scholarly journals Latent class cluster analysis identified hidden headache phenotypes in COVID-19: impact of pulmonary infiltration and IL-6

2021 ◽  
Author(s):  
Ömer Karadaş ◽  
Bilgin Öztürk ◽  
Ali Rıza Sonkaya ◽  
Bahar Taşdelen ◽  
Aynur Özge ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Latent Class Cluster Analysis ◽  
Pulmonary Infiltration

scholarly journals Negative psychotic symptoms and impaired role functioning predict transition outcomes in the at-risk mental state: a latent class cluster analysis study

2013 ◽  
Vol 43 (11) ◽  
pp. 2311-2325 ◽  
Author(s):  
L. R. Valmaggia ◽  
D. Stahl ◽  
A. R. Yung ◽  
B. Nelson ◽  
P. Fusar-Poli ◽  
...  
Keyword(s):  
At Risk ◽  
Cluster Analysis ◽  
Mental State ◽  
Latent Class ◽  
Mental States ◽  
Psychotic Symptoms ◽  
Role Functioning ◽  
Latent Class Cluster Analysis ◽  
At Risk Mental State

BackgroundMany research groups have attempted to predict which individuals with an at-risk mental state (ARMS) for psychosis will later develop a psychotic disorder. However, it is difficult to predict the course and outcome based on individual symptoms scores.MethodData from 318 ARMS individuals from two specialized services for ARMS subjects were analysed using latent class cluster analysis (LCCA). The score on the Comprehensive Assessment of At-Risk Mental States (CAARMS) was used to explore the number, size and symptom profiles of latent classes.ResultsLCCA produced four high-risk classes, censored after 2 years of follow-up: class 1 (mild) had the lowest transition risk (4.9%). Subjects in this group had the lowest scores on all the CAARMS items, they were younger, more likely to be students and had the highest Global Assessment of Functioning (GAF) score. Subjects in class 2 (moderate) had a transition risk of 10.9%, scored moderately on all CAARMS items and were more likely to be in employment. Those in class 3 (moderate–severe) had a transition risk of 11.4% and scored moderately severe on the CAARMS. Subjects in class 4 (severe) had the highest transition risk (41.2%), they scored highest on the CAARMS, had the lowest GAF score and were more likely to be unemployed. Overall, class 4 was best distinguished from the other classes on the alogia, avolition/apathy, anhedonia, social isolation and impaired role functioning.ConclusionsThe different classes of symptoms were associated with significant differences in the risk of transition at 2 years of follow-up. Symptomatic clustering predicts prognosis better than individual symptoms.

scholarly journals Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

2020 ◽  
Vol 11 ◽  
Author(s):  
Mariagrazia Benassi ◽  
Sara Garofalo ◽  
Federica Ambrosini ◽  
Rosa Patrizia Sant’Angelo ◽  
Roberta Raggini ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Psychiatric Inpatients ◽  
Latent Class Cluster Analysis ◽  
Cognitive Heterogeneity

scholarly journals LATENT CLASS CLUSTER ANALYSIS UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA 2017

2020 ◽  
Vol 1 (2) ◽  
pp. 46-54
Author(s):  
Atika Nurani Ambarwati
Keyword(s):  
Cluster Analysis ◽  
Latent Class ◽  
Latent Class Cluster Analysis

Pembangunan merupakan salah satu upaya untuk meningkatkan kesejahteraan dan kemakmuran masyarakat, dengan kedudukan manusia menjadi topik sentra dalam tiap perolehan program pembangunan. Keberhasilan pembangunan suatu bangsa ditentukan oleh ketersediaan Sumber Daya Manusia (SDM) yang berkualitas. Untuk mengukur suatu keberhasilan pembangunan manusia suatu bangsa salah satu indikator yang digunakan adalah Indeks Pembangun Manusia (IPM). IPM di Provinsi Jawa Tengah mengalami peningkatan setiap tahunnya. Pada tahun 2017 pembangunan manusia di Provinsi Jawa Tengah mengalami kenaikan status dari status “sedang” menjadi status “tinggi” yaitu sebesar 70,52 persen. Salah satu permasalahan pembangunan di Jawa Tengah adalah tinggi rendahnya Indeks Pembangunan Manusia (IPM) hanya ditunjukkan melalui indeks komposit, tetapi tidak ditunjukkan indikator mana yang dominan terhadap tinggi rendahnya peringkat Indeks Pembangunan Manusia (IPM). Maka pengelompokan dan pengklasifikasian wilayah kabupaten/kota di Provinsi Jawa Tengah perlu dilakukan sehingga dapat menunjukkan indikator mana yang dominan terhadap tinggi rendahnya peringkat IPM. Latent Class Cluster Analysis merupakan salah satu metode untuk mengklasifikasikan kabupaten/kota. Hasil dari penelitian mendapatkan 2 kelompok. Kelompok pertama terdiri dari kabupaten atau kota yang memiliki pembangunan manusia rendah. Kelompok kedua terdiri dari kabupaten atau kota yang memiliki pembangunan manusia tinggi.

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