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One promising approach to capturing this variability is through the identification of “phenotypes,” individual-specific webs of links between social and built environmental determinants [14,15]. These phenotypes have the potential to identify overlaps in ILD and salient intervention targets for health behavior [14-16].
JMIR Res Protoc 2025;14:e68667
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Deriving digital phenotypes can facilitate efficient clinical decision-making. Whereas passive sensing data are continuous (eg, backspace rates can be any value between 0 and 1), clinical decisions are often discrete (eg, treat or not to treat with medication or psychotherapy). Digital phenotypes are ecologically valid, data-driven, and require low patient burden [1,39].
J Med Internet Res 2024;26:e51269
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Passive digital phenotypes, which encompass various aspects such as location, activity, sleep, and smartphone use, have shown meaningful correlations with mental health states, facilitating the identification of risk levels and timely interventions [34-37]. However, collecting passive digital phenotypes presents several challenges. One significant issue is the potential for noise due to sensor dysfunction or disconnection, which can introduce inaccuracies in the collected data [38].
JMIR Form Res 2024;8:e59623
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The ongoing challenge of understanding the effect of key data quality issues on T2 D phenotypes is further exacerbated by the fact that T2 D phenotypes use multiple data types, such as diagnosis codes, medications, and laboratory results. Additionally, given the variability of key data quality issues of these data types across EHRs [10,11], measuring the effect of key existing data quality issues on T2 D phenotypes in one EHR may not translate into generalizable findings.
JMIR Med Inform 2024;12:e56734
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This approach uses multiple indicators to identify homogeneous subgroups—or phenotypes—with similar characteristics within a heterogeneous population [25]; these phenotypes usually have distinct features that result in divergent outcomes [26,27], such as differential treatment effects by phenotype [28].
JMIR Public Health Surveill 2024;10:e53371
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To expand on the previous investigation, improved features of the next generation of PAMS include an ML-based patient engagement prediction algorithm to identify d DPP digital engagement phenotypes and to guide and further personalize the messaging intervention.
JMIR AI 2024;3:e47122
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In this study, we propose to use audit log data for the de novo identification of EHR user types (ie, EHR use phenotypes). Phenotype was first introduced by Richesson et al [19] as a biological concept to describe a set of observable biological traits. In the context of EHR use measures, phenotype will be used to describe observable use patterns across gender and specialty differences as defined by an unsupervised clustering approach called affinity propagation.
JMIR Med Inform 2022;10(4):e34954
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Age strata and BMI were found to be different among clusters (P
On the basis of the description of clusters mentioned earlier, the OSA phenotypes can be defined. We classified patients into low (cluster 1), medium (cluster 2), and high (cluster 3) severity phenotypes, as their median AHI corresponded to mild, moderate, and severe levels respectively, defined in PSG for OSA diagnosis.
JMIR Med Inform 2021;9(6):e25124
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We use a knowledge base prototype instead of a knowledge base because we include different levels of facts in Onto KBCF, for example, nucleotide changes, amino acid changes, and clinical phenotypes.
JMIR Med Inform 2018;6(4):e52
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