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1. Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study

Author

Zong, Nansu, Ngo, Victoria, Stone, Daniel J, Wen, Andrew, Zhao, Yiqing, Yu, Yue, Liu, Sijia, Huang, Ming, Wang, Chen, and Jiang, Guoqian

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPrecision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions. ObjectiveThis study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries. MethodsWe extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance. ResultsWith 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review. ConclusionsAccurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.

Published
2021
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2. Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Author

Zhao, Yiqing, Fu, Sunyang, Bielinski, Suzette J, Decker, Paul A, Chamberlain, Alanna M, Roger, Veronique L, Liu, Hongfang, and Larson, Nicholas B

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundStroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events. ObjectiveThe aim of this study was to develop a machine learning–based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing. MethodsThe algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314). ResultsAmong the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample. ConclusionsWe developed and validated a machine learning–based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.

Published
2021
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7. Lifestyle Pattern Analysis Unveils Recovery Trajectories of Communities Impacted by Disasters

Author

Coleman, Natalie, Liu, Chenyue, Zhao, Yiqing, and Mostafavi, Ali

Subjects
Physics - Physics and Society
Abstract

The return of normalcy to the population's lifestyle is a critical recovery milestone in the aftermath of disasters, and delayed lifestyle recovery could lead to significant well-being impacts. Lifestyle recovery captures the collective effects of population activities and the restoration of infrastructure and business services. This study uses a novel approach to leverage privacy-enhanced location intelligence data to characterize distinctive lifestyle patterns and to unveil recovery trajectories after a disaster in the context of 2017 Hurricane Harvey in Harris County, Texas. The analysis integrates multiple data sources to record the number of visits from home census block groups (CBGs) to different points of interest during the baseline period and disruptive period. First, primary clustering using k-means characterized four distinct essential and non-essential lifestyle patterns. Then, secondary clustering characterized the impact of the hurricane into three recovery trajectories based on the severity of maximum disruption and duration of recovery. The results reveal multiple recovery trajectories and durations within each lifestyle cluster, which imply differential recovery rates among similar lifestyle and demographic groups. The findings offer a twofold theoretical significance: (1) lifestyle recovery is a critical milestone that needs to be examined, quantified, and monitored in the aftermath of disasters; (2) the spatial structures of cities formed by human mobility and distribution of facilities and extends the spatial reach of flood impacts on population lifestyles. The analysis and findings also provide novel data-driven insights for public official and emergency managers to examine, measure, and monitor a critical milestone in community recovery trajectory based on the return of lifestyles to normalcy., Comment: 26 pages, 6 figures, and 4 tables

Published
2022

10. Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records

Author

Zhao, Yiqing and Luo, Yuan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Delirium is a common acute onset brain dysfunction in the emergency setting and is associated with higher mortality. It is difficult to detect and monitor since its presentations and risk factors can be different depending on the underlying medical condition of patients. In our study, we aimed to identify subtypes within the delirium population and build subgroup-specific predictive models to detect delirium using Medical Information Mart for Intensive Care IV (MIMIC-IV) data. We showed that clusters exist within the delirium population. Differences in feature importance were also observed for subgroup-specific predictive models. Our work could recalibrate existing delirium prediction models for each delirium subgroup and improve the precision of delirium detection and monitoring for ICU or emergency department patients who had highly heterogeneous medical conditions.

Published
2021

17. Neutrophil extracellular traps induced by chemotherapy inhibit tumor growth in murine models of colorectal cancer

Author

Li, Yamu, Wu, Sulin, Zhao, Yiqing, Dinh, Trang, Jiang, Dongxu, Selfridge, J. Eva, Myers, George, Wang, Yuxiang, Zhao, Xuan, Tomchuck, Suzanne, Dubyak, George, Lee, Richard T., Estfan, Bassam, Shapiro, Marc, Kamath, Suneel, Mohamed, Amr, Huang, Stanley Ching-Cheng, Huang, Alex Y., Conlon, Ronald, Krishnamurthi, Smitha, Eads, Jennifer, Willis, Joseph E., Khorana, Alok A., Bajor, David, and Wang, Zhenghe

Subjects
Tumors -- Analysis -- Models, Drug therapy, Combination -- Models -- Analysis, Colorectal cancer -- Analysis -- Models, Nucleases -- Models -- Growth -- Analysis, Genetic engineering -- Growth -- Analysis -- Models, Chemotherapy -- Analysis -- Models, Proteins -- Models -- Analysis -- Growth, Cancer -- Chemotherapy, Thrombin -- Models -- Analysis -- Growth, Genetically modified organisms -- Analysis -- Growth -- Models, Company growth, Health care industry
Abstract

Neutrophil extracellular traps (NETs), a web-like structure of cytosolic and granule proteins assembled on decondensed chromatin, kill pathogens and cause tissue damage in diseases. Whether NETs can kill cancer cells is unexplored. Here, we report that a combination of glutaminase inhibitor CB-839 and 5-FU inhibited the growth of P/K3CA-mutant colorectal cancers (CRCs) in xenograft, syngeneic, and genetically engineered mouse models in part through NETs. Disruption of NETs by either DNase I treatment or depletion of neutrophils in CRCs attenuated the efficacy of the drug combination. Moreover, NETs were present in tumor biopsies from patients treated with the drug combination in a phase II clinical trial. Increased NET levels in tumors were associated with longer progression-free survival. Mechanistically, the drug combination induced the expression of IL-8 preferentially in P/OCA-mutant CRCs to attract neutrophils into the tumors. Further, the drug combination increased the levels of ROS in neutrophils, thereby inducing NETs. Cathepsin G (CTSG), a serine protease localized in NETs, entered CRC cells through the RAGE cell surface protein. The internalized CTSG cleaved 14-3-3 proteins, released BAX, and triggered apoptosis in CRC cells. Thus, our studies illuminate a previously unrecognized mechanism by which chemotherapy-induced NETs kill cancer cells., Introduction Neutrophils are the most abundant leukocytes in peripheral blood and play a vital role in host defenses against pathogens (1). Neutrophils kill pathogens, including bacteria and fungi, by phagocytosis [...]

Published
2024
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21. Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping

Author

Oniani, David, Wang, Chen, Zhao, Yiqing, Wen, Andrew, Liu, Hongfang, and Shen, Feichen

Subjects
Computer Science - Machine Learning
Abstract

Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy. Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, as cancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In this study, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic health records (EHRs) and genetic test reports for a collection of cancer patients, we developed a system leveraging a joint of phenotypic and genetic features for cancer patient subgrouping. The workflow is roughly divided into three parts: feature preprocessing, cancer patient classification, and cancer patient clustering based. In feature preprocessing step, we performed filtering, retaining the most relevant features. In cancer patient classification, we utilized joint categorical features to build a patient-feature matrix and applied nine different machine learning models, Random Forests (RF), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), Gradient Boosting (GB), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN), for classification purposes. Finally, in the cancer patient clustering step, we leveraged joint embeddings features and patient-feature associations to build an undirected feature graph and then trained the cancer feature node embeddings., Comment: arXiv admin note: text overlap with arXiv:2101.05866

Published
2021

22. Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features

Author

Oniani, David, Wang, Chen, Zhao, Yiqing, Wen, Andrew, Liu, Hongfang, and Shen, Feichen

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Cancer is responsible for millions of deaths worldwide every year. Although significant progress hasbeen achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy.Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, ascancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In thisstudy, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic healthrecords (EHRs) and genetic test reports for a collection of cancer patients, we evaluated variousgraph neural networks (GNNs) leveraging a joint of phenotypic and genetic features for cancer typeclassification. Models were applied and fine-tuned on the Mayo Clinic cancer disease dataset. Theassessment was done through the reported accuracy, precision, recall, and F1 values as well as throughF1 scores based on the disease class. Per our evaluation results, GNNs on average outperformed thebaseline models with mean statistics always being higher that those of the baseline models (0.849 vs0.772 for accuracy, 0.858 vs 0.794 for precision, 0.843 vs 0.759 for recall, and 0.843 vs 0.855 for F1score). Among GNNs, ChebNet, GraphSAGE, and TAGCN showed the best performance, while GATshowed the worst. We applied and compared eight GNN models including AGNN, ChebNet, GAT,GCN, GIN, GraphSAGE, SGC, and TAGCN on the Mayo Clinic cancer disease dataset and assessedtheir performance as well as compared them with each other and with more conventional machinelearning models such as decision tree, gradient boosting, multi-layer perceptron, naive bayes, andrandom forest which we used as the baselines.

Published
2021

23. Clinical Concept Extraction: a Methodology Review

Author

Fu, Sunyang, Chen, David, He, Huan, Liu, Sijia, Moon, Sungrim, Peterson, Kevin J, Shen, Feichen, Wang, Liwei, Wang, Yanshan, Wen, Andrew, Zhao, Yiqing, Sohn, Sunghwan, and Liu, Hongfang

Subjects
Computer Science - Information Retrieval, Computer Science - Computation and Language
Abstract

Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. Methods Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. Results A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

Published
2019
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29. Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records

Author

Wang, Yanshan, Zhao, Yiqing, Therneau, Terry M., Atkinson, Elizabeth J., Tafti, Ahmad P., Zhang, Nan, Amin, Shreyasee, Limper, Andrew H., and Liu, Hongfang

Subjects
Statistics - Applications, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts with EHR data retrieved from the Rochester Epidemiology Project (REP), for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying latent disease clusters through the visualization of disease representations learned by two approaches. We also tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters by alleviating the impact of age and sex, and that LDA could stratify patients into more differentiable subgroups than PDM in terms of p-values. However, the subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.

Published
2019

31. Returning integrated genomic risk and clinical recommendations: The eMERGE study

Author

Gordon, Adam, Sobowale, Agboade, Allworth, Aimee, Patel, Akshar, DiVietro, Alanna, Strong, Alanna, Sherafati, Alborz, Sherfati, Alborz, Bick, Alex, Miller, Alexandra, Chandel, Alka, Rosenthal, Alyssa, Khera, Amit, Kontorovich, Amy, Beck, Andrew, Beck, Andy, Espinoza, Angelica, Lewis, Anna, Prince, Anya, Khan, Atlas, Iverson, Ayuko, Khales, Bahram Namjou, Benoit, Barbara, Hernan, Becca, Kallman, Ben, Kerman, Ben, Shoemaker, Ben, Satterfield, Benjamin, Devine, Beth, Etheridge, Bethany, Goff, Blake, Freimuth, Bob, Grundmeier, Bob, Collier, Brenae, Mutai, Brenda, Harnett, Brett, Chang, Brian, Piening, Brian, Davis, Brittney, Korf, Bruce, Patterson, Candace, Demetriou, Carmen, Ta, Casey, Hammack, Catherine, Nelson, Catrina, Gascoigne, Caytie, Dorn, Chad, Moretz, Chad, Kachulis, Chris, Hoell, Christie, Cowles, Christine, Lange, Christoph, Weng, Chunhua, Prows, Cindy, Brokamp, Cole, Liu, Cong, Scherr, Courtney, Gonzalez, Crystal, Ramirez, Cynthia, Shimbo, Daichi, Roden, Dan, Schaid, Daniel, Kaufman, Dave, Crosslin, David, Kochan, David, Veenstra, David, Singh, Davinder, Karavite, Dean, Abrams, Debbie, Absher, Devin, Edwards, Digna Velez, Haverfield, Eden, Morales, Eduardo, Esplin, Edward, Malolepsza, Edyta, Alipour, Ehsan, Kenny, Eimear, Rosenthal, Elisabeth, Duvall, Eliza, McNally, Elizabeth, Bhoj, Elizabeth, Cohn, Elizabeth, Hibler, Elizabeth, Karlson, Elizabeth, Clayton, Ellen, Chesnut, Emily, DeFranco, Emily, Gallagher, Emily, Soper, Emily, Perez, Emma, Cash, Erin, Berner, Eta, Wang, Fei, Wehbe, Firas, Ricci, Francisco, Mentch, Frank, Shaibi, Gabriel, Jarvik, Gail, Hahn, George, Hripcsak, George, Wiesner, Georgia, Belbin, Gillian, Davogustto, Gio, Nadkarni, Girish, Qiu, Haijun, Hakonarson, Hakon, Bangash, Hana, Beasley, Hannah, Liu, Hao, Aungst, Heide, Tiwari, Hemant, Duckham, Hillary, Thomas, Hope, Kullo, Iftikhar, Holm, Ingrid, Allen, Isabelle, Ionita-Laza, Iuliana, Hellwege, Jacklyn, Petrzelka, Jacob, Odgis, Jacqueline, Narula, Jahnavi, Petrzelka, Jake, Patel, Jalpa, Cimino, James, Meigs, James, Snyder, James, Olson, Janet, Zahner, Janet, Pennington, Jeff, Pacheco, Jen, Pacheco, Jennifer Allen, Morse, Jennifer, Corsmo, Jeremy, Thayer, Jeritt, Cimino, Jim, Chen, Jingheng, Fournier, Jocelyn, Jackson, Jodell, Glessner, Joe, Pacyna, Joel, Smith, Johanna, Connolly, John, Lynch, John, Shelley, John, Mosley, Jonathan, Nestor, Jordan, Smoller, Jordan, Alsip, Jorge, Kannry, Joseph, Sutton, Joseph, Peterson, Josh, Smith, Joshua, Galasso, Julia, Smith, Julia, Wynn, Julia, Gundelach, Justin, Starren, Justin, Choi, Karmel, Mittendorf, Kate, Anderson, Katherine, Bonini, Katherine, Leppig, Kathleen, Muenzen, Kathleen, Larkin, Katie, Stuttgen, Kelsey, Wiley, Ken, Nguyen, Kenny, Dufendach, Kevin, Atkins, Kiley, Sawicki, Konrad, Norland, Kristjan, Kiryluk, Krzysztof, Beskow, Laura, Rasmussen-Torvik, Laura, Kottyan, Leah, Hsu, Li, Tian, Lifeng, Mahanta, Lisa, Martin, Lisa, Wang, Lisa, Gomez, Lizbeth, Thompson, Lorenzo, Orlando, Lori, Richter, Lucas, Rasmussen, Luke, Petukhova, Lynn, Seabolt, Lynn, O’Brien, Madison, Harden, Maegan, Fullerton, Malia, Harr, Margaret, Beasley, Mark, Guindo, Marta, Horike, Martha, Horike-Pyne, Martha, Abdalla, Marwah, Hamed, Marwan, Terry, Mary Beth, Maradik, Mary, Wyatt, Matt, Davis, Matthew, Lebo, Matthew, Smith, Maureen, Rosario, Maya del, Sabatello, Maya, Behr, Meckenzie, Roy-Puckelwartz, Meg, Habrat, Mel, Myers, Melanie, Yetisgen, Meliha, Iris, Merve, DaSilva, Michael, Preuss, Michael, McGowan, Michelle, Shi, Mingjian, Perera, Minoli, Thomas, Minta, Elkind, Mitch, Abbass, Mohammad, Saadatagah, Mohammad, Hess, Molly, Maradik, Molly, Vaitinadin, Nataraja “RJ”, Vaitinadin, Nataraja, Muthu, Naveen, Netherly, Neil, Lennon, Niall, Shang, Ning, Limdi, Nita, Forrest, Noah, Romero, Noheli, Robinson, Nora, Abul-Husn, Noura, Elsekaily, Omar, Dikilitas, Ozan, Kovatch, Patricia, Davis, Patrick, Appelbaum, Paul, Francaviglia, Paul, O’Reilly, Paul, Chandler, Paulette, Caraballo, Pedro, Tarczy-Hornoch, Peter, Shum, Pierre, Marathe, Priya, Murali, Priyanka, Feng, Qiping, Wells, Quinn, Atchley, Rachel, Narla, Radhika, Barton, Rene, Sterling, Rene, Chisholm, Rex, Green, Richard, Sharp, Richard, Peters, Riki, Kukafka, Rita, Rowley, Robb, Freimuth, Robert, Green, Robert, Winter, Robert, Mueller, Roger, Loos, Ruth, Irvin, Ryan, Suckiel, Sabrina, Hussain, Sajjad, Sharba, Samer, Aronson, Sandy, Jones, Sarah, Knerr, Sarah, Nigbur, Scott, Weiss, Scott, Mooney, Sean, Terek, Shannon, Aufox, Sharon, Nirenberg, Sharon, Murphy, Shawn, O’Byrne, Sheila, Wang (Sam) Choi, Shing, Aguilar, Sienna, Bland, S.T., Rodrigues, Stefanie, Ledbetter, Stephanie, Rutledge, Stephanie, Booth, Stuart James, Xian, Su, Trinidad, Susan Brown, Bakken, Suzanne, Schmidlen, Tara, Rakhra-Burris, Tejinder, Manolio, Teri, Mersha, Tesfaye, Walunas, Theresa, Chandereng, Thevaa, May, Thomas, Ge, Tian, Edwards, Todd, Kaszemacher, Tom, Hernandez, Valentina, Willis, Valerie, Desai, Vemi, Desai, Vimi, Lorenzi, Virginia, Gainer, Vivian, Wei, Wei-Qi, Chung, Wendy, Su, Wu-Chen, Chang, Xiao, Zhao, Yiqing, Luo, Yuan, Shen, Yufeng, Linder, Jodell E., Bland, Sarah T., Caraballo, Pedro J., Chisholm, Rex L., Clayton, Ellen Wright, Crosslin, David R., Esplin, Edward D., Forman, Sophie, Freimuth, Robert R., Gordon, Adam S., Harden, Maegan V., Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth W., Labrecque, Sofia, Lennon, Niall J., Limdi, Nita A., Mittendorf, Kathleen F., Murphy, Shawn N., Prows, Cynthia A., Rasmussen, Luke V., Sawicki, Konrad Teodor, Velez Edwards, Digna R., Abul-Husn, Noura S., Below, Jennifer E., Berner, Eta S., Booth, James, Chung, Wendy K., Cimino, James J., Fullerton, Stephanie M., Guiducci, Candace, Habrat, Melissa L., Hain, Heather, Hoell, Christin, Irvin, Marguerite R., Kachulis, Christopher, Kenny, Eimear E., Kullo, Iftikhar J., Manolio, Teri A., McNally, Elizabeth M., Mooney, Sean D., Namjou, Bahram, Perez, Emma F., Puckelwartz, Megan J., Roden, Dan M., Rosenthal, Elisabeth A., Saadatagah, Seyedmohammad, Schaid, Dan J., Schultz, Baergen, Shaibi, Gabriel Q., Sharp, Richard R., Shirts, Brian, Smith, Maureen E., Smoller, Jordan W., Suckiel, Sabrina A., Tiwari, Hemant K., Trinidad, Susan B., Wells, Quinn S., Wiesner, Georgia L., and Peterson, Josh F.

Published
2023
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50. Transforming Urban Landscapes: Reuse of Heritage Sites through Multi-Value Interpretations in Xi'an, China.

Author

Zhao, Yiqing, Jin, Keyu, Zhang, Dingqing, Wang, Li, Li, Ji, and Dai, Tianchen

Subjects
HISTORIC sites, URBAN growth, CULTURAL values, ENVIRONMENTAL auditing, SOCIOECONOMIC factors
Abstract

As a catalyst for urban landscape transformation, the reuse of heritage sites plays a pivotal role in shaping contemporary urban spaces and influencing socio-economic development. However, this phenomenon is not without controversy, particularly regarding the quality of preservation and the potential risks associated with over-commodification. Consequently, there has been an increasing research focus on reconciling these conflicting aspects. This study employs a multi-value interpretation framework to delve into the reuse of heritage sites in the urban area of Xi'an within the broader context of China's urbanization. Utilizing a comparative case study method, this research specifically examines the burgeoning phenomenon of heritage parks. This exploration sheds light on how the reuse of heritage intertwines with urbanization processes, by taking into account environmental, economic, social, and cultural values. The findings suggest that a multi-value interpretation framework is crucial to address the limitations of the current heritagization. Heritage sites in Xi'an have been integrated into the urban transformation process through heritage conservation and the production of new heritage spaces. However, the evolving multi-value evaluation of these heritage sites continues to influence the city's interpretation of its significant and fading past in planning. This study highlights the dynamic nature of heritage sites in the urban context and underscores the need for nuanced approaches to balance heritage preservation with the evolving demands of contemporary urban development. [ABSTRACT FROM AUTHOR]

Published
2024
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