Text Words Research Articles

The Role of Text Structure in Reading Comprehension

Lack of understanding of the text structure is the major problem in English reading teaching in junior high school and senior high school at present. High school English teaching is not only for the college entrance examination, but also to cultivate students' ability of listening, speaking, reading and writing. Because traditional teaching focuses on the interpretation and grammar of words. Teachers help students understand the text by explaining the text word for word, combing the language points and grammatical phenomena of the text, and practicing the words and phrases repeatedly, traditional teaching focuses on the explanation of words and syntax, there is lack of macro-processing of chapters in reading teaching, which restricts students’ reading comprehension level. Text structure, as an important internal feature of the text, plays a positive role in improving the correctness and efficiency of reading comprehension. Adopting correct reading teaching strategies and imparting text structure knowledge can greatly improve the efficiency of reading teaching in the long run.

Investment strategy via analyst report text mining

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

Multilabel Text Classification with Label-Dependent Representation

Assigning predefined classes to natural language texts, based on their content, is a necessary component in many tasks in organizations. This task is carried out by classifying documents within a set of predefined categories using models and computational methods. Text representation for classification purposes has traditionally been performed using a vector space model due to its good performance and simplicity. Moreover, the classification of texts via multilabeling has typically been approached by using simple label classification methods, which require the transformation of the problem studied to apply binary techniques, or by adapting binary algorithms. Over the previous decade, text classification has been extended using deep learning models. Compared to traditional machine learning methods, deep learning avoids rule design and feature selection by humans, and automatically provides semantically meaningful representations for text analysis. However, deep learning-based text classification is data-intensive and computationally complex. Interest in deep learning models does not rule out techniques and models based on shallow learning. This situation is true when the set of training cases is smaller, and when the set of features is small. White box approaches have advantages over black box approaches, where the feasibility of working with relatively small sets of data and the interpretability of the results stand out. This research evaluates a weighting function of the words in texts to modify the representation of the texts during multilabel classification, using a combination of two approaches: problem transformation and model adaptation. This weighting function was tested in 10 referential textual data sets, and compared with alternative techniques based on three performance measures: Hamming Loss, Accuracy, and macro-F1. The best improvement occurs on the macro-F1 when the data sets have fewer labels, fewer documents, and smaller vocabulary sizes. In addition, the performance improves in data sets with higher cardinality, density, and diversity of labels. This proves the usefulness of the function on smaller data sets. The results show improvements of more than 10% in terms of macro-F1 in classifiers based on our method in almost all of the cases analyzed.

The density of the rich meaning and the ease of formulations in the words of wisdom texts of Imam al-Baqir (peace be upon him) The knowledge words of wisdom as a model

The critics evaluated the speeches of the preachers and indicated their weaknesses and strengths based on an important critical criterion, which is that “the best speech is that which is too little to dispense you with a lot.” Accordingly, this research intends to study the words of wisdom texts on the concept of knowledge of Imam al-Baqir Muhammad ibn Ali (pbuh), to show the rich meanings and easy verbal formulation in them, through the relationship of grammatical functions and linking them to semantic functions in examples of them.The importance of the research is represented by showing the outstanding knowledge and cognitive effort of the imam in his wisdom related to knowledge, due to his status (pbuh), as the founder of the knowledge school, in promoting the culture of knowledge effort in his era.The method in this research is descriptive and analytical. It relies on the tools of deduction, interpretation and accuracy to clarify the intended meaning of the grammatical significance that corresponds to those words and the grammatical formulation according to the opinions of grammarians.The most important findings of the research are that the Qur’anic vision is clear in the methods of ruling the Imam (pbuh) and its meanings, and that he (pbuh) chose uncomplicated linguistic methods that are known to the general public, and he used more than the use of short phrases and the use of the imperative and conditional methods to urge learning and abundance Questioning and relying on the principle of cause and effect, which is consistent with urging science and taking its causes.

Deep Learning in Computational Linguistics for Chinese Language Translation

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

Predicting life satisfaction based on the emotion words in self-statement texts.

Measuring people's life satisfaction in real time on a large scale is quite valuable for monitoring and promoting public mental health; however, the traditional questionnaire method cannot fully meet this need. This study utilized the emotion words in self-statement texts to train machine learning predictive models to identify an individual's life satisfaction. The SVR model was found to have the best performance, with the correlation between predicted scores and self-reported questionnaire scores achieving 0.42 and the split-half reliability achieving 0.939. This result demonstrates the possibility of identifying life satisfaction through emotional expressions and provides a method to measure the public's life satisfaction online. The word categories selected through the modeling process were happy (PA), sorrow (NB), boredom (NE), reproach (NN), glad (MH), aversion (ME), and N (negation + positive), which reveal the specific emotions in self-expression relevant to life satisfaction.

Comparison of mifepristone plus misoprostol with misoprostol alone for first trimester medical abortion: A systematic review and meta-analysis.

To compare mifepristone plus a misoprostol-combined regimen with misoprostol alone in the medical abortion of first trimester pregnancy. An internet-based search of available literature was performed using text words contained in titles and abstracts. PubMed/Medline, Cochrane CENTRAL, EMBASE, and Google scholar were used to locate English-based articles published until December 2021. Studies fulfilling the inclusion criteria were selected, appraised, and assessed for methodological quality. The included studies were pooled for meta-analysis, and the results were presented in risk ratio at a 95% confidence interval. Nine studies comprising 2,052 participants (1,035 intervention and 1,017 controls) were considered. Primary endpoints were complete expulsion, incomplete expulsion, missed abortion, and ongoing pregnancy. The intervention was found to more likely induce complete expulsion irrespective of gestational age (RR: 1.19; 95% CI: 1.14-1.25). The administration of misoprostol 800 mcg after 24 h of mifepristone pre-treatment in the intervention group more likely induced complete expulsion (RR: 1.23; 95% CI: 1.17-1.30) than after 48 h. The intervention group was also more likely to experience complete expulsion when misoprostol was used either vaginally (RR: 1.16; 95% CI: 1.09-1.17) or buccally (RR: 1.23; 95% CI: 1.16-1.30). The intervention was more effective in the subgroup with a negative foetal heartbeat at reducing incomplete abortion (RR: 0.45; 95% CI: 0.26-0.78) compared with the control group. The intervention more likely reduced both missed abortion (RR: 0.21; 95% CI: 0.08-0.91) and ongoing pregnancy (RR: 0.12; 95% CI: 0.05-0.26). Fever was less likely to be reported (RR: 0.78; 95% CI: 0.12-0.89), whereas the subjective experience of bleeding was more likely to be encountered (RR: 1.31; 95% CI: 1.13-1.53) by the intervention group. The review strengthened the theory that a combined mifepristone and misoprostol regimen can be an effective medical management for inducing abortions during first trimester pregnancy in all contexts. Specifically, there is a high-level certainty of evidence on complete expulsion during the early stage and its ability to reduce both missed and ongoing pregnancies. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019134213, identifier CRD42019134213.

Experience of loneliness on well-being among young individuals: A systematic scoping review

A systematic scoping review was conducted to explore the current evidence on the experience of loneliness influencing well-being among youths. The electronic databases Scopus, APA PsycINFO, Emerald Insight and One Search were used to identify relevant studies, followed by an analysis of the text words contained in the title and abstract, and of the index terms used to describe the article. Reference lists of all shortlisted articles were searched for additional studies. 20 studies (quantitative, qualitative and mixed) published in the English language were identified for inclusion. Findings illustrate that the experience of loneliness is a complex, evolutionary process influenced by relational and environmental factors. Results from the studies identified factors that promote lower experience of loneliness and better well-being in future life stages. Future research can substantiate the issues related to young individuals being socially isolated from others for a prolonged duration.

Insulin Management for Type 2 Diabetes During Ramadan: A Narrative Review for Clinicians.

Muslims with insulin-dependent type 2 diabetes are at high risk for adverse events while fasting during the month of Ramadan. However, advances in pharmacologic therapy coupled with creative strategies of insulin administration can mitigate complications. This narrative literature review investigates which insulin subtypes are likely to prevent hypoglycemic events and reduce hyperglycemia during the Ramadan fasting season for this high-risk population. Narrative literature review Eligibility Criteria: The following MeSH terms were used: "Diabetes Mellitus, Type 2" and "Insulin," and the "Text Words": "Ramadan", "iftar", "Muslim fast", and "religious fast." The primary focus was on adult, non-pregnant, insulin-dependent type 2 diabetes during Ramadan. Anything beyond this focus was excluded. A total of nine pertinent studies were included for narrative review and analysis. PubMed, EMBASE and Medline. The studies identified suggest long-acting insulins reduce the risk of hypoglycemia, and rapid-acting insulin analogues may improve post-iftar hyperglycemia. Moreover, utilizing flexible glycemic targets during Ramadan is a novel strategy that has demonstrated improved outcomes after the fasting season. Certain insulin subtypes and dosing strategies may be advantageous to use during Ramadan. However, a systematic, comprehensive, and updated review, including a critical appraisal of each original study, is needed to improve clinical care of insulin-dependent type 2 diabetes during Ramadan.

Mirar a los mirones. Adaptation of Los mirones (1617) attributed to Miguel de Cervantes. Adaptation, Carlota Gaviño

<i>Mirar a los mirones</i>. Adaptation of <i>Los mirones</i> (1617) attributed to Miguel de Cervantes. Adaptation, Carlota Gaviño

Penggunaan Kalimat dalam Teks Eksposisi Karya Siswa Kelas X SMA Negeri 1 Nan Sabaris Kabupaten Padang Pariaman

This study aims to describe the use of sentences in exposition texts in terms of structural clarity, logical meaning, frugality of words and standardization of words in exposition texts by class X students of SMA Negeri 1 Nan Sabaris, Padang Pariaman Regency. This type of research is qualitative research using descriptive methods. The data to be obtained are words and sentences obtained from research data sources. The source of the data used in this study was exposition texts by class X students of SMA Negeri 1 Nan Sabaris, Padang Pariaman Regency, for the 2021/2022 academic year with a total of 30 student exposition texts. The data collection technique in this study is the fishing technique. The instrument in this study was the researcher himself and assisted by an inventory table. Based on the results of the study, it can be concluded that First, in terms of structural clarity consisting of clarity of active and passive structures, 47 non-standard sentences were found, 18 non-standard adverbial subjects were found, five non-standard predicates were found, non-subject adverbs were found. five non-standard sentences, and the subject is not missing found 16 non-standard sentences. Second, in terms of logical meaning, it consists of a logical relationship between the meanings of S and P found in 16 non-standard sentences and a logical relationship of detailed (parallel) meanings found in 13 non-standard sentences. Third, from the point of view of saving words consisting of using one subject from the same subject, three non-standard sentences were found, using one word from several synonymous words, 90 non-standard sentences were found, and using the words needed to express the author's intent, 125 non-standard sentences were found. . Fourth, in terms of word standardity, 32 non-standard words were found. the results obtained prove that the use of sentences in expository texts for class X students of SMA Negeri 1 Nan Sabaris Padang Pariaman Regency is still low.

The impact of symmetry violations on determining optimal control trajectories for complex robotic systems

Complex robotic systems, their properties, features and interactions are considered, the article offers the definition of a complex system on the basis of 5 properties: openness, non-morphic variability of three types (structural, spatial and information), Double-code, event aggregation and violation of physical symmetries. The influence of violations of physical symmetries on the determination of optimal control trajectories is reflected in the paradigm approach to the study of complex robotic systems that are not formalized as mathematical objects. The basic concepts, postulates and hypotheses are formulated. Ideal designs of variability of complex systems; energy causal sets; energies; events, causes, effects and evolution; spacetime, quanta and vacuum; interaction of individuals; operators of physical interactions, aggregated events, text and attachments of words. Three basic models for the study of complex systems - the model of physical interactions, the neurolinguistic model and the model of control at incomplete compatibility are proposed and briefly described. The structure of the kernel of the platform of physical simulation modeling for the researches of complex systems is resulted. Three types of space-time quantum modeling - minimal, semantic and evolutionary - are described. The article provides an illustration of results of application of the proposed approach to the study of actions of complex systems. It is noted that the resulting mathematical structure exhibits fractal properties. Typical trajectories of evolution - «homeostat»; «attenuation of actions»; «invariant»; «accident»; «window of possibilities» have been allocated. The article provides a number of principles of research of complex methodological and methodical systems. Recommendations were made on the scope of the proposed approach.

Detection of Generative Linguistic Steganography Based on Explicit and Latent Text Word Relation Mining Using Deep Learning

Covert communication channels can be easily constructed using text steganography based on social media. Offenders can easily utilize these channels to engage in various criminal activities, which brings great challenges in maintaining the security of cyberspace. Among the text information hiding methods, generative linguistic steganography poses the biggest threat to network security because it does not need the original carrier and has high embedding efficiency. The existing generative linguistic steganalysis methods fail to deeply mine text word relation, hence the detection performance is relatively unsatisfactory. In this paper, we prove that there is explicit and latent steganography-sensitive text word relation. Based on this, we propose a generative linguistic steganalysis method based on Explicit and Latent text word relation Mining, named ELM. Firstly, we employ a distributed readin module to convert words into real number vectors. Then, MRA (Mining Relation by Attentions) is proposed to mine the explicit and latent text word relation. Finally, global adaptive classification module is presented to exploit the mined relation feature to predict whether secret information is embedded in the current text segment. Experimental results demonstrate that the detection performance of ELM is better than the existing generative linguistic steganalysis methods.

Some CNS sarcomas seen: A 22-year series.

Central nervous system (CNS) and spine are seldom impacted by primary or metastatic sarcomas. We reviewed our 22-year experience with metastatic versus primary mesenchymal sarcomas in adults versus pediatric patients, additionally asking how many might today undergo nomenclature changes using CNS World Health Organization, 5th edition criteria. Case identification via text word search of pathology databases from our adult and pediatric referral hospitals, 2000 to August 2022, with exclusion of peripheral nervous system and primary chondro-osseous and notochordal tumors. Demographic, immunohistochemical, fluorescence in situ hybridization (FISH), and fusion results performed at the time of original diagnosis were acquired from reports. 57 cases were identified, with a 16 : 15 primary and 19 : 7 metastatic ratio in adult versus pediatric patients. Ewing sarcoma was the most frequent type (n = 18, 7 adult, 11 pediatric), with a rare primary PEComa, 2 alveolar soft part sarcomas, and metastatic angiosarcoma in the cohort. Only 3 cases, an intracranial sarcoma, DICER-1 mutant formerly diagnosed as rhabdomyosarcoma, an intracranial mesenchymal tumor, FET::CREB fusion-positive formerly diagnosed as angiomatoid fibrous histiocytoma, and a CIC-rearranged sarcoma required nomenclature updating by CNS WHO5 criteria. Few primary or metastatic, adult or pediatric, CNS/spinal sarcomas required nomenclature updates; almost all had been satisfactorily classified at the time of diagnosis, using immunohistochemistry, FISH, or fusion results.

Analyzing the Extent to which Gender Bias Exists in News Articles Using Natural Language Processing Techniques

Prior studies have shown the existence of gender bias in job postings, performance reviews, and letters of recommendation. However, very little research has been done on the presence of gender biases in mainstream news sources and how they vary across publications. Human editing, given the rapid pace of news dissemination, is not effective enough to address biases. Even computer programs that parse the news articles for specific words and references still fall short of identifying and detecting the undertones and implicit references, which is why sophisticated techniques like Artificial Intelligence (AI) are necessary. In this study, I used Natural Language Processing (NLP) methods, a series of Python-programs to further analyze how biases vary in new information, along the metrics of type, variety, and intensity. I used over 500,000 news articles from 15 publications, spanning over 4 years to build and train my algorithm. Using Word2Vec, a popular NLP method, I was able to conclude that more right leaning publications are more likely to exhibit misogynistic content that is biased against women. However, the method fell short of identifying many forms of objectification like Benevolent Sexism. Similarly, using VADER, a python-code of sentiment analysis tool, I was able to determine that mere metrics of positive, negative, and neutral sentiment are not sufficient to detect occurrences of gender bias. To gauge the breadth of sexist language effectively, I used the LIWC text analysis program which calculates the percentage of words in a given text that fall into one or more of over 80 linguistic, psychological and topical categories indicating various social, cognitive, and affective processes. As a result, with statistical evidence my study was able to conclude the presence of implicit gender bias occurs all across publications but is more prevalent in right-leaning publications.

Big Data Analysis and Forecast of Employment Position Requirements for College Students

With the help of natural language processing and machine learning, we can analyze the information of online recruitment text posted by employer companies and dig the requirement features of these employment positions, and this is a meaningful work with practical value. However, existing methods for analyzing online recruitment information are too simple to withstand the mass data on the Internet, so this paper aims to study the analysis and forecast of employment position requirements for college students based on big data analysis. At first, the recruitment information of companies is preprocessed, keywords in the recruitment text are extracted by the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm in the Python Chinese word segmentation toolkit, words in the recruitment text are segmented using the open source tool Word2Vec, and the uncounted words in the recruitment text are identified based on the Conditional Random Field (CRF) model. Then, this paper compares the occurrence probability of skills learnt by college students in a certain company employment position with the occurrence probability of the skills in all employment positions on the website, so as to find out the core skills learnt by college students that can match with the job positions required by companies. At last, this paper builds a XGBoost model to forecast the employment position requirements for college students, and verifies the validity of the model using experimental results.

Effect of receiving mobile text messages on cortisol concentrations in students at the University of the Free State.

Texting has become central to social life, with adverse effects on physiological functioning. Research into the impact of texting on cortisol secretion is limited. Thus study aimed to determine how receiving mobile text messages affected salivary cortisol concentrations and investigate the moderating effects of stress, anxiety and depression on cortisol secretion. Undergraduate physiology students attending physiology lectures at the Faculty of Health Sciences, University of the Free State, 2016. An experimental, crossover, quantitative design was used. Participants were involved over two consecutive days, receiving mobile text messages (intervention) on one day and acting as their own control on the other. Self-reported data on stress, anxiety, depression and subjective experience of the study, and saliva samples were collected. Text frequency and wording (neutral, positive, negative) were varied among participants. Forty-eight students participated in the study. Salivary cortisol concentrations did not differ significantly between the intervention and control days. High anxiety levels were associated with increased cortisol concentrations. No associations with cortisol concentrations were documented in low to moderate anxiety, stress, depression or how participants experienced the intervention. There were no significant differences between text frequency, text emotion and change in cortisol concentrations on the intervention day. Receiving mobile text messages did not elicit a significant cortisol response in participants. Findings added to the body of knowledge about the effect of texting on student learning by measuring salivary cortisol concentrations in a lecture setting, with investigation into the moderating effects of stress, anxiety, depression and participants' subjective experience.

Tamil Speech Synthesizer App for Android: Text Processing Module Enhancement

Objectives: Designing dynamic computer systems that are effective, efficient, simple, and satisfying to use is becoming extremely important in this age of information and communication technology. Text to Speech or Speech Synthesis is one of the many methods being investigated by researchers to improve Human-Computer Interaction. The goal here is to improve the text processing component of the Tamil voice synthesizer by including a text normalizer and loan word identification that is efficient and reliable. Methods: Text normalization is conducted on unconstrained Tamil text to turn non-standard terms into common words to reduce confusing utterances during intermediate word processing. Loan/Native words in Tamil literature are detected to enhance the Tamil voice synthesizer system’s pronunciation model. Findings: During normalization, non-standard Tamil words are replaced with standard ones to reduce ambiguous utterances during interim processing. A pronunciation model is built to improve the Tamil speech synthesizer system by identifying loan words in Tamil text. A syllable classifier is presented in this study, based on a decision list approach, which can handle various types of non-stationary sounds. Novelty: We also disclose a ’loan/native word classifier’ based on multiple linear regressions that perform well even with small words of three syllables. Such sophisticated text processors are required in today’s dominating Digital, Information-Communication Technology, and Human-Computer Interaction age. Keywords: Mobile Communication Technology; HumanComputer Interaction; Speech Synthesis Affirms; Syllable Classifier; Prerecorded database

Microblog Emotion Analysis Using Improved DBN Under Spark Platform

In order to solve the problems that traditional single-machine methods find it difficult to complete the task of emotion classification quickly, and the time efficiency and scalability are not high; a microblog emotion analysis method using improved deep belief network (DBN) under Spark platform is proposed. First, the Hadoop distributed file system is used to realize the distributed storage of text data, and the preprocessed data and emotion dictionary are converted into word vector representation based on the continuous bag-of-words model. Then, an improved DBN model is constructed by combining the adaptive learning method of DBN with the active learning method, and it is applied to the learning analysis of text word vectors. Finally, the data parallel optimization of the improved DBN model is realized, based on Spark platform to accurately and quickly obtain the emotion types of microblog texts. The experimental analysis of the proposed method based on the microblog text data set shows that the classification accuracy is more than 94%.

Antimicrobial prescription patterns in East Africa: a systematic review

BackgroundAntimicrobial resistance is currently a recognized global health problem stemming from poor antibiotic stewardship by health workers and inappropriate antimicrobial use by patients. Data showing the extent of poor antimicrobial stewardship in low- and middle-income countries are scanty though high incidences of antimicrobial resistance are increasingly reported in many settings across the globe. The objective of the present study was, therefore, to evaluate prescriptions for antimicrobials in East Africa.MethodsA comprehensive literature search strategy that includes text words and medical subject headings was developed and applied to predefined electronic databases. Two authors independently screened the titles and abstracts of the outputs of the literature search. Full texts were then independently reviewed by the first and the second authors. Eligible studies were formally assessed for quality and risk of bias using a scoring tool. Extracted data from included studies were combined in a meta-analysis where appropriate and presented using forest plots and tables or in a narrative text. Where data were available, subgroup analyses were performed.ResultsA total of 4284 articles were retrieved, but only 26 articles were included in the review. The majority of the included studies (30.8%) were retrieved from Ethiopia, followed by Sudan, Kenya, and Tanzania each contributing 19.2% of the included studies. The overall proportion of encounters with antimicrobials reported by the included studies was 57% CI [42–73%]. Ethiopia had an overall patient encounter with antimicrobials of 63% [50–76%] followed by Sudan with an overall encounter with antimicrobials of 62% CI [34–85%]. Included studies from Kenya reported an overall encounter with antimicrobials of 54% CI [15–90%], whereas included studies from Tanzania reported an overall patient encounter with antimicrobials of 40% CI [21–60%].ConclusionPrescription patterns demonstrated in this review significantly deviate from WHO recommendations suggesting inappropriate antimicrobial use in the East African countries. Further studies have to be pursued to generate more information on antimicrobial use in this region.