The future of precision health is data‐driven decision support

Sperger, John; Freeman, Nikki L. B.; Jiang, Xiaotong; Bang, David; Marchi, Daniel; Kosorok, Michael R.

doi:10.1002/sam.11475

Cited by 6 publications

(5 citation statements)

References 36 publications

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“…First, this study serves as the first step in the precision public health paradigm for infection prevention and control. Compared with precision medicine which finds the optimal treatment for each patient, precision public health focuses on recommendations tailored to large entities such as health institutions on a broader level (Kosorok and Laber, 2018;Sperger et al, 2020). Given that our proposed methods can provide contextual intelligence of bacterial infection rates for CF programs, a natural continuum is to identify modifiable program-level risk factors (frequency of bacterial culture performed, mask use, cleaning, CF knowledge education) and provide program-specific strategies to proactively prevent infections.…”

Section: Discussionmentioning

confidence: 99%

Risk-Adjusted Incidence Modeling on Hierarchical Survival Data with Recurrent Events

Jiang¹,

Stoudemire²,

Muhlebach³

et al. 2022

Preprint

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There is a constant need for many healthcare programs to timely address problems with infection prevention and control (IP&C). For example, pathogens can be transmitted among patients with cystic fibrosis (CF) in both the inpatient and outpatient settings within the healthcare system even with the existing recommended IP&C practices, and these pathogens are often associated with negative clinical outcomes. Because of limited and delayed data sharing, CF programs need a reliable method to track infection rates. There are three complex structures in CF registry data: recurrent infections, missing data, and multilevel correlation due to repeated measures within a patient and patient-to-patient transmissions. A step-by-step analysis pipeline was proposed to develop and validate a risk-adjusted model to help healthcare programs monitor the number of recurrent events while taking into account missing data and the hierarchies of repeated measures in right-censored data. We extended the mixed-effect Andersen-Gill model (the frailty model), adjusted for important risk factors, and provided confidence intervals for the predicted number of events where the variability of the prediction was estimated from three identified sources. The coverage of the estimated confidence intervals was used to evaluate model performance. Simulation results indicated that the coverage of our method was close to the desired confidence level. To demonstrate its clinical practicality, our pipeline was applied to monitor the infection incidence rate of two key CF pathogens using a U.S. registry. Results showed that years closer to the time of interest were better at predicting future incidence rates in the CF example.

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Section: Discussionmentioning

confidence: 99%

Risk-Adjusted Incidence Modeling on Hierarchical Survival Data with Recurrent Events

Jiang¹,

Stoudemire²,

Muhlebach³

et al. 2022

Preprint

Self Cite

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“…One exception was made to this rule: everyone with a history of heart attack was assigned to diet and exercise. The result was a modest clinical improvement in weight loss of 1.4 kilograms at 18 months that was statistically significant (p = .01) (Jiang et al, 2020). While this level of improvement may be relatively modest, the simplicity of the treatment rule would make it easy to implement in clinical practice, and it could have an important impact at the population level.…”

Section: Knee Osteoarthritismentioning

confidence: 99%

“…The very future we can imagine for statistics has changed too. We have previously argued that data-driven decision support is an exciting frontier for statisticians and the field at large (Sperger et al, 2020). Data-driven decision support is fundamentally different from the tasks of prediction and attribution that characterized the world that Dr. Breiman described.…”

Section: Introductionmentioning

confidence: 99%

Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support

Freeman¹,

Sperger²,

El-Zaatari³

et al. 2021

Observational Studies

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In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics -data-driven decision support. We assert that to realize the full 1. Equal contribution, co-first authors ©2021 Freeman et al..Freeman et al.potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.

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“…The concept of personalising care to the patient is not new, but recent advances in large scale databases, better measurement technologies, and computational tools, such as deep learning, are making data-driven precision medicine realistically achievable [432]. Data-driven precision medicine however has not yet become the clinical norm, requiring more clinical validation and model development for precision medicine to become one [15,380,761,873]. The use of precision medicine framework for exercise prescription and monitoring is not new either.…”

Section: Precision Strength Trainingmentioning

confidence: 99%

“…In contrast to traditionally used net mean treatment eect in studies, one is interested in the individual responses [600] within precision exercise framework, and how measurement noise (that is random) can be disentangled from the inherent physiological response (that is reproducible) [361]. While there have been advances in statistical methods to analyse these heterogeneous responses [174,801], the future studies need to consider jointly the research protocol design, measurement technologies, and modelling tools for disentanglement of sources and causal relationships [761,776,836].…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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The future of precision health is data‐driven decision support

Cited by 6 publications

References 36 publications

Risk-Adjusted Incidence Modeling on Hierarchical Survival Data with Recurrent Events

Risk-Adjusted Incidence Modeling on Hierarchical Survival Data with Recurrent Events

Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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