Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer

Meng, Weilin; Ou, Wanmei; Chandwani, Sheenu; Chen, Xin; Black, Wynona; Cai, Zhaohui

doi:10.1016/j.jbi.2019.103335

Cited by 30 publications

(18 citation statements)

References 19 publications

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“…For example, Meng et al used information on lines of therapy for cancer patients from the large insurance claim dataset to identify treatment pathways. These authors created an algorithm to derive a patient-level lines of therapy and aggregated this information via clustering and data visualization methods, to derive temporal phenotypes and support disease progression prediction [99]. Zhao et al applied a modified non-negative tensor-factorization approach (a technique used for discovering latent object variables in image analysis) on eHRs data in order to identify phenotypic subtypes in patients at risk for cardiovascular disease.…”

Section: New Approaches In Multimorbidity Research Associated With Pamentioning

confidence: 99%

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Majnarić

Babič

O’Sullivan

et al. 2021

JCM

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Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

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Section: New Approaches In Multimorbidity Research Associated With Pamentioning

confidence: 99%

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Majnarić

Babič

O’Sullivan

et al. 2021

JCM

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“…Meng et al developed temporal phenotyping methods to derive cancer treatment pathways within a large insurance claims dataset [28]. The authors aggregated lines of therapy information via clustering followed with data visualization to derive temporal cancer phenotypes in support of disease management and progression prediction.…”

Section: Disease Subtyping Using Novel Data Sources and Temporal Reasmentioning

confidence: 99%

“…First author NLP STND DL SRC SUB Disease Focus eMERGE OHDSI [14] Datta, S X [15] Liu, Q X X [16] Lyudovyk, O X X X Cancer X [17] Liu, C X X X [18] Hong, N X [19] Shang, N X X X [20] Hripcsak, G X X X [21] Ostropolets, A X X X [22] Reps, J X X X [23] Swerdel, J X X [24] Warner, J X Cancer X [25] Shen, F X X X X [26] Trace, JM X Parkinson's [27] Mate, S X [28] Meng, W Cancer [29] Zhao, J X X Cardiovascular X [30] Chen, X X X Rare disease [31] Xu, Z X X X Acute kidney injury [32] Zhang, L X X [33] Chen, P X TOTAL 6 12 6 6 3 5 6…”

Section: Referencementioning

confidence: 99%

Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability

Weng

Shah

Hripcsak

2020

Journal of Biomedical Informatics

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Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre-including this research content-immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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“…Previous studies suggest that lncRNAs have an important role in the regulation of autophagy via the mediation of autophagy-related gene (ATG) expression ( 8 , 9 ). Increasing evidence indicates that autophagy and lncRNAs play a key role in tumorigenesis and cancer cell survival, but whether lncRNAs modulate autophagy in HNSCC remains unknown, and the molecular mechanisms involved in autophagy-related lncRNA in HNSCC have not been elucidated ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

et al. 2021

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Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis. Considerable evidence indicates that autophagy and non-coding RNA play essential roles in the biological processes involved in cancers, but associations between autophagy-related long non-coding RNAs (lncRNAs) and HNSCC remain unclear. In the present study, HNSCC RNA sequences and autophagy-related gene data were extracted from The Cancer Genome Atlas database and the Human Autophagy Database. A total of 1,153 autophagy-related lncRNAs were selected via calculating Pearson’s correlation coefficient. Three prognosis-related autophagy lncRNAs were identified via univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis. We also constructed a prognostic model based on these autophagy-related lncRNAs and evaluated its ability to accurately and independently predict the prognosis of HNSCC patients. The area under the curve (AUC) was 0.864 (3-year) and 0.836 (5-year), and our model can independently predict the prognosis of HNSCC. The prognostic value of the three autophagy lncRNAs was confirmed via analysis of samples from five databases. To further identify the functions of the three lncRNAs, a co-expression network was constructed and pathway analysis was performed. In that analysis the lncRNAs were correlated with 189 related genes and 20 autophagy-related genes, and these lncRNAs mainly involved homologous recombination, the Fanconi anemia pathway, the autophagy-related pathway, and immune-related pathways. In addition, we validated the expression levels of three lncRNAs and autophagy markers (ATG12, BECN1, and MAP1LC3B) based on TIMER, Oncomine, and HPA database analysis. Our results indicated that TTTY15 was increased in HPV positive and HPV negative HNSCC patients, and three autophagy markers were up-regulated in all HNSCCC patients. Lastly, association between three lncRNAs and autophagy markers was performed, and our results showed that TTTY15 and MIF-AS1 were associated with autophagy markers. Collectively, these results suggested that three autophagy-related lncRNAs have prognostic value in HNSCC patients.

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Temporal phenotyping by mining healthcare data to derive lines of therapy for cancer

Cited by 30 publications

References 19 publications

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Deep phenotyping: Embracing complexity and temporality—Towards scalability, portability, and interoperability

Identification of Three Autophagy-Related Long Non-Coding RNAs as a Novel Head and Neck Squamous Cell Carcinoma Prognostic Signature

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