Schizophrenia Related Hospitalizations - a Big Data Analysis of a National Hospitalization Database

Epilepsy hospitalizations and mental disorders: A Portuguese population-based observational retrospective study (2008-2015)

BACKGROUND

Psychiatric comorbidities are highly frequent in people with epilepsy and were found to be markers of poorer prognosis. These comorbidities increase the use of healthcare resources, including emergency department visits and inpatient care. Despite this, there is little information on healthcare utilization associated with a wide range of comorbid mental disorders in people with epilepsy (PWE).

OBJECTIVE

To characterize registered mental disorders among all hospitalizations with a primary diagnosis of epilepsy and to analyze their association with crucial hospitalization outcomes.

METHODS

An observational retrospective study was performed using administrative data from hospitalization episodes with epilepsy as the primary diagnosis discharged between 2008 and 2015. Mental disorder categories 650 to 670 from Clinical Classification Software were selected as secondary diagnoses. Mann-Whitney U, Kruskall-Wallis, and Chi-squared tests were used to establish comparisons. For each episode, data regarding hospitalization outcomes was retrieved, including length of stay (LoS), in-hospital mortality (IHM), 8-year period readmissions, and total estimated charges.

RESULTS

Overall, 27,785 hospitalizations were analyzed and 33.9% had registered mental disorders, with alcohol-related disorders being the most prevalent (11.7%). For episodes with a concomitant register of a mental disorder, LoS was significantly longer (5.0 vs. 4.0 days, P <0.001), and IHM was higher (2.8% vs. 2.2%, P <0.001), as were readmissions (25.5% vs. 23.7%, P <0.001), and median episodes' charges (1,578.7 vs. 1,324.4 euros, P <0.001).

CONCLUSION

Epilepsy-related hospitalizations with registered mental disorders heightened the utilization of healthcare resources, stressing the importance of diagnosing and treating mental disorders in PWE.

Model innovation of students' mental health education from the perspective of big data

To improve the effect of college students' mental health education, and to meet the needs of mental health education data processing, this article combines big data technology and gene regulation ideas to propose a mental health education data processing algorithm based on big data technology and immunotherapy gene regulation network. Moreover, this article uses improved algorithms to extract features from mental health education network data, controls the problems in mental health education in real‐time, and proposes targeted improvement strategies. In addition, this article combines the improved algorithm to construct a mental health education system based on big data technology and immunotherapy gene regulation network are combined for an improved algorithm to process mental health education data. Finally, this article verifies the effectiveness of the system through experimental research.

A study on the changing trend and influencing factors of hospitalization costs of schizophrenia in economically underdeveloped areas of China

The public health problems caused by schizophrenia are becoming increasingly prominent and can place a huge economic burden on society. This study takes Gansu Province as an example to analyze the level and changing trend of the economic burden of schizophrenia inpatients in economically underdeveloped areas of China. Using a multi-stage stratified cluster sampling method, 39,054 schizophrenics from 197 medical and health institutions in Gansu Province were selected as the research objects, and their medical expenses and related medical records were obtained from the medical information system. The rank sum test and Spearman rank correlation were used for univariate analysis. Quantile regression and random forest were used to analyze the influencing factors. The results show that the average length of stay of schizophrenics in Gansu Province of China was 52.01 days, and the average hospitalization cost was USD1653.96 from 2014 to 2019. During the six years, the average hospitalization costs per time decreased from USD2136.85 to USD1401.33. The average out-of-pocket costs per time decreased from USD1238.78 to USD267.68. And the average daily hospitalization costs increased from USD38.18 to USD41.25. The main factors influencing hospitalization costs are length of stay, proportion of medications, and schizophrenic subtype. The hospitalization costs per time of schizophrenics in Gansu Province have decreased but remain at a high level compared to some other chronic non-communicable diseases. In the future, attention should be paid to improving the efficiency of medical institutions, enhancing community management, and promoting the transformation of the management model of schizophrenia.

Research on intelligent analysis strategies to improve athletes' psychological experience in the era of artificial intelligence

In order to improve the psychological quality of athletes, this paper combines artificial intelligence technology to quantitatively analyze the psychological experience of athletes. Moreover, in view of the insufficiency of the ASI method to standardize the data, this study proposes to use the normalization method for processing. In addition, in order to verify the feasibility and effectiveness of the research and development of the multi-dimensional dual-objective CD-CAT topic selection strategy, this paper adopts the Monte Carlo simulation experiment method to analyze the data. Finally, this paper constructs an analysis system of athlete's psychological experience and intelligent decision-making based on artificial intelligence. The experimental data analysis results show that the intelligent decision-making system based on artificial intelligence for athletes' psychological experience proposed in this paper has a good effect and can effectively promote the psychological training effect of athletes.

Online music-assisted rehabilitation system for depressed people based on deep learning

The processing of negative emotions is closely related to the occurrence of depression, and improving the mood of patients with depression has an important effect on improving symptoms. This article applies deep learning to the diagnosis and treatment of depression patients, combined with music-assisted rehabilitation to help depression patients recover. Moreover, this paper strengthens deep learning, combines the psychological characteristics of depression to improve the algorithm, and analyzes the algorithm with music-assisted rehabilitation, and obtains an intelligent algorithm that can be applied to the music-assisted rehabilitation system. Finally, this paper constructs an online music-assisted rehabilitation system for depression based on deep learning. The results of the experimental research show that the online music-assisted rehabilitation system for depression based on deep learning can play an important role in the treatment of depression.

Improvement of Student Weariness Emotion in English Classroom Based on Intelligent Internet of Things and Big Data Technology

In order to improve the recognition effect of student weariness emotion in English classroom, this paper combines intelligent Internet of Things technology and big data technology to construct an improvement model of student weariness emotion in English classroom. In the process of student facial expression recognition, according to the given grayscale threshold, this paper extracts the surface contour information from the three-dimensional volume data, extracts the student's surface contour information, and uses triangular facets to fit to form a triangular mesh. Moreover, this paper renders a triangular mesh model and shows how to speed up the calculation of PFH. In addition, this paper proposes a Fast Point Feature Histogram, which uses an iterative closest point fine registration algorithm for image registration. Finally, this paper constructs an emotion recognition model of students' weariness in English classroom. From the test results, it can be seen that the student weariness emotion recognition system in English classroom proposed in this paper can effectively identify students' weariness emotion.

Impact of history of mental disorders on short-term mortality among hospitalized patients with sepsis: A population-based cohort study

Background

Mental disorders are associated with markedly reduced life expectancy, in part due to an increased risk of death due to infection, likely reflecting sepsis-associated mortality. Patients with mental disorders are at an increased risk of sepsis, but data on the prognostic impact of mental disorders in sepsis are sparse, showing conflicting findings.

Methods

We used statewide data to identify hospitalizations aged ≥18 years with sepsis in Texas during 2014–2017. Mental disorders, including mood, anxiety, psychosis, and personality disorders were identified using Clinical Classification Software codes. Multilevel, multivariable logistic regression with propensity adjustment (primary model), with propensity score matching, and multivariable logistic regression as alternative models, were used to estimate the association between mental disorders and short-term mortality (defined as in-hospital mortality or discharge to hospice). Additional models were fitted for sensitivity analyses and to estimate the prognostic associations of individual categories of mental disorders.

Results

Among 283,025 hospitalizations with sepsis, 56,904 (20.1%) had mental disorders. Hospitalizations with vs without mental disorders were younger (age 18–44 years 12.2% vs 10.6%), more commonly white (61.0% vs 49.8%), with lower burden of comorbidities (mean [SD] Deyo comorbidity index 2.53 [2.27] vs 2.73 [2.47]), and with lower need for organ support (mechanical ventilation 32.8% vs 36.0%); p<0.0001 for all comparisons. Crude short-term mortality among sepsis hospitalizations with and without mental disorders was 25.0% vs 32.8%, respectively. On adjusted analyses, mental disorders remained associated with lower odds of short-term mortality (adjusted odds ratio 0.792 [95% CI 0.772–0.812]). This finding was consistent on the alternative modeling approaches, sensitivity analyses, and examination of individual categories of mental disorders.

Conclusions

Mental disorders were associated, unexpectedly, with markedly lower risk of short-term mortality in sepsis. Further studies to examine the mechanisms underlying these findings may inform future efforts to improve sepsis outcomes.

Bipolar Disorder Related Hospitalizations - a Descriptive Nationwide Study Using a Big Data Approach

Bipolar Disorder (BD) is a mental disorder which frequently requires long hospitalizations and need for acute psychiatric care. The aim of this study was to describe a nationwide perspective of BD related hospitalizations and to use a BigData based approach in mental health research. We performed a retrospective observational study using a nationwide hospitalization database containing all hospitalizations registered in Portuguese public hospitals from 2008-2015. Hospitalizations with a primary diagnosis of BD were selected based on International Classification of Diseases version 9, Clinical Modification (ICD-9-CM) codes of diagnosis 296.xx (excluding 296.2x; 296.3x and 296.9x). From 20,807 hospitalizations belonging to 13,300 patients, around 33.4% occurred in male patients with a median length of stay of 16.0 days and a mean age of 47.9 years. The most common hospitalization diagnosis in BD has the code 296.4x (manic episode) representing 34.3% of all hospitalizations, followed by the code 296.5x (depressed episode) with 21.4%. The mean estimated hospitalization charge was 3,508.5€ per episode, with a total charge of 73M€ in the 8-year period of this study.This is a nationwide study giving a broad perspective of the BD hospitalization panorama at a national level. We found important differences in hospitalization characteristics by sex, age and primary diagnosis.

Comorbidity combinations in schizophrenia inpatients and their associations with service utilization: A medical record-based analysis using association rule mining

BACKGROUND

Comorbidities are common among patients with schizophrenia yet the prevalence of comorbidity combinations and their associations with inpatient service utilization and readmission have been scarcely explored.

METHODS

Data were extracted from discharge summaries of patients whose primary diagnosis was schizophrenia spectrum disorders (ICD-10: F20-F29). We identified 30 most frequent comorbidities in patients' secondary diagnoses and then used the association rule mining (ARM) method to derive comorbidity combinations associated with length of stay (LOS), daily expense and one-year readmission.

RESULTS

The study included data from 8252 patients. The top five most common comorbidities were extrapyramidal syndrome (EPS, 44.58%), constipation (31.63%), common cold (21.80%), hyperlipidemia (20.99%) and tachycardia (19.13%). Most comorbidity combinations identified by ARM were significantly associated with longer LOS (≥70 days), few were associated with higher daily expenses, and fewer with readmission. The 3-way combination of common cold, hyperlipidemia and fatty liver had the strongest association with longer LOS (adjusted OR (aOR): 3.38, 95% CI: 2.12-5.38). The combination of EPS and mild cognitive disorder was associated with higher daily expense (≥700 RMB) (aOR: 1.67, 95% CI: 1.20-2.31). The combination of constipation, tachycardia and fatty liver were associated with higher 1-year readmission (aOR: 2.05, 95% CI: 1.03-4.09).

CONCLUSION

EPS, constipation, and tachycardia were among the most commonly reported comorbidities in schizophrenia patients in Beijing, China. Specific groups of comorbidities may contribute to higher inpatient psychiatric service utilization and readmission. The mechanism behind the associations and potential interventions to optimize service use warrant further investigation.