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Results in Journal Journal of Biomedical Informatics: 2,991

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Tiange Chen, Siwan Huang, Guanqiao Li, Yuan Zhang, Ye Li, Jinyi Zhu, Xuanling Shi, , , Linqi Zhang
Journal of Biomedical Informatics, Volume 118, pp 103800-103800; doi:10.1016/j.jbi.2021.103800

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Yingcheng Sun, Alex Butler, LaToya A. Stewart, Hao Liu, Chi Yuan, Christopher T. Southard, Jae Hyun Kim,
Journal of Biomedical Informatics, Volume 118, pp 103790-103790; doi:10.1016/j.jbi.2021.103790

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, Rafael Martínez-Tomás, Pilar Pozo, Félix de la Paz, Encarnación Sarriá
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103797

Abstract:
The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human–robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction.
, Bridget T. McInnes
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103784

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G. Bonaccorsi, M. Giganti, M. Nitsenko, G. Pagliarini, G. Piva,
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103780

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Rezarta Islamaj, Chih-Hsuan Wei, David Cissel, Nicholas Miliaras, Olga Printseva, Oleg Rodionov, Keiko Sekiya, Janice Ward,
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103779

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Cong Sun, , , Yin Zhang, Hongfei Lin, Jian Wang
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103799

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, , Laurent Meesseman, Jos De Roo, Martijn Vanbiervliet, Jos De Baerdemaeker, Herman Muys, , ,
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103783

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Jayanta Kumar Das, Subhadip Chakraborty,
Journal of Biomedical Informatics, Volume 118, pp 103801-103801; doi:10.1016/j.jbi.2021.103801

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, Lawrence J. Babb, Casey Overby Taylor, Luke V. Rasmussen, Robert R. Freimuth, Eric Venner, Fei Yan, Victoria Yi, Stephen J. Granite, Hana Zouk, et al.
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103795

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Edward J. Schenck, Katherine L. Hoffman, Marika Cusick, Joseph Kabariti, Evan T. Sholle, Thomas R. Campion
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103789

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Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103781

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, Thakir M. Mohsin, Dhiya Al-Jumeily, Mohamed Alloghani
Journal of Biomedical Informatics, Volume 118, pp 103766-103766; doi:10.1016/j.jbi.2021.103766

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Elizabeth Mauer, Jihui Lee, Justin Choi, Hongzhe Zhang, Katherine L. Hoffman, Imaani J. Easthausen, Mangala Rajan, Mark G. Weiner, Rainu Kaushal, Monika M. Safford, et al.
Journal of Biomedical Informatics, Volume 118, pp 103794-103794; doi:10.1016/j.jbi.2021.103794

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, Gabriele Cevenini, Paolo Barbini
Journal of Biomedical Informatics, Volume 118, pp 103793-103793; doi:10.1016/j.jbi.2021.103793

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, , , , Anand Avati, Andrew Ng, Sanjay Basu, Nigam H. Shah
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103826

Abstract:
Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was to determine whether ML models for allocating readmission-mitigating interventions have different usefulness based on their overall utility and discriminative ability. We conducted a retrospective utility analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart, including 513,495 commercially-insured inpatients (mean [SD] age 69 [19] years; 294,895 [57%] Female) over the period January 2016 through January 2017 from all 50 states with mean 90 day cost of $11,552. Utility analysis estimates the cost, in dollars, of allocating interventions for lowering readmission risk based on the reduction in the 90-day cost. Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0.76 (95% CI 0.76, 0.77); allocating interventions based on a model trained to predict cost as a proxy achieved a higher utility of $175.94 per patient, and an AUC of 0.62 (95% CI 0.61, 0.62). A hybrid model combining both intervention strategies is comparable with the best models on either metric. Estimated utility varies by intervention cost and efficacy, with each model performing the best under different intervention settings. We demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.
Ahmed H. Alkenani, , ,
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103803

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Xin Huang, , Haoze Tang, Bing Liu, Benzhe Su,
Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103796

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Journal of Biomedical Informatics, Volume 118; doi:10.1016/j.jbi.2021.103788

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Hao Liu, Yuan Chi, Alex Butler, Yingcheng Sun,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103771

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, Jing Guo, Pei Wang, Yaowei Wang, Minghao Yu, Xiang Wang, Po Yang,
Journal of Biomedical Informatics, Volume 117, pp 103736-103736; doi:10.1016/j.jbi.2021.103736

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James R. Rogers, George Hripcsak, Ying Kuen Cheung,
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103822

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David Cuadrado, David Riaño, Josep Gómez, Alejandro Rodríguez, María Bodí
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103768

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Amy Junghyun Lee, , Youngbin Shin, Jiwoo Lee, Hyo Jung Park, Young Chul Cho, Yousun Ko, Yu Sub Sung, Byung Sun Yoon
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103782

Abstract:
Major issues in imaging data management of tumor response assessment in clinical trials include high human errors in data input and unstandardized data structures, warranting a new breakthrough IT solution. Thus, we aim to develop a Clinical Data Interchange Standards Consortium (CDISC)-compliant clinical trial imaging management system (CTIMS) with automatic verification and transformation modules for implementing the CDISC Study Data Tabulation Model (SDTM) in the tumor response assessment dataset of clinical trials. In accordance with various CDISC standards guides and Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, the overall system architecture of CDISC-compliant CTIMS was designed. Modules for standard-compliant electronic case report form (eCRF) to verify data conformance and transform into SDTM data format were developed by experts in diverse fields such as medical informatics, medical, and clinical trial. External validation of the CDISC-compliant CTIMS was performed by comparing it with our previous CTIMS based on real-world data and CDISC validation rules by Pinnacle 21 Community Software. The architecture of CDISC-compliant CTIMS included the standard-compliant eCRF module of RECIST, the automatic verification module of the input data, and the SDTM transformation module from the eCRF input data to the SDTM datasets based on CDISC Define-XML. This new system was incorporated into our previous CTIMS. External validation demonstrated that all 176 human input errors occurred in the previous CTIMS filtered by a new system yielding zero error and CDISC-compliant dataset. The verified eCRF input data were automatically transformed into the SDTM dataset, which satisfied the CDISC validation rules by Pinnacle 21 Community Software. To assure data consistency and high quality of the tumor response assessment data, our new CTIMS can minimize human input error by using standard-compliant eCRF with an automatic verification module and automatically transform the datasets into CDISC SDTM format.
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103770

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Gang Yu, ZhongZhi Yu, Yemin Shi, Yingshuo Wang, Xiaoqing Liu, Zheming Li, Yonggen Zhao, Fenglei Sun, ,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103754

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, Tom Lawton, John Burden, John McDermid, Ibrahim Habli
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103762

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Journal of Biomedical Informatics, Volume 117, pp 103760-103760; doi:10.1016/j.jbi.2021.103760

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Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103767

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Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103758

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, Mari Ostendorf, Matthew Thompson, Meliha Yetisgen
Journal of Biomedical Informatics, Volume 117, pp 103761-103761; doi:10.1016/j.jbi.2021.103761

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Eric Prud'Hommeaux, Josh Collins, David Booth, Kevin J. Peterson, Harold R. Solbrig,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103755

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, Sara C. Madeira, Marta Gromicho, Mamede de Carvalho, Alexandra M. Carvalho
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103730

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Mehdi Mirzapour, Amine Abdaoui, Andon Tchechmedjiev, William Digan, Sandra Bringay,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103733

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Mike Wong, Paul Previde, Jack Cole, Brook Thomas, Nayana Laxmeshwar, Emily Mallory, Jake Lever, Dragutin Petkovic, Russ B. Altman,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103732

Abstract:
Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the later offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user’s data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive’s applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.
Thomas Ferté, , Thierry Schaeverbeke, Thomas Barnetche, Vianney Jouhet, Boris P. Hejblum
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103746

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Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103724

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, Danilo M. Eler, Rogério E. Garcia, Ronaldo C.M. Correia, Rafael M.B. Rodrigues
Journal of Biomedical Informatics, Volume 117, pp 103753-103753; doi:10.1016/j.jbi.2021.103753

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, Peng Wei, Elmer V. Bernstam, Richard D. Boyce, Trevor Cohen
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103719

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Journal of Biomedical Informatics, Volume 117; doi:10.1016/s1532-0464(21)00134-9

Journal of Biomedical Informatics, Volume 117; doi:10.1016/s1532-0464(21)00135-0

Saadia Arshad, Junaid Arshad, Muhammad Mubashir Khan, Simon Parkinson
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103815

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Haiyan Wang, Honghua Dai, , Bing Zhou, Peng Lu, Hongpo Zhang, Zongmin Wang
Journal of Biomedical Informatics; doi:10.1016/j.jbi.2021.103819

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Juan Zhao, Monika E. Grabowska, Vern Eric Kerchberger, Joshua C. Smith, H. Nur Eken, , , S. Trent Rosenbloom, Kevin B. Johnson,
Journal of Biomedical Informatics, Volume 117, pp 103748-103748; doi:10.1016/j.jbi.2021.103748

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, Dörthe Arndt, Jos De Roo, Erik Mannens
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103750

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Yan Huang, Xiaojin Li,
Journal of Biomedical Informatics, Volume 117; doi:10.1016/j.jbi.2021.103744

Abstract:
Fast temporal query on large EHR-derived data sources presents an emerging big data challenge, as this query modality is intractable using conventional strategies that have not focused on addressing Covid-19-related research needs at scale. We introduce a novel approach called Event-level Inverted Index (ELII) to optimize time trade-offs between one-time batch preprocessing and subsequent open-ended, user-specified temporal queries. An experimental temporal query engine has been implemented in a NoSQL database using our new ELII strategy. Near-real-time performance was achieved on a large Covid-19 EHR dataset, with 1.3 million unique patients and 3.76 billion records. We evaluated the performance of ELII on several types of queries: classical (non-temporal), absolute temporal, and relative temporal. Our experimental results indicate that ELII accomplished these queries in seconds, achieving average speed accelerations of 26.8 times on relative temporal query, 88.6 times on absolute temporal query, and 1037.6 times on classical query compared to a baseline approach without using ELII. Our study suggests that ELII is a promising approach supporting fast temporal query, an important mode of cohort development for Covid-19 studies.
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