Browsing by Author "Carmody, Leigh C."

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    Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery
    (Springer Nature, 2019) Zhang, Xingmin Aaron; Yates, Amy; Vasilevsky, Nicole; Gourdine, J. P.; Callahan, Tiffany J.; Carmody, Leigh C.; Danis, Daniel; Joachimiak, Marcin P.; Ravanmehr, Vida; Pfaff, Emily R.; Champion, James; Robasky, Kimberly; Xu, Hao; Fecho, Karamarie; Walton, Nephi A.; Zhu, Richard L.; Ramsdill, Justin; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; McDonald, Clement J.; Vreeman, Daniel J.; Peden, David B.; Bennett, Tellen D.; Feinstein, James A.; Martin, Blake; Stefanski, Adrianne L.; Hunter, Lawrence E.; Chute, Christopher G.; Robinson, Peter N.; Medicine, School of Medicine
    Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
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    The Human Phenotype Ontology in 2024: phenotypes around the world
    (Oxford University Press, 2024) Gargano, Michael A.; Matentzoglu, Nicolas; Coleman, Ben; Addo-Lartey, Eunice B.; Anagnostopoulos, Anna V.; Anderton, Joel; Avillach, Paul; Bagley, Anita M.; Bakštein, Eduard; Balhoff, James P.; Baynam, Gareth; Bello, Susan M.; Berk, Michael; Bertram, Holli; Bishop, Somer; Blau, Hannah; Bodenstein, David F.; Botas, Pablo; Boztug, Kaan; Čady, Jolana; Callahan, Tiffany J.; Cameron, Rhiannon; Carbon, Seth J.; Castellanos, Francisco; Caufield, J. Harry; Chan, Lauren E.; Chute, Christopher G.; Cruz-Rojo, Jaime; Dahan-Oliel, Noémi; Davids, Jon R.; de Dieuleveult, Maud; de Souza, Vinicius; de Vries, Bert B. A.; de Vries, Esther; DePaulo, J. Raymond; Derfalvi, Beata; Dhombres, Ferdinand; Diaz-Byrd, Claudia; Dingemans, Alexander J. M.; Donadille, Bruno; Duyzend, Michael; Elfeky, Reem; Essaid, Shahim; Fabrizzi, Carolina; Fico, Giovanna; Firth, Helen V.; Freudenberg-Hua, Yun; Fullerton, Janice M.; Gabriel, Davera L.; Gilmour, Kimberly; Giordano, Jessica; Goes, Fernando S.; Gore Moses, Rachel; Green, Ian; Griese, Matthias; Groza, Tudor; Gu, Weihong; Guthrie, Julia; Gyori, Benjamin; Hamosh, Ada; Hanauer, Marc; Hanušová, Kateřina; He, Yongqun Oliver; Hegde, Harshad; Helbig, Ingo; Holasová, Kateřina; Hoyt, Charles Tapley; Huang, Shangzhi; Hurwitz, Eric; Jacobsen, Julius O. B.; Jiang, Xiaofeng; Joseph, Lisa; Keramatian, Kamyar; King, Bryan; Knoflach, Katrin; Koolen, David A.; Kraus, Megan L.; Kroll, Carlo; Kusters, Maaike; Ladewig, Markus S.; Lagorce, David; Lai, Meng-Chuan; Lapunzina, Pablo; Laraway, Bryan; Lewis-Smith, David; Li, Xiarong; Lucano, Caterina; Majd, Marzieh; Marazita, Mary L.; Martinez-Glez, Victor; McHenry, Toby H.; McInnis, Melvin G.; McMurry, Julie A.; Mihulová, Michaela; Millett, Caitlin E.; Mitchell, Philip B.; Moslerová, Veronika; Narutomi, Kenji; Nematollahi, Shahrzad; Nevado, Julian; Nierenberg, Andrew A.; Novák Čajbiková, Nikola; Nurnberger, John I., Jr.; Ogishima, Soichi; Olson, Daniel; Ortiz, Abigail; Pachajoa, Harry; Perez de Nanclares, Guiomar; Peters, Amy; Putman, Tim; Rapp, Christina K.; Rath, Ana; Reese, Justin; Rekerle, Lauren; Roberts, Angharad M.; Roy, Suzy; Sanders, Stephan J.; Schuetz, Catharina; Schulte, Eva C.; Schulze, Thomas G.; Schwarz, Martin; Scott, Katie; Seelow, Dominik; Seitz, Berthold; Shen, Yiping; Similuk, Morgan N.; Simon, Eric S.; Singh, Balwinder; Smedley, Damian; Smith, Cynthia L.; Smolinsky, Jake T.; Sperry, Sarah; Stafford, Elizabeth; Stefancsik, Ray; Steinhaus, Robin; Strawbridge, Rebecca; Sundaramurthi, Jagadish Chandrabose; Talapova, Polina; Tenorio Castano, Jair A.; Tesner, Pavel; Thomas, Rhys H.; Thurm, Audrey; Turnovec, Marek; van Gijn, Marielle E.; Vasilevsky, Nicole A.; Vlčková, Markéta; Walden, Anita; Wang, Kai; Wapner, Ron; Ware, James S.; Wiafe, Addo A.; Wiafe, Samuel A.; Wiggins, Lisa D.; Williams, Andrew E.; Wu, Chen; Wyrwoll, Margot J.; Xiong, Hui; Yalin, Nefize; Yamamoto, Yasunori; Yatham, Lakshmi N.; Yocum, Anastasia K.; Young, Allan H.; Yüksel, Zafer; Zandi, Peter P.; Zankl, Andreas; Zarante, Ignacio; Zvolský, Miroslav; Toro, Sabrina; Carmody, Leigh C.; Harris, Nomi L.; Munoz-Torres, Monica C.; Danis, Daniel; Mungall, Christopher J.; Köhler, Sebastian; Haendel, Melissa A.; Robinson, Peter N.; Psychiatry, School of Medicine
    The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.