An informatics research agenda to support precision medicine: seven key areas

Tenenbaum, Jessica D.; Avillach, Paul; Benham‐Hutchins, Marge; Breitenstein, Matthew K.; Crowgey, Erin L.; Hoffman, Mark A.; Jiang, Xia; Madhavan, Subha; Mattison, John E.; Nagarajan, Radhakrishnan; Ray, Bisakha; Shin, Dmitriy; Visweswaran, Shyam; Zhao, Zhongming; Freimuth, Robert R.

doi:10.1093/jamia/ocv213

Cited by 62 publications

(54 citation statements)

References 34 publications

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“…2 Big Data analytics utilizing a variety of machine learning methodologies play a crucial role in developing analytical foundations of personalized medicine. 3 The broad introduction of electronic algorithms predicting clinical events in real time with the intent to improve patient care and decrease costs has been successfully facilitated by Big Data analytics, 4 and the recent explosion in availability of electronic health data is motivating a rapid expansion of healthcare predictive analytics applications. 5,6 One of the potentially fruitful areas for further expansion of Big Data analytics is chronic disease management.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

142

110

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Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training data sets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

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Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

142

110

View full text Add to dashboard Cite

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“…Novel and essential directions in the use of Big Data for health care have been recently redefined within the medical informatics community (Tenenbaum et al, 2016). Specifically, the wellknown conceptual approach of the "data, information, and knowledge" continuum has been reconsidered as the so-called Learning Healthcare System Cycle (LHSC), where healthcare practice and research should be part of a unique and synergic process.…”

Section: Big Data and The Learning Healthcare System Cyclementioning

confidence: 99%

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

Dagliati

Tibollo

Sacchi

et al. 2018

Front. Digit. Humanit.

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Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called "Learning Healthcare System Cycle," where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how "Big Data enabled" integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.

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“…User interface design and technological infrastructure will need to enable continued participant engagement after the point of enrolment. This feature will allow participants to be automatically updated when new research studies in need of analyzing their data are initiated (Tenenbaum et al 2016;Williams et al 2015). 2.…”

Section: Desideratamentioning

confidence: 99%

“…Digital technology is a significant part of this, as informatics can facilitate consent and monitor the use of data and samples. Furthermore, a semantically enriched dynamic consent would improve its adaptability to every new research scenario (Tenenbaum et al 2016). This paper therefore considers some of the technical challenges related to implementing a digital dynamic consent.…”

Section: Introductionmentioning

confidence: 99%

Desiderata for digital consent in genomic research

et al. 2018

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Herein, we describe the characterization of a Digital Consent (DC) System to support current ethical-legal issues associated with challenges posed by informed consent for genomic research. A potential solution to support ongoing interaction with patients and allow control over how their data and samples are being used in genomic research can be Digital Consent based. But there are other challenges that need to be addressed, such as incidental findings when analyzing the results of genomic tests (not expected).

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An informatics research agenda to support precision medicine: seven key areas

Cited by 62 publications

References 34 publications

Machine learning approaches to personalize early prediction of asthma exacerbations

Machine learning approaches to personalize early prediction of asthma exacerbations

Big Data as a Driver for Clinical Decision Support Systems: A Learning Health Systems Perspective

Desiderata for digital consent in genomic research

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