e.g. mhealth
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Skip search results from other journals and go to results- 11 Journal of Medical Internet Research
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ADRD codes were dropped from the matrix P to not confound clustering based on structured ADRD phenotype. A schematic depicting the ICD representation pipeline is provided in Figure 1 A.
Visualization of the clustering pipeline for (A) International Classification of Diseases (ICD) codes and (B) notes. For each subfigure, the workflow goes from left to right. BERT: Bidirectional Encoder Representations from Transformers.
Clinical notes were encoded using Clinical BERT before clustering.
JMIR Aging 2025;8:e65178
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The structured data were subsequently used for clustering.
Flowchart of radiology reports clustering using LLM. LLM: large language model.
The Med Txt-RR dataset was used in this study [5,18], comprising 135 Japanese radiology reports generated by 9 radiologists who interpreted CT images of 15 lung cancer cases sourced from Radiopaedia [19]. This dataset was used in an NTCIR-16 shared task [5], where participants competed to achieve optimal clustering performance.
JMIR Cancer 2025;11:e57275
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Local spatial autocorrelation reflects the degree of correlation between each local unit and its neighboring units and is applied to identify high- and low-value clustering of local spatial locations [23]. The local indicators of spatial autocorrelation (LISA) plots were then used to identify the local clusters of ONID in Guangdong Province.
JMIR Public Health Surveill 2024;10:e57851
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It employs various time series techniques to spot such patterns using matrix profiles and multivariate clustering. The Open APS Data Commons dataset, an extensive dataset collected in real-life conditions, was analyzed to discover temporal patterns in insulin needs driven by well-known factors such as carbohydrates and potentially novel factors. The results are limited to disclosing interesting temporal patterns in insulin need that cannot be explained solely by carbohydrates through the performed analysis.
JMIRx Med 2024;5:e66922
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It employs various time series techniques to spot such patterns using matrix profiles and multivariate clustering. The Open APS Data Commons dataset, an extensive dataset collected in real-life conditions, was analyzed to discover temporal patterns in insulin need driven by well-known factors such as carbohydrates and potentially novel factors. The results are limited to disclosing interesting temporal patterns in insulin need that cannot be explained solely by carbohydrates through the performed analysis.
JMIRx Med 2024;5:e66643
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To group similar days, we used time series k-means clustering. To prevent bias from different measurement scales, we applied min-max scaling of IOB, COB, and IG values to a range of 0 to 10 for each participant.
JMIRx Med 2024;5:e44384
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We used a clustering algorithm [19] to identify homogeneous profiles of situations involving violence. The Partitioning Around Medoids [20] algorithm was used to automatically detect groups or clusters of similar patients, provided the desired number of groups was specified. We applied the consensus clustering framework [21] to determine the optimal number of clusters. Finally, we investigated the clinical characteristics underlying each violent situation profile.
JMIR Public Health Surveill 2024;10:e43563
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Consensus clustering is a common clustering method used in machine learning algorithms. It uses repeated sampling to draw a certain sample for the data set, specifies the number of k clusters, and calculates the rationality under different numbers of clusters. The k means algorithm is a widely used clustering analysis algorithm that is simple, efficient, and can make the clustering results locally optimal [4].
JMIR Form Res 2024;8:e54638
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