Clinical performance pilot using cognitive computing for clinical trial matching at Mayo Clinic.

Helgeson, J.; Rammage, Melissa; Urman, Alexandra; Roebuck, M. Christopher; Coverdill, Sadie; Pomerleau, K.; Dankwa‐Mullan, Irene; Liu, Li-Ing; Sweetman, Robert; Chau, Quincy; Williamson, Mike; Vinegra, Michael; Haddad, Tufia C.; Goetz, Matthew P.

doi:10.1200/jco.2018.36.15_suppl.e18598

Cited by 27 publications

(13 citation statements)

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“…CTM performance in this cohort exceeded that of a previous study of CTM undertaken at Mayo Clinic in the United States in which accuracy was reported as 87.6% for 4 breast and 74.9% for 3 lung cancer trials. 12 In the Mayo study, CTM used NLP to process unstructured electronic health record (EHR) documents to ingest patient data, whereas in this study, clinical data were entered into CTM directly. Both manual entry and NLP processes can introduce errors.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

et al. 2020

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Objective The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital. Methods A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I–III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility. Results The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53–100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243–4132). Median time for the system to run a query and return results was 15.5 s (range 7.2–37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen’s kappa 0.70–1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%. Discussion and Conclusion The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.

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Section: Discussionmentioning

confidence: 99%

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

et al. 2020

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“…Instead of making regular trips to the hospital to participate in clinical trials, patients will receive remote monitor-ing, treatment, and life guidance. This AI-based clinical trial matching system has been successfully tested at the Mayo Clinic [50]. AI will provide more benefits to the subjects and improve their compliance.…”

Section: Participate In the Research Data Of Mg Rehabilitationmentioning

confidence: 99%

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis

Zhang

Dong

et al. 2021

BioMed Research International

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Myasthenia gravis (MG) is a chronic autoimmune disease of the nervous system, which is still incurable. In recent years, with the progress of immunosuppressive and supportive treatment, the therapeutic effect of MG in the acute stage is satisfactory, and the mortality rate has been greatly reduced. However, there is still no consensus on how to conduct long-term management of stable MG, such as guiding patients to identify relapses, practice exercise, return to work and school, etc. In the international consensus guidance for management of myasthenia gravis published by the Myasthenia Gravis Foundation of America (MGFA) in 2020, for the first time, “the role of physical training/exercise in MG” was identified as the topic of discussion. Finally, due to a lack of high-quality evidence on physical training/exercise in patients with MG, the topic was excluded after the literature review. Therefore, this paper reviewed the current status of MG rehabilitation research and the difficulties faced by stable MG patients in self-management. It is suggested that we should take advantage of artificial intelligence (AI) and leverage it to develop the data-driven decision support platforms for MG management which can be used for adverse event monitoring, disease education, chronic management, and a wide variety of data collection and analysis.

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“…For example, IBM’s Watson for Clinical Trial Matching (CTM) is a software platform that analyzes data from a patient’s electronic medical record against protocol eligibility criteria. In a pilot study conducted at the Mayo Clinic, implementation of the Watson for CTM system was associated with an increase in clinical trial enrollment and reduced screening times [ 114 ]. Thus, implementing automated clinical trial matching algorithms in the ambulatory oncology setting may supplement clinician review and increase clinical trial participation.…”

Section: Real-world Challenges In Implementing a Pdac Precision Onmentioning

confidence: 99%

Building towards Precision Oncology for Pancreatic Cancer: Real-World Challenges and Opportunities

Wang

Lakoma

Zogopoulos

2020

Genes

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The advent of next-generation sequencing (NGS) has provided unprecedented insight into the molecular complexity of pancreatic ductal adenocarcinoma (PDAC). This has led to the emergence of biomarker-driven treatment paradigms that challenge empiric treatment approaches. However, the growth of sequencing technologies is outpacing the development of the infrastructure required to implement precision oncology as routine clinical practice. Addressing these logistical barriers is imperative to maximize the clinical impact of molecular profiling initiatives. In this review, we examine the evolution of precision oncology in PDAC, spanning from germline testing for cancer susceptibility genes to multi-omic tumor profiling. Furthermore, we highlight real-world challenges to delivering precision oncology for PDAC, and propose strategies to improve the generation, interpretation, and clinical translation of molecular profiling data.

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Clinical performance pilot using cognitive computing for clinical trial matching at Mayo Clinic.

Cited by 27 publications

References 0 publications

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis

Building towards Precision Oncology for Pancreatic Cancer: Real-World Challenges and Opportunities

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