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Gliozzo J, Mesiti M, Notaro M, Petrini A, Patak A, Puertas-Gallardo A, Paccanaro A, Valentini G, Casiraghi E. Heterogeneous data integration methods for patient similarity networks. Brief Bioinform 2022; 23:6604996. [PMID: 35679533 PMCID: PMC9294435 DOI: 10.1093/bib/bbac207] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/14/2022] [Accepted: 05/04/2022] [Indexed: 12/29/2022] Open
Abstract
Patient similarity networks (PSNs), where patients are represented as nodes and their similarities as weighted edges, are being increasingly used in clinical research. These networks provide an insightful summary of the relationships among patients and can be exploited by inductive or transductive learning algorithms for the prediction of patient outcome, phenotype and disease risk. PSNs can also be easily visualized, thus offering a natural way to inspect complex heterogeneous patient data and providing some level of explainability of the predictions obtained by machine learning algorithms. The advent of high-throughput technologies, enabling us to acquire high-dimensional views of the same patients (e.g. omics data, laboratory data, imaging data), calls for the development of data fusion techniques for PSNs in order to leverage this rich heterogeneous information. In this article, we review existing methods for integrating multiple biomedical data views to construct PSNs, together with the different patient similarity measures that have been proposed. We also review methods that have appeared in the machine learning literature but have not yet been applied to PSNs, thus providing a resource to navigate the vast machine learning literature existing on this topic. In particular, we focus on methods that could be used to integrate very heterogeneous datasets, including multi-omics data as well as data derived from clinical information and medical imaging.
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Affiliation(s)
- Jessica Gliozzo
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,European Commission, Joint Research Centre (JRC), Ispra (VA), Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Marco Mesiti
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Marco Notaro
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Alessandro Petrini
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
| | - Alex Patak
- European Commission, Joint Research Centre (JRC), Ispra (VA), Italy
| | | | - Alberto Paccanaro
- Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX UK.,School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro Brazil
| | - Giorgio Valentini
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy.,DSRC UNIMI, Data Science Research Center, Milano, 20135, Italy.,ELLIS, European Laboratory for Learning and Intelligent Systems, Berlin, Germany
| | - Elena Casiraghi
- AnacletoLab - Computer Science Department, Universitá degli Studi di Milano, Via Celoria 18, 20135, Milan, Italy.,CINI, Infolife National Laboratory, Roma, Italy
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Kumar S, Oh I, Schindler S, Lai AM, Payne PRO, Gupta A. Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review. JAMIA Open 2021; 4:ooab052. [PMID: 34350389 PMCID: PMC8327375 DOI: 10.1093/jamiaopen/ooab052] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/21/2021] [Accepted: 06/30/2021] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.
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Affiliation(s)
- Sayantan Kumar
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Inez Oh
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Suzanne Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri, USA
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Bhardwaj N, Cecchetti AA, Murughiyan U, Neitch S. Analysis of Benzodiazepine Prescription Practices in Elderly Appalachians with Dementia via the Appalachian Informatics Platform: Longitudinal Study. JMIR Med Inform 2020; 8:e18389. [PMID: 32749226 PMCID: PMC7435704 DOI: 10.2196/18389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/27/2020] [Accepted: 06/15/2020] [Indexed: 01/22/2023] Open
Abstract
Background Caring for the growing dementia population with complex health care needs in West Virginia has been challenging due to its large, sizably rural-dwelling geriatric population and limited resource availability. Objective This paper aims to illustrate the application of an informatics platform to drive dementia research and quality care through a preliminary study of benzodiazepine (BZD) prescription patterns and its effects on health care use by geriatric patients. Methods The Maier Institute Data Mart, which contains clinical and billing data on patients aged 65 years and older (N=98,970) seen within our clinics and hospital, was created. Relevant variables were analyzed to identify BZD prescription patterns and calculate related charges and emergency department (ED) use. Results Nearly one-third (4346/13,910, 31.24%) of patients with dementia received at least one BZD prescription, 20% more than those without dementia. More women than men received at least one BZD prescription. On average, patients with dementia and at least one BZD prescription sustained higher charges and visited the ED more often than those without one. Conclusions The Appalachian Informatics Platform has the potential to enhance dementia care and research through a deeper understanding of dementia, data enrichment, risk identification, and care gap analysis.
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Affiliation(s)
- Niharika Bhardwaj
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Alfred A Cecchetti
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Usha Murughiyan
- Department of Clinical and Translational Science, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Shirley Neitch
- Department of Internal Medicine, Joan C Edwards School of Medicine, Marshall University, Huntington, WV, United States
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Zhang H, Zhu F, Dodge HH, Higgins GA, Omenn GS, Guan Y. A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease. Gigascience 2018; 7:5052206. [PMID: 30010762 PMCID: PMC6054197 DOI: 10.1093/gigascience/giy085] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 04/15/2018] [Accepted: 06/28/2018] [Indexed: 01/17/2023] Open
Abstract
Motivation Heterogeneous diseases such as Alzheimer's disease (AD) manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer cost benefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. Results We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated AD patients from control subjects with an area under the receiver operating curve of 0.920. Without knowing longitudinal information about subjects, the model predicted patients who are vulnerable to conversion from mild-cognitive impairment to AD through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.
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Affiliation(s)
- Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
| | - Fan Zhu
- Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
- Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Shuitu Hi-tech Industrial Park, Shuitu Town, Beibei District, Chongqing, China 400714
| | - Hiroko H Dodge
- Michigan Alzheimer's Disease Center, University of Michigan, 2101 Commonwealth Blvd, Ann Arbor, MI, USA 48105
- Department of Neurology, University of Michigan, 1500 E. Medical Center Dr., 1914 Taubman Center SPC 5316, Ann Arbor, MI, USA 48109
- Layton Aging and Alzheimer's Disease Center and Department of Neurology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, L226, Portland, OR, USA 97239
| | - Gerald A Higgins
- Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
- Department of Internal Medicine, University of Michigan, 3110 Taubman Center, SPC 5368, 1500 East Medical Center Drive, Ann Arbor, MI, USA 48109
- Department of Human Genetics, University of Michigan, 4909 Buhl Building, 1241 E. Catherine St., Ann Arbor, MI, USA 48109
- School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, USA 48109
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, 2017G Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI, USA 48109
- Department of Internal Medicine, University of Michigan, 3110 Taubman Center, SPC 5368, 1500 East Medical Center Drive, Ann Arbor, MI, USA 48109
- Department of Electronic Engineering and Computer Science, Bob and Betty Beyster Building, 2260 Hayward Street, University of Michigan, Ann Arbor, MI, USA 48109
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