Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis

Norgeot, Beau; Glicksberg, Benjamin S.; Trupin, Laura; Lituiev, Dmytro; Gianfrancesco, Milena; Oskotsky, Boris; Schmajuk, Gabriela; Yazdany, Jinoos; Butte, Atul J.

doi:10.1001/jamanetworkopen.2019.0606

Cited by 161 publications

(124 citation statements)

References 30 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…Other notable work in the field takes a radically different approach. A team at University of California San Francisco (UCSF) reports on a project with similar predictive goals for patients with one particular condition, rheumatoid arthritis (RA) [7]. In this study, variables were selected based on known clinical significance, though not necessarily known to have predictive value.…”

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

View full text Add to dashboard Cite

Objectives: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports—and sometimes initiates—and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension. Methods: Beyond personal awareness of a range of work commensurate with the author’s own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns (“artificial intelligence”, “data models”, “analytics”, etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered. Results: The substantive sections of the paper—Artificial Intelligence, Machine Learning, and “Big Data” Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence—provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer’s interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work. Conclusions: CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

show abstract

Section: Resultsmentioning

confidence: 99%

Review of Clinical Research Informatics

Solomonides

2020

Yearb Med Inform

View full text Add to dashboard Cite

show abstract

“…54 At least one largely successful test of a prototype RLS has been reported. 55 In addition, the metrics used to evaluate an RLS and show that its continuous updates are leading to improvements could be based on a number of important healthcare goals. For example, would OS, reduced side-effects, patient satisfaction, reduced costs, or some combination of all of these, be used to guide the system?…”

Section: Real-world Data Learning Systemsmentioning

confidence: 99%

Strategies for Testing Intervention Matching Schemes in Cancer

Schork

Goetz

Lowey

et al. 2020

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as “intervention matching schemes” (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, such as basket, umbrella, and adaptive trials. In addition, IMS are at the heart of proposed “rapid learning systems” in hospitals, and implicit in cell replacement strategies, such as cytotoxic T‐cell therapies targeting patient‐specific neo‐antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.

show abstract

“…Forecasting of health relevant events by using artificial intelligence has the ability to intervene early with lifestyle changes or more gentle medical approaches [10].…”

Section: Doi: 101159/000506672mentioning

confidence: 99%