Introduction
Multiple definitions for “difficult to treat” patients (DTP) were given throughout the years. While most authors focus on diagnoses, others focus on clinical, social and demographic ...factors, which should be regarded as factors of bad prognosis and elevated costs for the healthcare systems.
Objectives
To identify and haracterize DTP patients admitted in acute ward, based on practical criteria.
Methods
Through the hospital’s IT services, all acute inpatient episodes at Centro Hospitalar Psiquiátrico de Lisboa were collected, since 2017. Cluster analysis was performed, regarding number of previous admissions (PA) and days of admission. Descriptive and comparative statistics (with multiple comparisons) for the different clusters, regarding age, gender, diagnosis at discharge (according to ICD10), and, to the DTP, previous medical following, compliance to medication, and substance use at admission.
Results
Three clusters were identified: (C1, n=5861) a larger, uncharacteristic one; (C2, n=1168) with a higher number of PA (average of 8, versus less than 2 on the others); and (C3, n=1462) with higher number of days of admissions (58 versus less than 16). Statistical significance was found regarding age (higher in C3), gender (more men in C2), nationality (C1 with more foreigners). Regarding diagnosis at discharge, statistical difference was found between the 3 groups: C1 has significantly less patients with Schizophrenia (11% versus 30% in the others), but more depressive (21% versus 6% in C2 and 12% in C3) and neurotic disorders. C2 presented less dementias (0,5% versus 3% in C1 and 10% in C3) and delusional disorders, but more bipolar disorders (24% versus 15% in C1 and C3); C3 represented less episodes due to substance abuse (alcohol or others) and personality disorders. In both C2 and C3, no psychiatric consultation happened in the 3 months prior admission to around 40% of episodes, and 50% had stopped medication. The majority had only oral medication. Almost 24% of C2 tested positive for cannabinoids, with no differences regarding other substances.
Conclusions
These findings allow the definition of 2 kinds of DTP, which present unique characteristics but some common features (namely poor adherence to consultations and are in therapeutic compliance). An assertive multidisciplinary approach, focused on current treatment and relapse prevention (including social structures, more frequent clinical follow-up, and rehabilitation centers), will be the key to their treatment.
Disclosure of Interest
None Declared
The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still ...a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.
We present a review of second language researchers’ use of cluster analysis, an advanced statistical method still uncommon but increasingly used to identify groups or patterns in a dataset and to ...examine group differences. After describing key methodological considerations in conducting cluster analysis, we present a methodological synthesis of 65 studies published between 1989 and 2018 that employed cluster analysis. We specifically review the use of cluster analysis for themes of usage and reporting practices. Our findings indicate that hierarchical cluster analysis and K‐means cluster analysis were the most commonly used cluster methods, but the widespread use of these two methods tended not to be accompanied by sound reporting practices, particularly when justifying cluster solutions. In our analysis, we highlight concerns related to reporting and evaluation. For future use and to inform methodological practices in second language research, we briefly report on a sample study of cluster analysis that uses open data.
Los últimos años de la escuela secundária se caracterizan por desafíos interpersonales y académicos, además de altos niveles de deserción y fracaso escolar. Sin embargo, son pocos los estudios que ...tienen como objetivo investigar este período de la trayectoria educativa, con el fin de identificar recursos personales y contextuales. El objetivo fue analizar perfiles de ajuste psicosocial de los estudiantes, considerando factores de riesgo (exposición a la violencia y discriminación diaria), protección (apoyo social y clima escolar) y ajuste indicador (satisfacción con la vida). Participaron 709 estudiantes que cursaban los grados 7º, 8º y 9º de escuelas públicas. Análisis de cluster identificaron perfiles: resiliente, con valores altos de indicadores de riesgo con buen ajuste; vulnerable, con índices de alto riesgo y bajo ajuste. Se concluye que invertir en la reducción de factores de riesgo y potenciación de factores protectores, a través de programas preventivos, es fundamental para el desarrollo.
Cluster analysis of IRPJ precedents in CARF Costa, Fabiano de Castro Liberato; Martinez, Antonio Lopo; Klann, Roberto Carlos
Revista de contabilidade e organizações,
01/2023, Letnik:
17
Journal Article
Recenzirano
Odprti dostop
O objetivo deste estado foi agrupar acordaos do Conselho Administrativo de Recursos Piscáis i'(ART) relacionados ao Imposto de Renda Pessoa Jurídica (IRPJ), prolatados entre 2016 e 2020, empregando ...técnicas de aprendizado de máquina (VIL) para a clusterizaçao de documentos textuais. A análise resultan em 13 clusters exclusivos, um ochado inédito na literatura contabil tributaria no Brasil. Essa identificaçao é relevante para o (ARI', contribuintes, administraçao tributaria e profissionais contábeis e tributaristas envolvidos em questöes contábeis e tributarias relacionadas ao IRPJ. Os algoritmos de AÍL utilizados mostraram-se eficientes na resoluçao de problemas complexos de processamento de linguagem natural (PLN), como criar representaçöes vetoriais de temos e identificar temáticas em dados nao estmturados, fomecendo contribuiçöes valiosas para o entendimento de materias controversas no IRPJ á luz da jurisprudencia administrativa. A clusterizaçao de precedentes se traduz em maior acessibilidade e análise de padrees nos jidgamentos, facilitando a tomada de decisöes na contabilidade tributaria.
Abstract
Introduction
Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent ...work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records.
Methods
We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method.
Results
Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles.
Conclusion
These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology.
Support
National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
K-Means is a simple clustering algorithm that has the ability to throw large amounts of data, partition datasets into several clusters k. The algorithm is quite easy to implement and run, relatively ...fast and efficient. Another division of K-Means still has several weaknesses, namely in determining the number of clusters, determining the cluster center. The results of the cluster formed from the K-means method is very dependent on the initiation of the initial cluster center value provided. This causes the results of the cluster to be a solution that is locally optimal. This research was conducted to overcome the weaknesses in the K-Means algorithm, namely: improvements to the K-Means algorithm produce better clusters, namely the application of Sum Of Squared Error (SSE) to help K-Means Clustering in determining the optimum number of clusters, From this modification process, it is expected that the cluster center obtained will produce clusters, where the cluster members have a high level of similarity. Improving the performance of the K-Means cluster will be applied to determining the number of clusters using the elbow method.
PM2.5 has become a global challenge threatening human health, climate, and the environment. PM2.5 is ranked as the most common cause of premature mortality and morbidity. Therefore, the current study ...endeavors to probe the spatiodynamic characteristics of PM2.5 in the Republic of Niger and its impacts on human health from 1998 to 2019. Based on remotely sensed satellite datasets, the study found that the concentration of PM2.5 continued to rise in Niger from 68.85 μg/m3 in 1998 to 70.47 μg/m3 in 2019. During the study period, the annual average PM2.5 concentration is far above the WHO guidelines and the interim target-1 (35 μg/m3). The overall annual growth rate of PM2.5 concentration in Niger is 0.02 μg/m3/year. The health risk (HR) due to PM2.5 exposure is also escalated in Niger, particularly, in Southern Niger. The extent of the extremely high-risk areas corresponding to 1 × 104–9.4 × 105 μg.persons/m3 is increased from 0.9% (2000) to 2.8% (2019). Niamey, southern Dakoro, Mayahi, Tessaoua, Mirriah, Magaria, Matameye, Aguié, Madarounfa, Groumdji, Madaoua, Bouza, Keita, eastern Tahoua, eastern Illéla, Bkomnni, southern Dogon-Doutchi, Gaya, eastern Boboye, central Kollo, and western Tillabéry are experienced high HR due to long-term exposure to PM2.5. These findings indicate that PM2.5 causes a serious health risk across Niger. There is an immediate need to carry out its regional control. Therefore, policymakers and the Nigerien government should make conscious efforts to identify the priority target areas with radically innovative appropriate mitigation interventions.
Display omitted
•PM2.5 continues to upsurge in Niger with a growth rate of 0.02 μg/m3/year.•The annual average PM2.5 concentrations are far above the WHO guidelines.•Southern Niger is under extremely-high health risk areas.•The health risk of the population to PM2.5 exposure rise from 2000 to 2019.
PDF
