Diagnosis

Martelletti, Paolo

doi:10.1007/978-3-030-97359-9_3

Cited by 1 publication

(1 citation statement)

References 567 publications

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“…The goal of this augmentation is to gain insights into the effectiveness and efficiency of clinical treatments at a population level outside of results derived in a high-resource clinical trial setting studying a stringently defined disease population. RWE is increasingly employed not only to understand real-world outcomes, but also to power studies sufficiently to enable subgroup analytics, comparative effectiveness, and tailored treatment plans [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a novel model for characterizing migraine outcomes within real-world data

Hindiyeh¹,

Riskin

Alexander

et al. 2022

J Headache Pain

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Background In disease areas with ‘soft’ outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and other routinely collected data can be challenging due to how the data are collected and recorded. In this study, we aimed to define and validate a scalable framework model to measure outcomes of migraine treatment and prevention by use of artificial intelligence (AI) algorithms within EHR data. Methods Headache specialists defined descriptive features based on routinely collected clinical data. Data elements were weighted to define a 10-point scale encompassing headache severity (1–7 points) and associated features (0–3 points). A test data set was identified, and a reference standard was manually produced by trained annotators. Automation (i.e., AI) was used to extract features from the unstructured data of patient encounters and compared to the reference standard. A threshold of 70% close agreement (within 1 point) between the automated score and the human annotator was considered to be a sufficient extraction accuracy. The accuracy of AI in identifying features used to construct the outcome model was also evaluated and success was defined as achieving an F1 score (i.e., the weighted harmonic mean of the precision and recall) of 80% in identifying encounters. Results Using data from 2,006 encounters, 11 features were identified and included in the model; the average F1 scores for automated extraction were 92.0% for AI applied to unstructured data. The outcome model had excellent accuracy in characterizing migraine status with an exact match for 77.2% of encounters and a close match (within 1 point) for 82.2%, compared with manual extraction scores—well above the 70% match threshold set prior to the study. Conclusion Our findings indicate the feasibility of technology-enabled models for validated determination of soft outcomes such as migraine progression using the data elements typically captured in the real-world clinical setting, providing a scalable approach to credible EHR-based clinical studies. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%