Cancer Informatics in 2019: Deep Learning Takes Center Stage

Warner, Jeremy L.; Patt, Debra A.

doi:10.1055/s-0040-1701993

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Focusing on integrating genomic data with EHR systems, Warner et al [65] discuss the status (as of 2016) and the challenges. Although well-established nomenclatures exist for omics data, a lack of consistent modeling standards prevents integration of omics laboratory results into EHRs.…”

Section: Resultsmentioning

confidence: 99%

A Review of AI and Data Science Support for Cancer Management

Parimbelli

Wilk

Cornet

et al. 2020

Preprint

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Introduction - Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ Data Science methods to monitor the health status and provide support to cancer patients managed at home. Objective - Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. Methods - We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient state from it and deliver coaching/behavior change interventions. Results - Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 110 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. Conclusion - Development of modern decision support system for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality of life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

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Section: Resultsmentioning

confidence: 99%

A Review of AI and Data Science Support for Cancer Management

Parimbelli

Wilk

Cornet

et al. 2020

Preprint

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“…Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. CI editors Jeremy Warner and Debra Pratt also wrote that in 2023, the selection of papers in cancer informatics intends to illuminate the current progress of research with a focus on efforts to translate research towards immediate clinical applicability [17].…”

Section: One Health and Medical Informaticsmentioning

confidence: 99%

Informatics for One Health

Hollis,

Mougin,

Soualmia

2023

Yearb Med Inform

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Objectives: To introduce the 2023 International Medical Informatics Association (IMIA) Yearbook by the editors. Methods: The editorial provides an introduction and overview to the 2023 IMIA Yearbook where the special topic is “Informatics for One Health”. The special topic, survey papers and some best papers are discussed. The section changes in the Yearbook editorial committee are also described. Results: IMIA Yearbook 2023 provides many perspectives on a relatively new topic called “One Digital Health”. The subject is vast, and includes the use of digital technologies to promote the well-being of people and animals, but also of the environment in which they evolve. Many sections produced new work in the topic including One Health and all sections included the latest themes in many specialties in medical informatics. Conclusions: The theme of “Informatics for One Health” is relatively new but the editors of the IMIA Yearbook have presented excellent and thought-provoking work for biomedical informatics in 2023.

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“…Informatics efforts have allowed oncologists to better understand rare tumors by creating larger cohorts of similar patients. Additionally, with improvements in machine learning techniques, our ability to decode the complexity of cancer has dramatically improved across different data streams, spanning genomics, diagnostic imaging, and the electronic health record [1].…”

Section: Introductionmentioning

confidence: 99%

Clinical Informatics Approaches to Facilitate Cancer Data Sharing

Aneja

Avesta

et al. 2023

Yearb Med Inform

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Objectives: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. Methods: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. Results: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. Conclusions: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

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Cancer Informatics in 2019: Deep Learning Takes Center Stage

Cited by 5 publications

References 21 publications

A Review of AI and Data Science Support for Cancer Management

A Review of AI and Data Science Support for Cancer Management

Informatics for One Health

Clinical Informatics Approaches to Facilitate Cancer Data Sharing

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