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Multidomain postoperative recovery trajectories after lumbar and thoracolumbar spine surgery

Spine Journal 2025年

作者： Yakdan, Salim Zhang, Jingwen Benedict, Braeden Xu, Ziqi Javeed, Saad Zhang, Justin K. Steel, Benjamin A. Gupta, Vivek P. Botterbush, Kathleen Piccirillo, Jay F. Rodebaugh, Thomas L. Goodin, Burel R. Buchowski, Jacob M Neuman, Brian Hafez, Daniel Kelly, Michael Ray, Wilson Z. Lu, Chenyang Frumkin, Madelyn Greenberg, Jacob K. Department of Neurological Surgery Washington University School of Medicine St. Louis MO United States Department of Computer Science & Engineering Washington University in St. Louis St. Louis MO United States Department of Otolaryngology-Head and Neck Surgery Washington University School of Medicine St. Louis MO United States Department of Psychology and Neuroscience University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Anesthesiology Washington University St. Louis MO United States Department of Orthopedic Surgery Washington University School of Medicine St. Louis MO United States Department of Orthopedic Surgery Rady Children's Hospital University of California San Diego CA United States AI for Health Institute Washington University St. Louis MO United States Center for Technology and Behavioral Health Departments of Biomedical Data Science and Psychiatry Geisel School of Medicine at Dartmouth Lebanon NH United States

BACKGROUND CONTEXT: Understanding early postoperative recovery is crucial for improving perioperative care and long-term outcomes. Traditional recovery assessments relying primarily on cross-sectional patient-reported measures may not fully capture the complexity of the recovery process. PURPOSE: Our study evaluates early postoperative recovery using mobile health assessment and explores the relationship between early recovery patterns, multidomain recovery, and long-term outcomes. STUDY DESIGN/SETTING: A prospective observational study. PATIENT SAMPLE: A total of 129 patients were included in the analysis. OUTCOME MEASURES: Patient-reported outcome measures (PROMs) of pain, physical function, and disability at 1, 6 and 12 months postoperatively. METHODS: This study recruited patients aged 21 to 85 undergoing lumbar/thoracolumbar surgery for degenerative diseases. During the first month postoperatively, patients completed daily Ecological Momentary Assessment (EMA) surveys assessing pain, depression, and physical function and were passively monitored with Fitbit. EMA and Fitbit data (steps per minute, Maximum 30-minute cadence, and activity bout number) were clustered using functional principal component analysis. RESULTS: 129 patients were included (median age: 62;56% female). Two dominant recovery patterns were identified across all domains. At 12 months, patients with more favorable recovery across all domains except for activity bout number and depression, showed better outcomes. Favorable recovery in pain intensity and steps per minute was associated with greater improvements in all outcomes, while favorable recovery in maximum 30-minute cadence was associated with greater improvement in disability and physical function. Patients with less favorable pain recovery had a significantly higher complication rate (23% vs. 7%). A greater number of favorable recovery domains was associated with better 12-month outcomes improvement and demonstrated superior prognostic v

关键词： Degenerative spine disease Disability Ecological momentary assessment Fitbit Long-term outcomes Physical function Spine surgery

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Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction

arXiv

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arXiv 2019年

作者： Williams, Josie V. Razavian, Narges Departments of Computer Science Courant Institute of Mathematical Sciences New York University Departments of Population Health Radiology Center for Data Science New York University Langone Medical Center

As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients’ EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Physics-informed Deep Diffusion MRI Reconstruction with Synthetic data: Break Training data Bottleneck in Artificial Intelligence

arXiv

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arXiv 2022年

作者： Qian, Chen Gao, Yuncheng Han, Mingyang Wang, Zi Ruan, Dan Shen, Yu Wu, Yaping Zhou, Yirong Wang, Chengyan Jiang, Boyu Tao, Ran Wu, Zhigang Wang, Jiazheng Zhu, Liuhong Guo, Yi Kang, Taishan Lin, Jianzhong Gong, Tao Yang, Chen Fei, Guoqiang Lin, Meijin Guo, Di Zhou, Jianjun Wang, Meiyun Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University China Department of Medical Imaging Henan Provincial People's Hospital The People's Hospital of Zhengzhou University China Human Phenome Institute Fudan University China United Imaging Healthcare China Philips Healthcare China China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China Departments of Radiology Shandong Provincial Hospital Affiliated Shandong First Medical University China China Department of Neurology Zhongshan Hospital Fudan University China Department of Applied Marine Physics and Engineering Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Biomedical Research Institute Henan Academy of Sciences China

Diffusion magnetic resonance imaging (MRI) is the only imaging modality for non-invasive movement detection of in vivo water molecules, with significant clinical and research applications. Diffusion MRI (DWI) acquired by multi-shot techniques can achieve higher resolution, better signal-to-noise ratio, and lower geometric distortion than single-shot, but suffers from inter-shot motion-induced artifacts. These artifacts cannot be removed prospectively, leading to the absence of artifact-free training labels. Thus, the potential of deep learning in multi-shot DWI reconstruction remains largely untapped. To break the training data bottleneck, here, we propose a Physics-Informed Deep DWI reconstruction method (PIDD) to synthesize high-quality paired training data by leveraging the physical diffusion model (magnitude synthesis) and inter-shot motion-induced phase model (motion phase synthesis). The network is trained only once with 100,000 synthetic samples, achieving encouraging results on multiple realistic in vivo data reconstructions. Advantages over conventional methods include: (a) Better motion artifact suppression and reconstruction stability;(b) Outstanding generalization to multi-scenario reconstructions, including multi-resolution, multi-b-value, multi-undersampling, multi-vendor, and multi-center;(c) Excellent clinical adaptability to patients with verifications by seven experienced doctors (p Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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A graph transformer-based foundation model for brain functional connectivity network

引用

Pattern Recognition 2026年 169卷

作者： Yulong Wang Vince D Calhoun Godfrey D Pearlson Peter Kochunov Theo G.M. van Erp Yuhui Du School of Computer and Information Technology Shanxi University Taiyuan PR China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Departments of Psychiatry and Neurobiology Yale University New Haven CT USA Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School The University of Texas Health Science Center Houston TX USA Department of Psychiatry and Human Behavior School of Medicine University of California Irvine Irvine CA USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine CA USA

Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN). Our graph transformer model integrates node and edge embeddings to extract FCN features and adapts flexibly to classification, regression, and clustering via task-specific adapters. We validate the model on fMRI data from 10,718 scans across multiple tasks: gender classification, mental disorder classification (distinguishing schizophrenia or autism from healthy population), brain age prediction, and depressive and anxiety disorder biotyping. Compared to 14 competing methods, our model consistently outperforms them. Moreover, it facilitates biomarker discovery by identifying task-specific FC patterns. In summary, we present a novel, versatile foundation model for FCN that advances neuroimaging research through scalable and interpretable analysis.

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Vaccinations or Non-Pharmaceutical Interventions: Safe Reopening of Schools in England

Research Square

引用

Research Square 2021年

作者： Cuesta-Lazaro, Carolina Quera-Bofarull, Arnau Aylett-Bullock, Joseph Lawrence, Bryan N. Fong, Kevin Icaza-Lizaola, Miguel Sedgewick, Aidan Truong, Henry Vernon, Ian Williams, Julian Pagel, Christina Krauss, Frank Institute for Data Science Durham University Durham United Kingdom Institute for Computational Cosmology Durham University Durham United Kingdom Institute for Particle Physics Phenomenology Durham University Durham United Kingdom National Centre for Atmospheric Science University of Reading Reading United Kingdom Departments of Meteorology and Computer Science University of Reading Reading United Kingdom Department of Science Technology Engineering and Public Policy University College London London United Kingdom Department of Anaesthesia University College London Hospital London United Kingdom Centre for Extragalactic Astronomy Durham University Durham United Kingdom Department of Mathematical Sciences Durham University DurhamDH1 3LE United Kingdom Institute for Hazard Risk & Resilience Durham University DurhamDH1 3LE United Kingdom Clinical Operational Research Unit University College London United Kingdom

With high levels of the Delta variant of COVID-19 circulating in England during September 2021, schools are set to reopen with few school-based non-pharmaceutical interventions (NPIs). In this paper, we present simulation results obtained from the individual-based model, JUNE, for English school opening after a prior vaccination campaign using an optimistic set of assumptions about vaccine efficacy and the likelihood of prior-reinfection. We take a scenario-based approach to modelling potential interventions to assess relative changes rather than real-world forecasts. Specifically, we assess the effects of vaccinating those aged 16-17, those aged 12-17, and not vaccinating children at all relative to only vaccinating the adult population, addressing what might have happened had the UK began teenage vaccinations earlier. Vaccinating children in the 12-15 age group would have had a significant impact on the course of the epidemic, saving thousands of lives overall in these simulations. In the absence of such a vaccination campaign our simulations show there could still be a significant positive impact on the epidemic (fewer cases, fewer deaths) by continuing NPI strategies in schools. Our analysis suggests that the best results in terms of lives saved are likely derived from a combination of the now planned vaccination campaign and NPIs in schools. © 2021, CC BY.

关键词： COVID-19

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Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference

arXiv

引用

arXiv 2019年

作者： Cai, T. Tony Zhang, Anru R. Zhou, Yuchen Department of Statistics & Data Science The Wharton School University of Pennsylvania PhiladelphiaPA19104 United States Departments of Biostatistics & Bioinformatics Computer Science Mathematics and Statistical Science Duke University DurhamNC27710 United States

We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model – an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results. © 2019, CC BY-SA.

关键词： Convex optimization

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Robust self-supervised denoising of voltage imaging data using CellMincer

Npj imaging

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Npj imaging 2024年第1期2卷 51页

作者： Brice Wang Tianle Ma Theresa Chen Trinh Nguyen Ethan Crouse Stephen J Fleming Alison S Walker Vera Valakh Ralda Nehme Evan W Miller Samouil L Farhi Mehrtash Babadi Data Sciences Platform (DSP) Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Oakland University Rochester MI USA. Spatial Technology Platform (STP) Broad Institute of MIT and Harvard Cambridge MA USA. Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard Cambridge MA USA. Departments of Molecular & Cell Biology and Chemistry and Helen Wills Neuroscience Institute UC Berkeley Berkeley CA USA.

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer's state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5-2.9 dB) and reduces SNR variability by 17-55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

关键词： Cellular imaging Fluorescence imaging Image processing

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A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency

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npj Mental Health Research 2022年第1期1卷 19页

作者： Miner, Adam S. Fleming, Scott L. Haque, Albert Fries, Jason A. Althoff, Tim Wilfley, Denise E. Agras, W. Stewart Milstein, Arnold Hancock, Jeff Asch, Steven M. Stirman, Shannon Wiltsey Arnow, Bruce A. Shah, Nigam H. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA United States Center for Biomedical Informatics Research Stanford University Stanford CA United States Department of Biomedical Data Science Stanford University Stanford CA United States Department of Computer Science Stanford University Stanford CA United States Allen School of Computer Science & Engineering University of Washington Seattle WA United States Departments of Psychiatry Medicine Pediatrics and Psychological & Brain Sciences Washington University in St. Louis St. Louis MO United States Clinical Excellence Research Center Stanford University Stanford CA United States Department of Communication Stanford University Stanford CA United States VA Palo Alto Health Care System Palo Alto CA United States Division of Primary Care and Population Health Stanford University School of Medicine Stanford CA United States National Center for Posttraumatic Stress Disorders Dissemination and Training Division VA Palo Alto Healthcare System Menlo Park CA United States Technology and Digital Solutions Stanford Healthcare Stanford CA United States

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists. © The Author(s) 2022.

关键词： Translational research Human behaviour

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