score metric. In contrast to many of the rule-based systems introduced during the n2c2 challenge workshop, our system presents a model that heavily draws on machine-based learning. In addition, the MK representations add more value to clinical comprehension and interpretation of natural texts.
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Affiliation(s)
- Chi-Jen Chen
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.,Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.,Pervasive AI Research Labs, Ministry of Science and Technology, Taipei, Taiwan
| | - Jin-Hua Chen
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Wen-Lian Hsu
- Pervasive AI Research Labs, Ministry of Science and Technology, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
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Warikoo N, Chang YC, Hsu WL. LPTK: a linguistic pattern-aware dependency tree kernel approach for the BioCreative VI CHEMPROT task. Database (Oxford) 2018; 2018:5139652. [PMID: 30346607 PMCID: PMC6196310 DOI: 10.1093/database/bay108] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Revised: 08/30/2018] [Accepted: 09/24/2018] [Indexed: 11/14/2022]
Abstract
Identifying the interactions between chemical compounds and genes from biomedical literatures is one of the frequently discussed topics of text mining in the life science field. In this paper, we describe Linguistic Pattern-Aware Dependency Tree Kernel, a linguistic interaction pattern learning method developed for CHEMPROT task-BioCreative VI, to capture chemical-protein interaction (CPI) patterns within biomedical literatures. We also introduce a framework to integrate these linguistic patterns with smooth partial tree kernel to extract the CPIs. This new method of feature representation models aspects of linguistic probability in geometric representation, which not only optimizes the sufficiency of feature dimension for classification, but also defines features as interpretable contexts rather than long vectors of numbers. In order to test the robustness and efficiency of our system in identifying different kinds of biological interactions, we evaluated our framework on three separate data sets, i.e. CHEMPROT corpus, Chemical-Disease Relation corpus and Protein-Protein Interaction corpus. Corresponding experiment results demonstrate that our method is effective and outperforms several compared systems for each data set.
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Affiliation(s)
- Neha Warikoo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Yung-Chun Chang
- Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
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