scholarly journals Object and subject Heavy-NP shift in Arabic

2014 ◽  
Vol 2 ◽  
pp. 23-33 ◽  
Author(s):  
Emad Mohamed
Keyword(s):  
Logistic Regression ◽  
Noun Phrase ◽  
Binary Logistic Regression ◽  
Predictor Variables ◽  
Sentence Structure ◽  
Criterion Variable ◽  
Data Set ◽  
Part Of Speech ◽  
History Of ◽  
The Subject

In order to examine whether Arabic has Heavy Noun Phrase Shifting (HNPS), I have extracted from the Prague Arabic Dependency Treebank a data set in which a verb governs either an object NP and an Adjunct Phrase (PP or AdvP) or a subject NP and an Adjunct Phrase. I have used binary logistic regression where the criterion variable is whether the subject/object NP shifts, and used as predictor variables heaviness (the number of tokens per NP, adjunct), part of speech tag, verb disposition (ie. whether the verb has a history of taking double objects or sentential objects), NP number, NP definiteness, and the presence of referring pronouns in either the NP or the adjunct. The results show that only object heaviness and adjunct heaviness are useful predictors of object HNPS, while subject heaviness, adjunct heaviness, subject part of speech tag, definiteness, and adjunct head POS tags are active predictors of subject HNPS. I also show that HNPS can in principle be predicted from sentence structure.

scholarly journals PARSING TWITTER MENGGUNAKAN METODE LEFT-CORNER PARSING DENGAN MEMANFAATKAN POS TAGGER

Repositor ◽  
2020 ◽  
Vol 2 (7) ◽  
pp. 897
Author(s):  
Dyah Anitia ◽  
Yuda Munarko ◽  
Yufis Azhar
Keyword(s):  
Noun Phrase ◽  
Word Class ◽  
Sentence Structure ◽  
Top Down ◽  
Data Set ◽  
Bottom Up ◽  
Part Of Speech ◽  
Left Corner ◽  
Pos Tagger ◽  
Bahasa Indonesia

AbstrakPada penelitian ini dilakukan investigasi parser dengan pendekatan left-corner untuk data tweet bahasa Indonesia. Total koleksi tweet sebanyak 850 tweet yang dibagi menjadi tiga kumpulan data, yakni data train POS Tagger, data train dan data uji. Left-corner menggabungkan dua metode yakni top-down dan bottom-up. Dimana top-down digunakan pada proses pengenalan kelas kata dan bottom-up digunakan pada proses pengenalan struktur kalimat. Adapun jenis tag yang digunakan dalam proses top-down berjumlah 23 tagset dan frasa  yang digunakan untuk menentukan struktur kalimat frasa yakni frasa nomina, frasa verbal, frasa adjektiva, frasa adverbia dan frasa preposisional. Hasilnya adalah untuk pendekatan left corner mencapai nilai precision 88,29%, nilai recall 68,3% dan F1 measure 77,02%. Nilai yang diperoleh dengan pendekatan left-corner lebih besar dibandingkan nilai dengan pendekatan bottom-up. Hasil dari nilai yang diperoleh dengan bottom up mencapai nilai precision 68,79%, nilai recall 47,12% dan F1 measure 55,9%. Hal ini disebabkan penggunaan kelas kata pada proses top-down berpengaruh pada sturuktur kalimat pada proses bottom up.AbstractIn this research, we investigated parser with left-corner parser approach for data tweet in Indonesian language. The data used was consisted of 850 tweets which divided for into three data set, that is data train for POS Tagger, data train for parser and data test. The left-corner combines two methods, top-down and bottom-up methods. Top-down  used for processes a sequence of words, and attaches a part of speech tag to each and bottom-up used for processes a sentence structure. We used 41 tags and the pharse used to define the sentence structure is noun phrase, verbal phrase, adjective pharse, adverd phrase and prepositional pharse. The result was that precision 88,29%,  recall 68,3% and F1 measure 77,02% of left-corner approach. The value obtained by the left-corner approach is greater than the value with the bottom-up approach. The result was that precision 68,29%,  recall 47,12% and F1 measure 55,9% of bottom-up approach. This is because the use of word class in top-down process affect the sentence structure in the bottom up process. that is because the use of word class in top-down process affect the sentence structure in the bottom up process.

scholarly journals Assessment of pain and postoperative nausea and vomiting and their association in the early postoperative period: an observational study from Palestine

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Reem M. Elsaid ◽  
Ashraqat S. Namrouti ◽  
Ahmad M. Samara ◽  
Wael Sadaqa ◽  
Sa’ed H. Zyoud
Keyword(s):  
Logistic Regression ◽  
Observational Study ◽  
Postoperative Nausea ◽  
Postoperative Nausea And Vomiting ◽  
Binary Logistic Regression ◽  
Nausea And Vomiting ◽  
Prior History ◽  
Factors Associated ◽  
Duration Of Surgery ◽  
History Of

Abstract Background Postoperative nausea and vomiting (PONV) and postoperative pain (POP) are most commonly experienced in the early hours after surgery. Many studies have reported high rates of PONV and POP, and have identified factors that could predict the development of these complications. This study aimed to evaluate the relationship between PONV and POP, and to identify some factors associated with these symptoms. Methods This was a prospective, multicentre, observational study performed at An-Najah National University Hospital and Rafidia Governmental Hospital, the major surgical hospitals in northern Palestine, from October 2019 to February 2020. A data collection form, adapted from multiple previous studies, was used to evaluate factors associated with PONV and POP in patients undergoing elective surgery. Patients were interviewed during the first 24 h following surgery. Multiple binary logistic regression was applied to determine factors that were significantly associated with the occurrence of PONV. Results Of the 211 patients included, nausea occurred in 43.1%, vomiting in 17.5%, and PONV in 45.5%. Multiple binary logistic regression analysis, using PONV as a dependent variable, showed that only patients with a history of PONV [odds ratio (OR) = 2.28; 95% confidence interval (CI) = 1.03–5.01; p = 0.041] and POP (OR = 2.41; 95% CI = 1.17–4.97; p = 0.018) were significantly associated with the occurrence of PONV. Most participants (74.4%) reported experiencing pain at some point during the first 24 h following surgery. Additionally, the type and duration of surgery were significantly associated with POP (p-values were 0.002 and 0.006, respectively). Conclusions PONV and POP are common complications in our surgical patients. Factors associated with PONV include a prior history of PONV and POP. Patients at risk should be identified, the proper formulation of PONV protocols should be considered, and appropriate management plans should be implemented to improve patients’ outcomes.

scholarly journals Study of Risk Factors Associated with Suicide Attempt in Patients with Bipolar Disorder Type I

2020 ◽  
Vol 11 (02) ◽  
pp. 291-298
Author(s):  
Karthick Subramanian ◽  
Vikas Menon ◽  
Siddharth Sarkar ◽  
Vigneshvar Chandrasekaran ◽  
Nivedhitha Selvakumar
Keyword(s):  
Risk Factors ◽  
Bipolar Disorder ◽  
Logistic Regression ◽  
Family History ◽  
Suicide Attempt ◽  
Coping Strategies ◽  
Binary Logistic Regression ◽  
Type I ◽  
Factors Associated ◽  
History Of

Abstract Background Suicide is the leading contributor to mortality in bipolar disorder (BD). A history of suicidal attempt is a robust predictive marker for future suicide attempts. Personality profiles and coping strategies are the areas of contemporary research in bipolar suicides apart from clinical and demographic risk factors. However, similar research in developing countries is rarer. Objectives The present study aimed to identify the risk factors associated with suicidal attempts in BD type I (BD-I). Materials and Methods Patients with BD-I currently in clinical remission (N = 102) were recruited. Sociodemographic details and the clinical data were collected using a semistructured pro forma. The psychiatric diagnoses were confirmed using the Mini-International Neuropsychiatric Interview 5.0. The National Institute of Mental Health–Life Chart Methodology Clinician Retrospective Chart was used to chart the illness course. Presumptive Stressful Life Events Scale, Coping Strategies Inventory Short Form, Buss–Perry aggression questionnaire, Past Feelings and Acts of Violence, and Barratt Impulsivity scale were used to assess the patient’s stress scores, coping skills, aggression, violence, and impulsivity, respectively. Statistical Analysis Descriptive statistics were used for demographic details and characteristics of the illness course. Binary logistic regression analyses were performed to identify the predictors for lifetime suicide attempt in BD-I. Results A total of 102 patients (males = 49 and females = 53) with BD-I were included. Thirty-seven subjects (36.3%) had a history of suicide attempt. The illness course in suicide attempters more frequently had an index episode of depression, was encumbered with frequent mood episodes, especially in depression, and had a higher propensity for psychiatric comorbidities. On binary logistic regression analysis, the odds ratios (ORs) for predicting a suicide attempt were highest for positive family history of suicide (OR: 13.65, 95% confidence interval [CI]: 1.28–145.38, p = 0.030), followed by the presence of an index depressive episode (OR: 6.88, 95% CI: 1.70–27.91, p = 0.007), and lower scores on problem-focused disengagement (OR: 0.72, 95% CI: 0.56–0.92, p = 0.009). Conclusion BD-I patients with lifetime suicide attempt differ from non-attempters on various course-related and temperamental factors. However, an index episode depression, family history of suicide, and lower problem-focused engagement can predict lifetime suicide attempt in patients with BD-I.

Defining the Growth/No-Growth Interface for Listeria monocytogenes in Mexican-Style Cheese Based on Salt, pH, and Moisture Content

1999 ◽  
Vol 62 (6) ◽  
pp. 601-609 ◽  
Author(s):  
LANCE F. BOLTON ◽  
JOSEPH F. FRANK
Keyword(s):  
Logistic Regression ◽  
Listeria Monocytogenes ◽  
Binary Logistic Regression ◽  
Ordinal Logistic Regression ◽  
Growth Interface ◽  
Data Set ◽  
Logistic Regression Models ◽  
Binary Model ◽  
Fresh Cheese ◽  
Brine Concentration

The objective of this study was to define combinations of pH, salt, and moisture that produce growth, stasis, or inactivation of Listeria monocytogenes in Mexican-style cheese. A soft, directly acidified, rennet-coagulated, fresh cheese similar to Mexican-style cheese was produced. The cheese was subsequently altered in composition as required by the experimental protocol. A factorial design with four moisture contents (42, 50, 55, and 60%), four salt concentrations (2.0, 4.0, 6.0, and 8.0% wt/wt), six pH levels (5.0, 5.25, 5.50, 5.75, 6.0, and 6.5), and three replications was used. Observations of growth, stasis, or death were obtained for each combination after 21 and 42 days of incubation at 10°C. Binary logistic regression was used to develop an equation to determine the probability of growth or no growth for any combination within the range of the data set. In addition, ordinal logistic regression was used to calculate proportional odds ratios for growth, stasis, and death for each treatment combination. Ordinal logistic regression was also used to develop equations to determine the probability of growth, stasis, and death for formulations within the range of the data set. Models were validated with independently produced data. Of 60 samples formulated to have a 5% probability of Listeria growth (pH, 5.0 to 6.0; brine concentration, 8.17 to 16.00%), none supported growth. Of 30 samples formulated to have 50% probability of growth using the binary model (pH, 5.50 to 6.50; brine concentration, 3.23 to 12.50%), 20 supported growth. Of 30 samples formulated to have a 50% probability of growth according to the ordinal model (pH, 5.50 to 6.50; brine concentration, 3.37 to 10.90%), 16 supported growth. These data indicate that the logistic regression models presented accurately predict the behavior of L. monocytogenes in Mexican-style cheese.

Clinical Factors Associated with the Diagnosis of Granulomatosis with Polyangiitis

2017 ◽  
Vol 156 (3) ◽  
pp. 484-488 ◽  
Author(s):  
Erdem Eren ◽  
Toygar Kalkan ◽  
Seçil Arslanoğlu ◽  
Mustafa Özmen ◽  
Kazım Önal ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Nasal Mucosa ◽  
Odds Ratio ◽  
Granulomatosis With Polyangiitis ◽  
Univariate Analysis ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Significant Difference ◽  
History Of

Objective To determine the predictive value of nasal endoscopic findings and symptoms in the diagnosis of granulomatosis with polyangiitis (GPA). Study Design A cross-sectional study. Setting A tertiary university hospital. Subjects and Methods A total of 116 adults were enrolled in the study: 19 patients with GPA, 29 patients with other rheumatic diseases, and 68 healthy volunteers. All patients were examined with a flexible endoscope, and nasal endoscopic images were recorded and evaluated blindly. The medical history of each patient was taken by a physician blinded to the patient’s diagnosis. Results Univariate analysis indicated a statistically significant difference in rhinorrhea ( P = .002), postnasal drip ( P = .015), epistaxis ( P < .001), and saddle nose ( P = .017). However, binary logistic regression analysis demonstrated that only history of epistaxis ( P = .012; odds ratio, 5.6) was statistically significant in predicting GPA. Univariate analysis showed a statistically significant difference in nasal secretion ( P = .028), nasal septal perforation ( P < .017), nasal crusting ( P < .001), nasal adhesion ( P < .001), nasal granuloma ( P = .017), and hemorrhagic fragile nasal mucosa ( P < .001). A binary logistic regression analysis demonstrated that only hemorrhagic fragile nasal mucosa ( P < .001; odds ratio, 52.9) was a statistically significant predictor of GPA. Conclusions Given the results of this study, we believe that hemorrhagic fragile nasal mucosa and history of recurrent epistaxis may put patients at risk for GPA and should be investigated accordingly.

Resource-based view of innovation activity in SMEs: an empirical analysis based on the global competitiveness project

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lívia Lukovszki ◽  
András Rideg ◽  
Norbert Sipos
Keyword(s):  
Logistic Regression ◽  
Literature Review ◽  
Empirical Analysis ◽  
Binary Logistic Regression ◽  
Point Of View ◽  
Innovation Activity ◽  
Data Set ◽  
Content Type ◽  
Resources And Capabilities ◽  
The Impact

Purpose The purpose of this study is to identify the corporate functions that contribute most to the innovation success of SMEs with limited resources. After a systematic literature review, the authors used a unique primary data set of 784 SMEs from eight countries. Descriptive statistics and binary logistic regression were used to show the data set peculiarities. The logistic regression targeted the presence of innovative products and services in sales by 11 dummy variables and 4 principal factors describing SMEs’ different resources and capabilities. Design/methodology/approach The authors developed a resource-based product innovation model that is synthesising the impact of the company resources and capabilities and of the innovation activity of the company on the actual innovation performance. The authors carry out an empirical analysis of the characteristic features of innovation activity in an international sample of SMEs. Findings The results show that two corporate functions play a crucial role in the effectiveness of innovation for SMEs as follows: management and research and development (R&D). In addition, although of lesser importance, the effect of the marketing function also appears significant. The binary logistic regression had 84.2% of explanatory power. Originality/value From a scientific point of view, the SME-focussed, complex and synthesising RBV model of innovation construction and literature review can be used as a reference point for future researches. From a practical point of view, the analysis is useful for those SMEs, which want to gain a competitive advantage through innovation. Indeed, the results show that in the case of SMEs, a company wishing to innovate must invest in three corporate functions for innovation to be effective as follows: management, R&D and marketing.

scholarly journals AFP and CA-125 as an accurate risk factor to predict eye metastasis in hypertension patients with liver Carcinoma： A STROBE-compliant article

2021 ◽  
Author(s):  
Jing Tang ◽  
Li-Juan Zhang ◽  
Yan-Cheng Huang ◽  
Rong Huang ◽  
Hui-Ye Shu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Liver Cancer ◽  
Cancer Metastasis ◽  
Binary Logistic Regression ◽  
Receiver Operating Curve ◽  
Ca 125 ◽  
Liver Carcinoma ◽  
Binary Logistic Regression Analysis ◽  
History Of ◽  
Ocular Metastasis

Abstract Purpose Primary liver carcinoma is a common malignant tumor. In this study, we analyzed the differences between hypertension patients with ocular metastasis of liver cancer and those with metastases to other sites, the correlation between history of HBV and liver cancer metastasis, and independent risk factors for ocular metastasis. METHODS We used treatment records from 488 patients with metastases of primary liver cancer from August 2001 to May 2015, divided into two groups based on metastatic sites: OM (ocular metastasis) and NOM (non-ocular, other sites of metastasis) groups. The Student’s t-test and Chi-square test were used to assess the significance of differences between the groups and define the relationship between history of HBV and ocular metastasis of liver cancer. Binary logistic regression analysis was used to identify indicators of ocular metastasis of liver cancer and receiver operating curve (ROC) analyses to estimate their diagnostic value. RESULTS No significant differences in sex, age, tumor stage, pathological type, or treatment were identified between the OM and NOM groups, while the prevalence of HBV was higher in the former than that in latter, confirming the association between history of HBV and ocular metastasis. Binary logistic regression demonstrated that AFP and CA-125 were independent indicators of liver metastasis (both P < 0.001). ROC curve analyses generated cut-off values for AFP and CA-125 of 957.2 ng/ml and 114.25 U/ml, respectively, with corresponding AUC values of 0.739 and 0.810. The specificity of the combination of AFP and CA-125 was higher than either factor separately.

scholarly journals Proportion and Associated Factors of Low Vision among Adult Patients Attending at University of Gondar Tertiary Eye Care and Training Center, Gondar Town, Ethiopia

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Melkamu Temeselew Tegegn ◽  
Gizachew Tilahun Belete ◽  
Ayanaw Tsega Ferede ◽  
Aragaw Kegne Assaye
Keyword(s):  
Logistic Regression ◽  
Cataract Surgery ◽  
Associated Factors ◽  
Low Vision ◽  
Binary Logistic Regression ◽  
Training Center ◽  
Eye Care ◽  
History Of ◽  
Study Participants ◽  
And Training

Introduction. Low vision is a worldwide health problem in both developing and developed countries. A national survey of low vision and blindness in Ethiopia showed that the prevalence of low vision was 3.7% and that of blindness was 1.6%, whereas there is no evidence in the study area. Purpose. The study was aimed to assess the proportion and associated factors of low vision at the University of Gondar tertiary eye care and training center. Methods. A hospital-based cross-sectional study was conducted on 727 study participants with a systematic random sampling technique from April 18 to May 16, 2019. Data were collected through the use of a structural questionnaire and physical eye examination. Data were entered into Epi Info version 7, and analysis was performed by using statistical package for social science (SPSS) version 20. The binary logistic regression model was fitted to identify factors associated with low vision, and variables with a P value of <0.05 in a multivariable binary logistic regression were considered as statistically significant. Results. A total of 715 study participants have participated in this study with a mean age of 49.39 ± 19.93 years. The prevalence of low vision was 35.7% (95% CI: 32.3, 39.3). Being female (AOR = 1.58; 95% CI: 1.10, 2.28), no formal educational level (AOR = 2.24; 95% CI: 1.25, 4.02), history of cataract surgery (AOR = 2.58; 95% CI: 1.53, 4.36), and age ≥ 70 years (AOR: 3.96; 95% CI: 2.21, 7.10) were significantly associated with low vision. Conclusion and Recommendation. The prevalence of low vision found in this study was high as compared with the national and global magnitude. Older age, being female, previous history of cataract surgery, and no formal education were independently and significantly associated with low vision. Cataract and uncorrected refractive errors were identified as the main causes of low vision. Therefore, it requires a plan to provide an eye care education to the community, increasing the quality of cataract surgery and refractive service for the community in the catchment area.

BINARY LOGISTIC REGRESSION AND PHM ANALYSIS FOR RELIABILITY DATA

2014 ◽  
Vol 21 (05) ◽  
pp. 1450023 ◽  
Author(s):  
ALEXANDRE C. MENDES ◽  
NASSER FARD
Keyword(s):  
Logistic Regression ◽  
Survival Data ◽  
Recurrent Events ◽  
Binary Logistic Regression ◽  
Time Dependent ◽  
Time Interval ◽  
Data Set ◽  
Accelerated Life ◽  
Address Data ◽  
Time Dependent Covariates

This study proposes a modification for the binary logistic regression to treat time-dependent covariates for reliability studies. The proportional hazard model (PHM) properties are well suited for modeling survival data when there are categorical predictors; as it compares hazards to a reference category. However, time-dependent covariates present a challenge for the analysis as stratification does not produce hazards for the covariate stratified or creation of dummy time-dependent covariates faces difficulty on selecting the time interval for the interaction and the coefficient results may be difficult to interpret. The findings show that the logistic regression can provide equal or better results than the PHM applied for reliability analysis when time-dependent covariate is evaluated. The PHM is potentially preferred to address data set without time-dependent variables as it does not require any data manipulation. The logistic regression ignores the information on timing of the events; which is corrected by breaking each subject survival history into a set of discrete time intervals that are treated as distinct observations evaluated as a binary distribution. Recurrent events can be addressed by both methods with proper correction for lack of heterogeneity. The application of the modified logistic regression model for the study of reliability is innovative and with readily potential application for step-stress time-dependent accelerated life testing.

scholarly journals Covid-19 Patient Mortality Risk Classification Using Bayesian Binary Logistic Regression

2021 ◽  
Vol 18 (1) ◽  
pp. 150-160
Author(s):  
Muhammad Qolbi Shobri ◽  
Ferra Yanuar ◽  
Dodi Devianto
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Mortality Rate ◽  
Bayesian Method ◽  
Binary Logistic Regression ◽  
Predictor Variables ◽  
New Disease ◽  
Death Risk ◽  
Risk Of Death ◽  
The World

At the end of 2019 the world was shocked by a new disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). The disease is called Covid-19 (Coronavirus Disease). The mortality rate due to disease is increasing every day. In Indonesia as of April 2021, confirmed Covid-19 patients who died reached 42,530 patients, seeing the high mortality rate of Covid-19 patients so it needs to be studied further so that the risk of death of these Covid-19 patients can be minimized. This research utilizing  binary logistic regression with Bayesian method parameter estimation. In this study, the predictor variables used were in the form of categories that each category in the predictor variables was assumed to have the same risk of death risk of Covid-19 patients. The results of this study indicate that the number of comorbids has a significant effect on the risk of death of Covid-19 patients, the more the number of comorbids suffered by the patient, the higher the risk of death of the patient. The accuracy of this method in classifying data is 84.68%.

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