The academic curriculum has shown to promote sedentary behavior in college students. This study aimed to profile the physical fitness of physical education majors using unsupervised machine learning a...
Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-dri...
Observational EHR data from a tertiary paediatric hospital, containing 61 522 unique patients and 3315 unique ICD-10 diagnosis codes was used, after preprocessing. K-means clustering was applied to id...
Four age clusters of diseases were identified, broadly aligning to ages between: 0 and 1; 1 and 5; 5 and 13; 13 and 18. Diagnoses, within the clusters, aligned to existing knowledge regarding the prop...
Unsupervised ML applied to EHR data identifies clinically relevant age distributions of diagnoses which can augment existing decision making. However, biases within healthcare datasets dramatically im...
Cell deformability is a well-established marker of cell states for diagnostic purposes. However, the measurement of a wide range of different deformability levels is still challenging, especially in c...
Immune-related liver injury (liver-irAE) is a clinical problem with a potentially poor prognosis....
We retrospectively collected clinical data from patients treated with immune checkpoint inhibitors between September 2014 and December 2021 at the Nagoya University Hospital. Using an unsupervised mac...
This study included a total of 702 patients. Among them, 492 (70.1%) patients were male, and the mean age was 66.6 years. During the mean follow-up period of 423 days, severe liver-irAEs (Common Termi...
The combined assessment of multiple markers and body temperature may help stratify high-risk groups for developing liver-irAE....
Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and over...
We assessed a total of 43 symptoms in a discovery sample of 6,602 participants of the population-based Rotterdam Study between 2009 and 2013, and in a replication sample of 3,005 participants between ...
First, clustering analyses of the questionnaire items suggested a three-cluster solution representing clusters of "mixed" symptoms, "depressed affect and nervousness", and "troubled sleep and interper...
We identified three clusters of psychiatric symptoms that most commonly co-occur in a population-based sample. These symptoms clustered stable over samples, but across the topics of depression, anxiet...
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. Th...
Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical ...
The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respe...
We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we fo...
However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information....
Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifica...
Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe tran...
It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI)....
We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-m...
The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively....
We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI....
Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical va...
Statins could elevate hepatic transaminase in ischemic stroke patients. There needed to be more evidence on which method stopped statins or adjusting the dose of statins was better for patients. And n...
We collected ischaemic stroke patients with elevated hepatic transaminase when they take statins. The outcome was a recurrent stroke rate, transaminase value after stopping or adjusted, mortality, and...
The patients stopping statins had a higher stroke recurrence and rate of FFO (mRS 0-2), a lower mean value of transaminase, and mortality. By difference unsupervised machine learning group, the km2 gr...
For ischemic patients with elevated hepatic transaminase, stopping statins temporarily was a better choice of treatment strategy. These patients who were younger, male, with a lesser NIHSS score at ad...
