Evaluating the Effective Factors of Hospital Rooms on Patients’ Recovery Using the Data Mining Method

2021 ◽  
pp. 193758672110313
Author(s):  
S. Nikabadi ◽  
H. Zabihi ◽  
A. Shahcheraghi
Keyword(s):  
Data Mining ◽  
Length Of Hospital Stay ◽  
Knowledge Discovery In Databases ◽  
Hospital Environments ◽  
Pearson Coefficient ◽  
System Data ◽  
Python Programming ◽  
Nursing Station ◽  
The Relationship ◽  
Interior Environment

Objectives: This study aims to investigate the effective environmental factors of hospital rooms in patients’ recovery through data mining techniques. Background: Previous studies have shown the positive effect of the interior environment of the hospitals on patients’ recovery. The methods of these studies were mainly based on the evidence and patients’ perception while hospital environments are associated with a large amount of data that make them an appropriate case for data mining studies. But data mining studies in hospitals mainly focused on medical and management purposes rather than evaluating the interior environment condition. Methods: We analyzed the hospital information system data of a hospital using Python programming language and some of its libraries. Preprocessing and eliminating the outliers, labeling and clustering of diseases, data visualization and analysis, final evaluation, and concluding were done using the knowledge discovery in databases process. Results: Pearson coefficient value for rooms’ area was .5 and, respectively, for the distance from the ward entrance and nursing station were .75 and .70. The χ2 values for the variables of room types, location, and occupation were 24.62, 18.98, and 21.53, respectively, and for the beds’ location was 0.12. Conclusions: The results confirmed the correlation of the length of stay with the room types, location, and occupation, distance from the nursing station and ward entrance and also showed a moderate correlation with the rooms’ area. However, no evidence was found about the relationship between the beds’ location in rooms and patients’ length of hospital stay.

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Relationship between Rational Thinking and Belief in a Just World

1980 ◽  
Vol 46 (1) ◽  
pp. 161-162 ◽  
Author(s):  
Clayton T. Shorkey
Keyword(s):  
Undergraduate Students ◽  
Rational Behavior ◽  
Just World ◽  
Rational Thinking ◽  
Pearson Coefficient ◽  
The World ◽  
The Relationship

The relationship between rational thinking and belief in a just world was examined using scores on the Rational Behavior Inventory and the Just World Scale from 129 undergraduate students. It was hypothesized that rational thinking would be incompatible with absolutistic beliefs that the world is a just place. A Pearson coefficient of —.11 was computed between scores on the two scales; this supports the hypothesis that neither absolutistic acceptance nor rejection of a belief in a just world is related to rational thinking.

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

Visual and quantitative assessment of COVID-19 pneumonia on chest CT: The relationship with disease severity and clinical findings

2021 ◽  
Vol 17 ◽  
Author(s):  
Furkan Kaya ◽  
Petek Şarlak Konya ◽  
Emin Demirel ◽  
Neşe Demirtürk ◽  
Semiha Orhan ◽  
...  
Keyword(s):  
Hospital Stay ◽  
Disease Severity ◽  
Pearson Correlation ◽  
Laboratory Data ◽  
Length Of Hospital Stay ◽  
Chest Ct ◽  
Laboratory Findings ◽  
Pneumonia Severity ◽  
High Attenuation ◽  
The Relationship

Background: Lungs are the primary organ of involvement of COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. Aim: We aimed to evaluate the visual and quantitative pneumonia severity on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. Methods: We retrospectively evaluated adult COVID-19 patients who underwent chest CT, clinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between variables. The Pearson correlation test analyzed the correlation between variables. The diagnostic performance of the variables was assessed using receiver operating characteristic (ROC) analysis was used. Results: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with disease severity (p<0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r=730,p<0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. Conclusion: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of hospitalization.

scholarly journals Proposta de metodologia para a criação de etiqueta de classificação – estudo de caso: desempenho escolar

2016 ◽  
Vol 23 (1) ◽  
pp. 177-191
Author(s):  
Anderson Roges Teixeira Góes ◽  
Maria Teresinha Arns Steiner
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Support Vector ◽  
Vector Machines

Resumo A qualidade na educação tem sido objeto de muita discussão, seja nas escolas e entre seus gestores, seja na mídia ou na literatura. No entanto, uma análise mais profunda na literatura parece não indicar técnicas que explorem bancos de dados com a finalidade de obter classificações para o desempenho escolar, nem tampouco há um consenso sobre o que seja “qualidade educacional”. Diante deste contexto, neste artigo, é proposta uma metodologia que se enquadra no processo KDD (Knowledge Discovery in Databases, ou seja, Descoberta de Conhecimento em Bases de Dados) para a classificação do desempenho de instituições de ensino, de forma comparativa, com base nas notas obtidas na Prova Brasil, um dos itens integrantes do Índice de Desenvolvimento da Educação Básica (IDEB) no Brasil. Para ilustrar a metodologia, esta foi aplicada às escolas públicas municipais de Araucária, PR, região metropolitana de Curitiba, PR, num total de 17, que, por ocasião da pesquisa, ofertavam Ensino Fundamental, considerando as notas obtidas pela totalidade dos alunos dos anos iniciais (1º. ao 5º. ano do ensino fundamental) e dos anos finais (6º. ao 9º. ano do ensino fundamental). Na etapa de Data Mining, principal etapa do processo KDD, foram utilizadas três técnicas de forma comparativa para o Reconhecimento de Padrões: Redes Neurais Artificiais; Support Vector Machines; e Algoritmos Genéticos. Essas técnicas apresentaram resultados satisfatórios na classificação das escolas, representados por meio de uma “Etiqueta de Classificação do Desempenho”. Por meio desta etiqueta, os gestores educacionais poderão ter melhor base para definir as medidas a serem adotadas junto a cada escola, podendo definir mais claramente as metas a serem cumpridas.

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

scholarly journals Implementation of Apriori Algorithm Data Mining for Increase Sales

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Reza Alfianzah ◽  
Rani Irma Handayani ◽  
Murniyati Murniyati
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
A Priori ◽  
Data Mining Algorithm ◽  
Sales Data ◽  
The Right ◽  
Or Organization ◽  
Product Sales ◽  
The Relationship ◽  
Confidence Value

Any company or organization that wants to survive needs to determine the right business strategy. The product sales data carried out by Lakoe Dessert Pondok Kacang will eventually result in a pile of data, so it is unfortunate if it is not re-analyzed. The products offered vary with a wide variety of products as many as 45 products, to find out the products with the most sales and the relationship between one product and another, one of the algorithms is needed in the data mining algorithm, namely the a priori algorithm to find out, and with the help of the Rapidminer 5 application, with a support value 2,4% and a confidence value 50%, products that customers often buy or are interested in can be found. This study used sales data for March 2020, which amounted to 209 transaction data. From the research, it was found that the item with the name Pudding Strawberry and Pudding Vanilla was the product most purchased by consumers. With knowledge of the most sold products and the patterns of purchasing goods by consumers, Lakoe Dessert Pondok Kacang can develop marketing strategies to market other products by analyzing the profits from selling the most sold products and anticipating running out or empty of stock or materials at a later date.

scholarly journals Inflammatory Burden and Persistent CT Lung Abnormalities in COVID-19 Patients

2021 ◽  
Author(s):  
Giulia Besutti ◽  
Paolo Giorgi Rossi ◽  
Marta Ottone ◽  
Lucia Spaggiari ◽  
Simone Canovi ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Length Of Hospital Stay ◽  
Lung Damage ◽  
Reactive Protein ◽  
Lung Abnormalities ◽  
The Relationship ◽  
Lung Ct ◽  
D Dimer ◽  
Inflammatory Burden

Abstract Inflammatory burden is associated with COVID-19 severity and outcomes. Residual computed tomography (CT) lung abnormalities have been reported after COVID-19. The aim was to evaluate the association between inflammatory burden during COVID-19 and residual lung CT abnormalities collected on follow-up CT scans performed 2–3 and 6–7 months after COVID-19, in severe COVID-19 pneumonia survivors. C-reactive protein (CRP) curves describing inflammatory burden during the clinical course were built, and CRP peaks, velocities of increase, and integrals were calculated. Other putative determinants were age, sex, mechanical ventilation, lowest PaO2/FiO2 ratio, D-dimer peak, and length of hospital stay (LOS). Of the 259 included patients (median age 65 years; 30.5% females), 202 (78%) and 100 (38.6%) had residual, predominantly non-fibrotic, abnormalities at 2-3 and 6-7 months, respectively. In age- and sex-adjusted models, best CRP predictors for residual abnormalities were CRP peak (odds ratio [OR] for one standard deviation [SD] increase=1.79; 95% confidence interval [CI]=1.23-2.62) at 2-3 months and CRP integral (OR for one SD increase=2.24; 95%CI=1.53-3.28) at 6-7 months. Hence, inflammation is associated with short- and medium-term lung damage in COVID-19. Other severity measures, including mechanical ventilation and LOS, but not D-dimer, were mediators of the relationship between CRP and residual abnormalities.

scholarly journals EVALUASI PELAPORAN KAMSELTIBCAR LANTAS DALAM MENDUKUNG PELAPORAN DEKADE AKSI KESELAMATAN JALAN

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nunung Nuring Hayati ◽  
Ni Nyoman Suartini ◽  
Achmad Wicaksono ◽  
Ike Fibriani ◽  
Mirtha Firmansyah ◽  
...  
Keyword(s):  
Data Mining ◽  
Road Safety ◽  
Road Transport ◽  
Public Knowledge ◽  
Traffic Law ◽  
Using Data ◽  
Two Parameters ◽  
Self Protection ◽  
The Relationship ◽  
Made In

Kamsebtibcar Lantas or secure, safety, orderliness, and fluency of traffic are made in support of road safety actions reporting on traffic due to the lack of public knowledge about the importance of using self-protection tools that have been determined in traffic law number 22 of 2009 concerning traffic and road transport. By using this program, you can find out the relationship between the factors that cause accidents. From those collected from various regions in East Java, taken from 2016 to 2018. The data obtained can be processed using data mining techniques. This technique works by using a pattern that is a reference for decision making. By using the Fp-Growth algorithm that works with the data tree system to find out the patterns of reporting activities that are happening, this pattern is determined by two parameters, namely support (support value) and confidence (certainty value). With this system, it can help the parties concerned to improve facilities in various Kamseltibcar Lantas reporting activities. Kamsebtibcar lantas atau keamanan, keselamatan, ketertiban, dan kelancaran lalu lintas dibuat dalampedalam mendukung pelaporan aksi keselamatan jalan pada lalu lintas yang dikarenakan minimnya pengetahuan masyarakat tentang pentingnya penggunaan alat perlindungan diri yang telah ditentukan pada undang-undang nomor 22 tahun 2009 tentang lalu lintas dan angkutan jalan. Dengan menggunakan program ini dapat mengetahui hubungan antara faktor-faktor penyebab kecelakaan. Data yang dikumpulkan dari berbagai daerah yang ada di wilayah Jawa Timur diambil pada tahun 2016 sampai dengan 2018. Data yang telah didapat dapat diolah menggunakan teknik data mining. Teknik ini berfungsi dengan menggunakan pola yang menjadi acuan untuk penentuan keputusan. Dengan menggunakan algoritma Fp-Growth yang bekerja dengan sistem data tree untuk mengetahui pola kegiatan pelaporan kamsebticar lalu lintas yang sedang terjadi, pola ini ditentukan dengan dua parameter, yaitu support (nilai penunjang) dan confidence (nilai kepastian). Dengan sistem ini dapat membantu pihak yang berkaitan untuk meningkatkan fasilitas dalam berbagai kegiatan pelaporan Kamseltibcar Lantas.

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