Xianghao Zhan is a 5th -year Ph.D. candidate at Stanford Bioengineering. He obtained his M.S in Bioengineering in 2021 and his M.S in Statistics in 2023 both at Stanford. Before that he got B. Eng. in Control Science and Engineering (Automation) and his B. Art in English Language and Literature with Summa Cum Laude at Chu Kochen Honors College, Zhejiang University, China, in 2019.

Under the guidance of Prof. Oliver Gevaert and Prof. David B. Camarillo, he mainly focuses on the optimization of computational modeling of traumatic brain injury with machine learning and animal modeling based on biomechanical and radiological data. His research interests and projects also extend to the data mining of free-text clinical notes with natural language processing, biomedical data fusion for COVID-19 patient outcome prediction, machine learning reliability quantification with conformal prediction, reliability-based semi-supervised learning, and domain adaptation for biomedical sensory systems (with artificial olfaction systems and surface electromyography systems). He has published 18 peer-reviewed articles as a first/co-first author (IF 136.1) in such journals as NPJ Digital Medicine, IEEE Transactions on Biomedical and Health Informatics, IEEE Transactions on Biomedical Engineering, Journal of Sport and Health Science, with 4 first-author journal articles under review. He has been a peer reviewer for 16 journals including Annals of Biomedical Engineering, Journal of Neurotrauma, Computer methods in biomechanics and biomedical engineering.

In addition to his research, he has two master degrees while pursuing his Ph.D. degree: BIOE 2021 and STATS 2023. He has taken more than 10 data science and machine learning courses at Stanford with course project experiences and technical background with UNet-based image segmentation, BERT, Transformer-XL, DeepSEA, BPNet, VAE/SSVAE, flow model, energy-based model cycle-GAN, CNN-based image classification, LSTM-based clinical event prediction, Bi-LSTM-based neural machine translation, BERT, DCT/DWT/STFT, PCA, DRCA, NFL, convex optimization.

His research is recognized by the field and he was awarded with IET Postgraduate Research Award for an Outstanding Researcher (one awardee across the globe, first Chinese), Siebel Scholar Class of 2024, IET Healthcare Technology William James Award (one awardee across the globe), Stanford Interdisciplinary Graduate Fellowship (highest honor for interdisciplinary Stanford graduates), Pfeiffer Research Foundation Fellow, AMIA Trainee Award (six awardees, the only Chinese), American Society of Neurotrauma Trainee Award (20 awardees, the only Chinese), Chu Kochen Scholarship (12/23,000), Ten most Preeminent Students of Zhejiang University (10/36,000), Chinese National Scholarship (Top 0.2%).

He is dedicated to support underrepresented minorities. He has been a program leader for Stanford Summer Research Program and mentored 3 undergrads from the underrepresented minorities. He has been a research mentor at Foothill College for two years and mentored latino students from local community college. Additionally, he is a sports fan with 13 Stanford Intramural champions (10 volleyball, 3 tennis) and two medals from regional volleyball tournaments. He enjoys the sport passion and team spirits as a captain.

Honors & Awards

  • The Gustavus and Louise Pfeiffer Research Graduate Fellowship, Stanford University (09/25/2023)
  • Siebel Scholar Class of 2024, Siebal Foundation (08/24/2023)
  • 2022 IET Postgraduate Scholarship for an Outstanding Researcher, The Institute of Engineering and Technology (7/31/2022)
  • 2022 Stanford Interdisciplinary Graduate Fellowship (SIGF), Stanford University, Wu Tsai Neuroscience Institute (5/27/2022)
  • 2022 IET Healthcare Technologies Student and Early Career Awards - William James Award, The Institute of Engineering and Technology (9/23/2022)
  • Pfeiffer Research Foundation Fellow, Stanford University (09/17/2022)
  • 2021 Chinese Government Award for Outstanding Self-financed Students Abroad, Chinese Ministry of Education (7/28/2022)
  • 8-time Intramural Volleyball Champions, Stanford University (08/18/2023)
  • 3-time Intramural Tennis Champions, Stanford University (8/27/2021)
  • 2023 AMIA IS23 LEAD Trainee and Early Career Meeting Scholarship, American Medical Informatics Association (2/22/2023)
  • 2022 National Neurotrauma Society Trainee Travel Award, American National Neurotrauma Society (6/29/2022)
  • 2022 IET National Travel Award, The Institute of Engineering and Technology (10/6/2022)
  • ISCB 2022 Best Presenter Award (CAMDA Tract), International Society of Computational Biology (7/12/2022)
  • ISMB/ECCB 2021 Best Talk Award (CAMDA Tract), ISMB/ECCB (7/30/2021)
  • IET PresentIn10 Competition Category Winner of Healthy Lives; International Grand Finalist (Top 3), The Institution of Engineering and Technology (7/6/2021)
  • James Clark Graduate Fellowship, Stanford University (09/14/2020)
  • 2019 IET Cyber-systems and Robotics Research Article Contest. Second Prize. (Top 5 Awardee), IET Cyber-systems and Robotics (11/24/2019)
  • Winner of IET Present Around The World National Final 2019, Institute of Engineering and Technology(IET) (5/31/2019)
  • 2019 IEEE International Conference on Bioinformatics and Computational Biology Best Presenter, 2019 IEEE International Conference on Bioinformatics and Computational Biology (3/24/2019)
  • Chu Kochen Scholarship (Summa Cum Laude), Zhejiang University (12/31/2018)
  • Ten Most Preeminent Students of 2017, Zhejiang University (12/31/2017)
  • Chinese National Scholarship, Ministry of Education of the People’s Republic of China (12/31/2016)
  • Excellent Graduate of Zhejiang Province, Department of Education of Zhejiang Province (7/1/2019)
  • First-level fellowship of Chinese Instrument and Control Society, Chinese Instrument and Control Society (4/28/2019)
  • Cambridge Trust International Scholarship, University of Cambridge (2/18/2019)
  • 2018-2020 Zhebao-Ali Jike Award Project, Zhejiang University (1/2/2020)
  • Chu Kochen Honors College Preeminence Scholarship, Chu Kochen Honors College, Zhejiang University (12/20/2018)
  • Chunhui Scholarship and Chunhui Star Honor, College of Control Science and Engineering, Zhejiang University (06/01/2019)

Professional Affiliations and Activities

  • Student Member, American Medical Informatics Association (2023 - Present)
  • Student Member, IEEE Engineering in Medicine and Biology Society (2022 - Present)
  • Student Member, American National Neurotrauma Society (2022 - Present)
  • Graduate Student Member, Chinese Neurotrauma Scholar Association (2022 - Present)
  • Student Member, The Institution of Engineering and Technology (IET) (2021 - Present)
  • Member, Chinese Automation Society (2018 - Present)
  • Member, Chinese Instrument and Control Society (2019 - Present)

Education & Certifications

  • Certificate, Stanford Graduate School of Business, Ignite Program (2024)
  • Master of Science, Stanford University, STATS-MS (2023)
  • Master of Science, Stanford University, BIOE-MS (2021)
  • B. Eng, Zhejiang University, Automation (Control Science and Engineering), Summa Cum Laude (2019)
  • B. Art, Zhejiang University, English Language and Literature, Summa Cum Laude (2019)
  • Non-degree programme, University of Cambridge, Graduation Project on Acoustic Particle Trapping (2019)
  • Non-degree programme, UCLA, Cross-disciplinary Scholars in Science and Engineering, Radiology and MRI (2018)

Service, Volunteer and Community Work

  • Program Manager at Stanford Summer Research Program, Stanford University School of Medicine (5/1/2021 - 9/1/2021)

    I was excited and honored to mentor three undergrads in their summer research at Stanford in 2021 and provided my instructions and experience in research strategy, computational framework and graduate school application.


    Stanford University

  • Research Mentor at Foothill College SLI Internship, Foothill College (1/12/2022 - Present)

    I was excited and honored to mentor a undergrad from the Foothill College for three quarters, a local community college, on the project of statistic analysis of the predictive factors of kinematics in the prediction of force on the helmet and the head during head impacts for three quarters.


    Foothill College

  • Volunteer of Stanford Brain Day, Stanford University (2/27/2020 - 2/27/2020)

    I went to Jane Leland Stanford Middle School and acted as a volunteer in the Brain Day activity. I helped the middle school students know more about brain anatomy, brain function, animal brain comparison and how science looks like. Greatly appreciate this opportunity to help me bridge the gap between researchers and the cute students whose curiosity also inspire and refresh me.


    Jane Leland Stanford Middle School

  • Captain of Chu Kochen Honors College Men's Volleyball Team, Chu Kochen Honors College, Zhejiang University (December 12, 2016 - October 30, 2018)

    I am the Captain of Chu Kochen Honors College Men's Volleyball Team from 2016 to 2018. I led routine training, organize teams for matches, play and give instructions in matches, manage the whole team with trips and group activities. I have held the life experience sharing forum for 3 consecutive semesters and invited many famous senior teachers and students to share their experiences and life philosophies with my team members. I also launched an interest-driven research, Volleyball Planning Based on Machine Learning and Operational Research to help catalyze the combination between study and recreation together with the idea of helping those junior players to enjoy the experiences in the team.


    Zhejiang University

  • Teaching assistant at University of Washington/University of Wisconsin China Summer Lab, College of Chemical Engineering and Bioengineering (June 27, 2018 - July 10, 2018)

    I was the teaching assistant at the summer lab held by Zhejiang University, University of Wisconsin-Madison and University of Washington at College of Chemical Engineering and Bioengineering, In addition to help those participants of the summer lab with my knowledge of rectification tower, I also help the communication between students and the teacher by doing interactive interpretation. We had a great time over the 2 weeks of time.


    Zhejiang University

  • Volunteer and organizer of AIESEC International Volunteer Project in Zhengzhou, AIESEC (July 1, 2016 - August 1, 2016)

    I was the organizer and a volunteer at “Dare to Dream” AIESEC International Volunteer Project in Zhengzhou. I worked with volunteers from Egypt, Russia, Malaysia and Taiwan to prepare both cultural and technical courses for local middle school students in order to broaden their horizons as Zhengzhou is an area where the education is more exam-oriented. We help to open up a new window for them to look outside the area with insights on exotic cultures, college life, advanced science and technology. I did lectures on medical imaging, my experience in medical device company visits in 2015 in MN, USA, my experience in college and public speeches. I also helped coordinated with the international volunteers. The total workload was approximately 15 hours/week, 4 weeks/year in 2016.


    Yixin School, Xinghua South St., Zhengzhou, China

  • Volunteer at Ramsey County Library System in Minnesota, Ramsey County Library System, Minnesota (June 1, 2015 - July 10, 2015)

    I was a Ramsey County Library System volunteer in Minnesota. I did the sorting and management of books in the library in Shoreview (majorly) and Mounts View. The total work time was about 6 hours/week, 3 weeks/year in 2015


    Shoreview Library

  • Leader of Chu Kochen International and Translation Workshop, Chu Kochen Honors College, Zhejiang University (September 1, 2018 - July 21, 2019)

    Work on the translation and interactive interpretation in events of international communication of Chu Kochen Honors College, Zhejiang University


    Zhejiang University

  • Member at Zhejiang University Youth League, Zhejiang University Youth League, Zhejiang University (September 14, 2015 - September 2, 2016)

    Work on organization, propagation, summary and launching the summer social practice system in Zhejiang University. Help students get access to available sites for social practice across from China.


    Zhejiang University

  • Member at Chu Kochen Honors College Student Union, Chu Kochen Honors College Student Union, Zhejiang University (September 14, 2015 - September 1, 2016)

    Media and propagation of Chu Kochen Honors College events and news organized by student union


    Zhejiang University

  • “Big Data and New Development Motivation in Guiyang Province” Summer social practice and field trip, Zhejiang University (August 1, 2016 - August 15, 2016)

    “Big Data and New Development Motivation in Guiyang Province” was the major summer social practice and field trip I have done in my first summer vacation during my undergraduate life. As Guiyang Province is the newly established center for big-data research and industries, I led a group to go to Guiyang Province to delve further into this area. We did survey with local people on related topics, discussion with governmental officials, visits to big-data exhibition centers and visits to hospitals and high speed railway stations and online marketing to know detailed information of how data can help boost civil life in specific sectors.


    Guizhou, Guiyang, China

  • Founder of ZML Lab-A Platform for the Publication of Science and Sharing of Researches, Stanford University (10/22/2019 - Present)

    Xianghao is the Founder of ZML Lab. It is a voluntary internet-based platform for the free-of-charge publication of science and researches based on a WeChat Public Account. More than 50 professional researchers (postdocs, graduate students, etc.) have been invited as the appointed authors. So far, 10 articles have been published and 450 people have subscribed this platform with more than 10,000 reads.


    Shriram Center, Stanford

  • Captain of Lightening Storm, Winner of Stanford Intramural Volleyball League Division 1-2020, Stanford University (1/22/2020 - 3/3/2020)

    Captain of Lightening Storm, Winner of Stanford Intramural Volleyball League Division 1-2020, Opposite Spiker


    Stanford University

  • Cofounder of Case Crossover COVID-19 Science Communication Platform, Harvard University (3/23/2020 - Present)

    This platform is cofounded by Harvard Healthcare Club (Student association at Harvard School of Public Health), ZML Lab (A platform founded by Xianghao Zhan at Stanford University) and JHU Chinese Public Health Forum (Student organization of JHU School of Public Health at Johns Hopkins University)


    Harvard University

Personal Interests

In my spare time, I love playing volleyball, badminton, tennis and singing. I have award-winning experiences respecting these four hobbies in my undergraduate life. You could find me if planning to play volleyball, beach volleyball, badminton and tennis or if you plan to go karaoke. Meanwhile, I am keen and passionate in languages. I love communicating with different people in English, doing C-E bilingual translation and I am currently learning Spanish for half a year. With these interests, I am really eager to make more friends here on Stanford Campus.

Research Interests

  • Brain and Learning Sciences
  • Data Sciences
  • Literacy and Language

All Publications

  • Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Zhan, X., Liu, Y., Cecchi, N. J., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2024; 71 (6): 1853-1863


    The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM.To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models.The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate.This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

    View details for DOI 10.1109/TBME.2024.3354192

    View details for Web of Science ID 001230139500007

    View details for PubMedID 38224520

  • A wearable hydraulic shock absorber with efficient energy dissipation INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES Cecchi, N. J., Liu, Y., Vegesna, R. V., Zhan, X., Yang, W., Campomanes, L., Grant, G. A., Camarillo, D. B. 2024; 270
  • Differences In Strain Distribution Across Brain Regions In Non-concussive Collegiate Football Head Impacts Le Flao, E., Zhan, X., Cecchi, N. J., Liu, Y., Callan, A. A., Watson, L. P., Pang, C., Grant, G. A., Zeineh, M. M., Camarillo, D. B. LIPPINCOTT WILLIAMS & WILKINS. 2023: 416
  • Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation. ArXiv Zhan, X., Sun, J., Liu, Y., Cecchi, N. J., Le Flao, E., Gevaert, O., Zeineh, M. M., Camarillo, D. B. 2023


    Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.

    View details for PubMedID 37332565

    View details for PubMedCentralID PMC10274939

  • Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings. Annals of biomedical engineering Cecchi, N. J., Callan, A. A., Watson, L. P., Liu, Y., Zhan, X., Vegesna, R. V., Pang, C., Le Flao, E., Grant, G. A., Zeineh, M. M., Camarillo, D. B. 2023


    Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory andon the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5m/s (range: 9.7 to 39.6%), 18% at 5.5m/s (range: -5.5 to 40.5%), and 10% at 7.4m/s (range: -6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.

    View details for DOI 10.1007/s10439-023-03169-2

    View details for PubMedID 36917295

  • Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics. Journal of sport and health science Zhan, X., Li, Y., Liu, Y., Cecchi, N. J., Raymond, S. J., Zhou, Z., Alizadeh, H. V., Ruan, J., Barbat, S., Tiernan, S., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2023


    Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification.Data was analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain.The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2-value than baseline models without classification.The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

    View details for DOI 10.1016/j.jshs.2023.03.003

    View details for PubMedID 36921692

  • Correction: Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards. Annals of biomedical engineering Cecchi, N. J., Domel, A. G., Liu, Y., Rice, E., Lu, R., Zhan, X., Zhou, Z., Raymond, S. J., Sami, S., Singh, H., Rangel, I., Watson, L. P., Kleiven, S., Zeineh, M., Camarillo, D. B., Grant, G. 2023

    View details for DOI 10.1007/s10439-022-03132-7

    View details for PubMedID 36593307

  • Laboratory And On-field Testing Of A Commercially Available Padded Helmet Cover Cecchi, N. J., Callan, A. A., Watson, L. P., Liu, Y., Zhan, X., Zeineh, M. M., Grant, G. A., Camarillo, D. B. LIPPINCOTT WILLIAMS & WILKINS. 2022: 45
  • Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts. Annals of biomedical engineering Zhan, X., Li, Y., Liu, Y., Cecchi, N. J., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2022


    In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s2 led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.

    View details for DOI 10.1007/s10439-022-03020-0

    View details for PubMedID 35922726

  • Reliably Filter Drug-induced Liver Injury Literature with Natural Language Processing and Conformal Prediction. IEEE journal of biomedical and health informatics Zhan, X., Wang, F., Gevaert, O. 2022; PP


    Drug-induced liver injury describes the adverse effects of drugs that damage liver. Life-threatening results including liver failure or death were also reported in severe cases. Therefore, the events related to liver injury are strictly monitored for all approved drugs and the liver toxicity is an important assessments for new drug candidates. These reports are documented in research papers that contain preliminary in vitro and in vivo experiments. Conventionally, data extraction from previous publications relies heavily on resource-demanding manual labelling, which considerably restricted the efficiency of the information extraction. The development of natural lan- guage processing techniques enables the automatic processing of biomedical texts. Herein, based on around 28,000 papers (titles and abstracts) provided by the Critical Assessment of Massive Data Analysis challenge, this study benchmarked model performances on filtering liver-damage-related literature. Among five text embedding techniques, the model using term frequency- inverse document frequency (TF-IDF) and logistic regression outperformed others with an accuracy of 0.957 on the valida- tion set. Furthermore, an ensemble model with similar overall performances was developed with a logistic regression model on the predicted probability given by separate models with different vectorization techniques. The ensemble model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the hold-out validation data in the challenge. Moreover, important words in positive/negative predictions were identified via model interpretation. The prediction reliability was quantified with conformal prediction, which provides users with a control over the prediction uncertainty. Overall, the ensemble model and TF- IDF model reached satisfactory classification results, which can be used by researchers to rapidly filter literature that describes events related to liver injury induced by medications.

    View details for DOI 10.1109/JBHI.2022.3193365

    View details for PubMedID 35877798

  • Find the spatial co-variation of brain deformation with principal component analysis. IEEE transactions on bio-medical engineering Zhan, X., Liu, Y., Cecchi, N. J., Gevaert, O., Zeineh, M., Grant, G., Camarillo, D. B. 2022; PP


    Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types, we apply principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS ×MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and use the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM.PC1 explained >80% variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

    View details for DOI 10.1109/TBME.2022.3163230

    View details for PubMedID 35349430

  • Unsupervised Cross-User Adaptation in Taste Sensation Recognition Based on Surface Electromyography IEEE Transactions on Instrumentation and Measurement Wang, H., Zhan, X., Liu, L., Ullah, A., Li, H., Gao, H., Wang, Y., Hu, R., Li, G. 2022; 71

    View details for DOI 10.1109/TIM.2022.3160834

  • CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose IEEE Transactions on Instrumentation and Measurement Liu, L., Zhan, X., Yang, X., Guan, X., Wu, R., Wang, Z., Luo, Z., Wang, Y., Li, G. 2022

    View details for DOI 10.1109/TIM.2021.3134321

  • Translational models of mild traumatic brain injury tissue biomechanics Current Opinion in Biomedical Engineering Zhan, X., Oeur, A., Liu, Y., Zeineh, M. M., Grant, G. A., Margulies, S. S., Camarillo, D. B. 2022; 24
  • Towards a comprehensive delineation of white matter tract-related deformation. Journal of neurotrauma Zhou, Z., Li, X., Liu, Y., Fahlstedt, M., Georgiadis, M., Zhan, X., Raymond, S. J., Grant, G., Kleiven, S., Camarillo, D. B., Zeineh, M. 2021


    Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

    View details for DOI 10.1089/neu.2021.0195

    View details for PubMedID 34617451

  • Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards. Annals of biomedical engineering Cecchi, N. J., Domel, A. G., Liu, Y., Rice, E., Lu, R., Zhan, X., Zhou, Z., Raymond, S. J., Sami, S., Singh, H., Rangel, I., Watson, L. P., Kleiven, S., Zeineh, M., Camarillo, D. B., Grant, G. 2021


    Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain's response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0's validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s2 for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.

    View details for DOI 10.1007/s10439-021-02853-5

    View details for PubMedID 34549342

  • Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation. Annals of biomedical engineering Zhan, X., Li, Y., Liu, Y., Domel, A. G., Alizadeh, H. V., Zhou, Z., Cecchi, N. J., Raymond, S. J., Tiernan, S., Ruan, J., Barbat, S., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2021


    Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

    View details for DOI 10.1007/s10439-021-02813-z

    View details for PubMedID 34244908

  • Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football. Annals of biomedical engineering Liu, Y., Domel, A. G., Cecchi, N. J., Rice, E., Callan, A. A., Raymond, S. J., Zhou, Z., Zhan, X., Li, Y., Zeineh, M. M., Grant, G. A., Camarillo, D. B. 2021


    Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40ms and a post-trigger time of 70ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200ms time window recorded by the mouthguard. Therefore, approximately 110ms is recommended for complete modeling of impacts for football.

    View details for DOI 10.1007/s10439-021-02821-z

    View details for PubMedID 34231091

  • AI-based analysis of CT images for rapid triage of COVID-19 patients. NPJ digital medicine Xu, Q., Zhan, X., Zhou, Z., Li, Y., Xie, P., Zhang, S., Li, X., Yu, Y., Zhou, C., Zhang, L., Gevaert, O., Lu, G. 2021; 4 (1): 75


    The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n=1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n=700) and Cohort 3 (n=662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p<0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: .

    View details for DOI 10.1038/s41746-021-00446-z

    View details for PubMedID 33888856

  • Rapid Estimation of Entire Brain Strain Using Deep Learning Models IEEE Transactions on Biomedical Engineering Zhan, X., Liu, Y., Raymond, S. J., Vahid Alizadeh, H., Domel, A. G., Gevaert, O., Zeineh, M. M., Grant, G. A., Camarillo, D. 2021: 11


    Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications.We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts.The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization.Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts.In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

    View details for DOI 10.1109/TBME.2021.3073380

  • Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases Patterns Zhan, X., Humbert-Droz, M., Mukherjee, P., Gevaert, O. 2021: 100289
  • Boost AI Power: Data Augmentation Strategies with Unlabeled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose IEEE Sensors Journal Liu, L., Zhan, X., Wu, R., Guan, X., Wang, Z., Pilanci, M., Luo, Z., Li, G., Wang, Y. 2021: 1-11
  • The relationship between brain injury criteria and brain strain across different types of head impacts can be different Journal of Royal Society Interface Zhan, X., Li, Y., Liu, Y., Domel, A. G., Vahid Alidazeh, H., Raymond, S. J., Ruan, J., Barbat, S., Tienan, S., Gevaert, O., Zeineh, M., Grant, G., Camarillo, D. 2021; 18 (20210260)

    View details for DOI 10.1098/rsif.2021.0260

  • An Optimized Deep Convolutional Neural Network for Dendrobium Classification Based on Electronic Nose Sensors and Actuators A: Physical Wang, Y., Diao, J., Wang, Z., Zhan, X., Zhang, B., Li, N., Li, G. 2020; 302
  • An electronic nose-based assistive diagnostic prototype for lung cancer detection with conformal prediction Measurement Zhan, X., Wang, Z., Yang, M., Luo, Z., Wang, Y., Li, G. 2020
  • Particle Trapping with Modulated Acoustic Wave 2019 Chinese Automation Congress Zhan, X., Li, N., Stevenson, A. C., Li, G., Hu, R. 2019
  • Discrimination between Alternative Herbal Medicines from Different Categories with the Electronic Nose SENSORS Zhan, X., Guan, X., Wu, R., Wang, Z., Wang, Y., Li, G. 2018; 18 (9)


    As alternative herbal medicine gains soar in popularity around the world, it is necessary to apply a fast and convenient means for classifying and evaluating herbal medicines. In this work, an electronic nose system with seven classification algorithms is used to discriminate between 12 categories of herbal medicines. The results show that these herbal medicines can be successfully classified, with support vector machine (SVM) and linear discriminant analysis (LDA) outperforming other algorithms in terms of accuracy. When principal component analysis (PCA) is used to lower the number of dimensions, the time cost for classification can be reduced while the data is visualized. Afterwards, conformal predictions based on 1NN (1-Nearest Neighbor) and 3NN (3-Nearest Neighbor) (CP-1NN and CP-3NN) are introduced. CP-1NN and CP-3NN provide additional, yet significant and reliable, information by giving the confidence and credibility associated with each prediction without sacrificing of accuracy. This research provides insight into the construction of a herbal medicine flavor library and gives methods and reference for future works.

    View details for DOI 10.3390/s18092936

    View details for Web of Science ID 000446940600195

    View details for PubMedID 30181445

    View details for PubMedCentralID PMC6165400

  • NU-WAY, an Application of Numerical Methods in Campus Running Route Evaluation Zhan Xianghao, Yang Xikai, Zhang Jianming, IEEE IEEE. 2018: 798–803