e.g. mhealth
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Both systems support two phenotyping methods: (1) basic phenotyping (closely aligned with the ABE classification in GOLD 2025) and (2) expanded phenotyping (combines the ABE classification with stratification based on SABA use levels—low or high: L or H), offering a nuanced characterization aligned with Seleida’s risk profiles.
3. To assess the model’s clinical use for early risk detection and phenotype-driven decision support.
JMIR Med Inform 2025;13:e74932
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Deep Phenotyping of Obesity: Electronic Health Record–Based Temporal Modeling Study
The inconsistent therapeutic response makes obesity phenotyping (ie, classify obesity into subtypes) and associated precision management important targets of investigation. Earlier advancements in obesity staging [26] and phenotyping-guided pragmatic trials [27] have moved beyond the oversimplified classification by BMI [28] and demonstrated clinical values [29-31]. However, health care practitioners (HCPs) still found major obstacles in adopting them for obesity precision medicine [32].
J Med Internet Res 2025;27:e70140
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Trends and Gaps in Digital Precision Hypertension Management: Scoping Review
There were 4 studies with a precision health focus on phenotyping [19,55-57] using a range of demographic, behavioral, clinical, and genetic data to apply phenotyping in diverse ways to enhance understanding and management of HTN (Table 6). Sample sizes ranged from 13 for a qualitative study [57] to 764,135 in a whole-genome sequencing analysis [19].
J Med Internet Res 2025;27:e59841
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Assessing Digital Phenotyping for App Recommendations and Sustained Engagement: Cohort Study
Digital phenotyping methods can also be used to predict changes in anxiety and depression [22], meaning that it may be possible to suggest mental health app use early and as a preventive approach.
In piloting how digital phenotyping may help improve app recommendation, there are many metrics to consider. The most important may be engagement, as without engagement, even the most effective app will not be impactful.
JMIR Form Res 2024;8:e62725
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This work aims to encapsulate and outline a pertinent assortment of research and clinical studies that leverage the RADAR-base platform for data collection and digital phenotyping. The focus of this paper will be on distilling prevalent usage patterns and addressing the challenges encountered in using the platform for diverse research endeavors.
JMIR Ment Health 2024;11:e51259
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Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review
An area that will be instrumental in applying EMRs to public health is case phenotyping, which is developing case definitions to identify positive cases of a disorder in EMR data.
Accurate case identification in EMRs is an area where more research needs to be conducted. This is especially true for case identification of psychiatric disorders. Previous reviews of phenotyping algorithms for psychiatric disorders only considered primary care databases as their setting [10,11].
JMIR Med Inform 2024;12:e49781
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This is especially true for phenotyping and similar cohort identification tasks such as clinical trial recruitment, which often have specific inclusion and exclusion criteria that draw from information elements in both structured and unstructured data.
When the inclusion or exclusion criteria and features of interest pertain to emergent entities of interest, however, as was the case with COVID-19, existing approaches to constructing NLP algorithms break down.
JMIR Med Inform 2024;12:e49997
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For example, a clinician may identify a patient with T2 D at the point of care despite having incomplete data; however, a T2 D phenotyping algorithm may miss the same patient in a large EHR data warehouse due to the underlying data quality issues; hence, the patient may inadvertently be excluded from a research study or population health intervention.
Various data quality frameworks have been proposed to measure the quality of health care data.
JMIR Med Inform 2024;12:e56734
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LASSO and random forest were selected because both are well documented in the informatics literature and widely used for phenotyping applications [37]. Fit metrics were compared using Mc Nemar chi-square tests. The classifiers were anticipated to have predictive ability, with accuracy ranging from 70% to 95%.
JMIR Ment Health 2023;10:e47084
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A Call to Expand the Scope of Digital Phenotyping
For instance, others have used digital phenotyping to define recovery in surgical patients and monitor for signs of respiratory depression to potentially prevent opioid overdose [7-9]. With the ever-expanding wealth of digital health information available, digital phenotyping will become an inevitable part of medicine.
Despite these advances, there remains controversy over the optimal way (or gold standard) to collect data for digital phenotyping.
J Med Internet Res 2023;25:e39546
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