PREDICTING RATINGS FOR USER REVIEWS AND OPINION MINING ANALYZE FOR PHYSICIANS AND HOSPITALS

ABSTRACTHealth care is taking its turn in the internet now and online health information consumption is also booming. Users have started generating healthcarereports like online doctor reviews open to all. Hence, online health forums are increasingly popular these days since people can gather their requireddata by just sitting at home and select the best doctor by considering the reviews available online. The patients also browse on their concerneddiseases and use the open forum for discussion on the topics. On an average, these online health-care providers are mainly focusing on reviews aboutthe physicians. The feedback provided by patients is considered and we also analyze the sentiments of the patient to estimate the value of the reviews.The rating for the doctors is divided into various categories such as Staff, Knowledge, and Helpfulness. We propose support vector machine and apriorifor the classification of data and use sentiment based rating prediction to analyze doctor's reviews and opinion mining patterns for online patterns.By providing physician ratings in website, it offers the patients to know about the physician and consider the critique and information to make theirdecision.Keywords: Support vector machine, Apriori, Sentiment classification, Opinion mining

Mother Tongue Influence on English Pronunciation: A Case study in College Students

Mother tongue interference can affect students’ English speaking skill especially in pronouncing the words intrinsically. Considering Tamil language is the dominant mother language used by students at College level, the students as EFL learners often use their mother tongue in daily communication and at campus, and College students pronounce English words in a different way. This study investigated the forms of Tamil language interfering students in pronouncing English words, and how they were interfered. This was a descriptive study in the field of English pronunciation elaborated in a qualitative way. Recorded interviews and pronunciation testing were the main techniques of collecting data. Interview guide, validated pronunciation test, and document analysis were the instruments. In conclusion, the interference was positive since Tamil language’s phonemic sounds have some similarities to those of English, so this condition does not potentially change the meanings of English words

Compatibility of phorate and chlorpyriphos with Trichoderma harzianum (Rifai.) applied for integrated disease management in black pepper (Piper nigrum L.)

The compatibility of the biocontrol agent, Trichoderma harzianum utilized for the management of Phytophthora foot rot of black pepper (Piper nigrum), with phorate and chlorpyriphos applied for the management of nematodes and mealybugs respectively, was studied. The experiments were conducted in vitro and in soil at different concentrations for each chemical considering the recommended dose (6-36' ppm ai for phorate and 10-40 ppm ai for chlorpyriphos). The, in vitro studies indicated that phorate at 6-36 ppm did not' affect radial growth and sporulation of T. harzianum, whereas, chlorpyriphos at 10-40 ppm retarded radial growth up to 50% at 24 hand 48 h but not at 72 h, and retarded sporulation. In soil, there was no significant difference in the number of viable colonies of T. harzianum at 1% concentration for phorate concentrations of 1000 and 2000 ppm. However, incorporation of chlorpyriphos into soil resulted in increase in number of viable colonies of T. harzianum. The study indicated that phorate and chlorpyriphos could be safely applied with T. harzianum for the management of Phytophthora foot rot, nematodes and mealybugs on black pepper. &nbsp

Compatibility of phorate and chlorpyriphos with Trichoderma harzianum (Rifai.) applied for integrated disease management in black pepper (Piper nigrum L.)

The compatibility of the biocontrol agent, Trichoderma harzianum utilized for the management of Phytophthora foot rot of black pepper (Piper nigrum), with phorate and chlorpyriphos applied for the management of nematodes and mealybugs respectively, was studied. The experiments were conducted in vitro and in soil at different concentrations for each chemical considering the recommended dose (6-36' ppm ai for phorate and 10-40 ppm ai for chlorpyriphos). The, in vitro studies indicated that phorate at 6-36 ppm did not' affect radial growth and sporulation of T. harzianum, whereas, chlorpyriphos at 10-40 ppm retarded radial growth up to 50% at 24 hand 48 h but not at 72 h, and retarded sporulation. In soil, there was no significant difference in the number of viable colonies of T. harzianum at 1% concentration for phorate concentrations of 1000 and 2000 ppm. However, incorporation of chlorpyriphos into soil resulted in increase in number of viable colonies of T. harzianum. The study indicated that phorate and chlorpyriphos could be safely applied with T. harzianum for the management of Phytophthora foot rot, nematodes and mealybugs on black pepper. &nbsp

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Background Worldwide, over 14% of individuals hospitalized for psychiatric reasons have readmissions to hospitals within 30 days after discharge. Predicting patients at risk and leveraging accelerated interventions can reduce the rates of early readmission, a negative clinical outcome (i.e., a treatment failure) that affects the quality of life of patient. To implement individualized interventions, it is necessary to predict those individuals at highest risk for 30-day readmission. In this study, our aim was to conduct a data-driven investigation to find the pharmacological factors influencing 30-day all-cause, intra- and interdepartmental readmissions after an index psychiatric admission, using the compendium of prescription data (prescriptome) from electronic medical records (EMR). Methods The data scientists in the project received a deidentified database from the Mount Sinai Data Warehouse, which was used to perform all analyses. Data was stored in a secured MySQL database, normalized and indexed using a unique hexadecimal identifier associated with the data for psychiatric illness visits. We used Bayesian logistic regression models to evaluate the association of prescription data with 30-day readmission risk. We constructed individual models and compiled results after adjusting for covariates, including drug exposure, age, and gender. We also performed digital comorbidity survey using EMR data combined with the estimation of shared genetic architecture using genomic annotations to disease phenotypes. Results Using an automated, data-driven approach, we identified prescription medications, side effects (primary side effects), and drug-drug interaction-induced side effects (secondary side effects) associated with readmission risk in a cohort of 1275 patients using prescriptome analytics. In our study, we identified 28 drugs associated with risk for readmission among psychiatric patients. Based on prescription data, Pravastatin had the highest risk of readmission (OR = 13.10; 95% CI (2.82, 60.8)). We also identified enrichment of primary side effects (n = 4006) and secondary side effects (n = 36) induced by prescription drugs in the subset of readmitted patients (n = 89) compared to the non-readmitted subgroup (n = 1186). Digital comorbidity analyses and shared genetic analyses further reveals that cardiovascular disease and psychiatric conditions are comorbid and share functional gene modules (cardiomyopathy and anxiety disorder: shared genes (n = 37; P = 1.06815E-06)). Conclusions Large scale prescriptome data is now available from EMRs and accessible for analytics that could improve healthcare outcomes. Such analyses could also drive hypothesis and data-driven research. In this study, we explored the utility of prescriptome data to identify factors driving readmission in a psychiatric cohort. Converging digital health data from EMRs and systems biology investigations reveal a subset of patient populations that have significant comorbidities with cardiovascular diseases are more likely to be readmitted. Further, the genetic architecture of psychiatric illness also suggests overlap with cardiovascular diseases. In summary, assessment of medications, side effects, and drug-drug interactions in a clinical setting as well as genomic information using a data mining approach could help to find factors that could help to lower readmission rates in patients with mental illness

Assessment of phytoplankton diversity, distribution, and environmental variables along the southeast coast of India

Coastal waters are dynamic because of anthropogenic activities that contribute nutrients and contaminants. These changes have the potential to alter patterns of primary production and thus pelagic food webs. Here, we investigated the spatial variation of the phytoplankton community and its response to changing environmental variables at 84 stations along the five coastal districts of Tamil Nadu (TN). During the present study, 85 phytoplankton species were recorded, such as diatoms (64), dinoflagellates (18), silicoflagellates (1), and Cyanophyceae (2). The maximum phytoplankton abundance was recorded on the Thanjavur coast and gradually decreased towards the south coast of Tamil Nadu. Among the phytoplankton community, 50% was dominated by pennate diatoms, attributed to higher NO3− concentrations in the coastal waters due to agricultural discharge. Cluster analysis revealed that Ramanathapuram and Tirunelveli formed a closed cluster, whereas Thanjavur and Pudukottai formed a separate closed cluster associated with higher nutrient and metal concentrations, highlighting the difference in physicochemical parameters between the northern and southern districts of the TN coast. Relatively high nutrient concentrations in the coastal waters of northern districts are of greater concern, which could impact the coastal ecosystem. Coastal eutrophication is becoming a widespread phenomenon, causing disruption in the food chain and ecosystem balances and hence requiring regular monitoring and management

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Circulating microRNAs in sera correlate with soluble biomarkers of immune activation but do not predict mortality in ART treated individuals with HIV-1 infection: A case control study

Introduction: The use of anti-retroviral therapy (ART) has dramatically reduced HIV-1 associated morbidity and mortality. However, HIV-1 infected individuals have increased rates of morbidity and mortality compared to the non-HIV-1 infected population and this appears to be related to end-organ diseases collectively referred to as Serious Non-AIDS Events (SNAEs). Circulating miRNAs are reported as promising biomarkers for a number of human disease conditions including those that constitute SNAEs. Our study sought to investigate the potential of selected miRNAs in predicting mortality in HIV-1 infected ART treated individuals. Materials and Methods: A set of miRNAs was chosen based on published associations with human disease conditions that constitute SNAEs. This case: control study compared 126 cases (individuals who died whilst on therapy), and 247 matched controls (individuals who remained alive). Cases and controls were ART treated participants of two pivotal HIV-1 trials. The relative abundance of each miRNA in serum was measured, by RTqPCR. Associations with mortality (all-cause, cardiovascular and malignancy) were assessed by logistic regression analysis. Correlations between miRNAs and CD4+ T cell count, hs-CRP, IL-6 and D-dimer were also assessed. Results: None of the selected miRNAs was associated with all-cause, cardiovascular or malignancy mortality. The levels of three miRNAs (miRs -21, -122 and -200a) correlated with IL-6 while miR-21 also correlated with D-dimer. Additionally, the abundance of miRs -31, -150 and -223, correlated with baseline CD4+ T cell count while the same three miRNAs plus miR- 145 correlated with nadir CD4+ T cell count. Discussion: No associations with mortality were found with any circulating miRNA studied. These results cast doubt onto the effectiveness of circulating miRNA as early predictors of mortality or the major underlying diseases that contribute to mortality in participants treated for HIV-1 infection