Learning patient similarity using joint distributed embeddings of treatment and diagnoses

We propose the use of vector-based word embedding models to learn a cross-conceptual representation of medical vocabulary. The learned model is dense and encodes useful knowledge from the training concepts. Applying the embedding to the concepts of diagnoses and medications, we then show that they can then be used to measure similarities among patient prescriptions, leading to the discovery of in- formative and intuitive relationships between patients

Serological and molecular characterization of Cryptosporidium species from humans in Sokoto State, Nigeria

Cryptosporidium species are one of the most common causes of gastrointestinal infection in humans around the world. This study aimed at the characterization of Cryptosporidium species in humans using the 18S rRNA gene. Among the 368 human faecal samples screened using Cryptosporidium antigen Copro-ELISA kit, 61 (16.6%) were positive. The positive faecal samples were subjected to Nested PCR for the amplification of 830 bp fragments of small subunit (SSU) rRNA gene and followed by nucleotide sequencing. Out of the 61 copro-ELISA positive samples, 5 (8.2%) were PCR positive for Cryptosporidium species (3 (4.9%) of C. parvum and 2 (3.3%) of C. hominis). Two HIV patients were found to be harbouring C. parvum and C. hominis, so also as hypertensive and diarrheic patients harbouring C. parvum and C. hominis, respectively. Higher prevalence rates of Cryptosporidium was found in young children (11.1%), males (8.7%), loose faeces (42.9%) than older age groups (8.7%), females (7.9%) and well-formed (3.1%) or mucoid/pasty faeces (0%) based on the data gathered from the close-ended questionnaire also used on each human subject. This study was the first to report C. parvum and C. hominis infecting humans in Sokoto state, Northwestern Nigeria. It is suggested that a multi-locus study of Cryptosporidium species in developing countries would be necessary to determine the extent of transmission of Cryptosporidium in the populations

Efficiently Reusing Natural Language Processing Models for Phenotype Identification in Free-text Electronic Medical Records: Methodological Study

Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to construct comprehensive patient profiles for delivering better health-care. Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results. Objective: The aim of this work is to minimise the effort involved in reusing NLP models on free-text medical records. Methods: We formally define and analyse the model adaptation problem in phenotype identification tasks. We identify “duplicate waste” and “imbalance waste”, which collectively impede efficient model reuse. We propose a concept embedding based approach to minimise these sources of waste without the need for labelled data from new settings. Results: We conduct experiments on data from a large mental health registry to reuse NLP models in four phenotype identification tasks. The proposed approach can choose the best model for a new task, identifying up to 76% of phenotype mentions without the need for validation and model retraining, and with very good performance (93-97% accuracy). It can also provide guidance for validating and retraining the selected model for novel language patterns in new tasks, saving around 80% of the effort required in “blind” model-adaptation approaches. Conclusions: Adapting pre-trained NLP models for new tasks can be more efficient and effective if the language pattern landscapes of old settings and new settings can be made explicit and comparable. Our experiments show that the phenotype embedding approach is an effective way to model language patterns for phenotype identification tasks and that its use can guide efficient NLP model reuse

The side effect profile of Clozapine in real world data of three large mental health hospitals

Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and methods: We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER). Results: Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs. Conclusion: A better understanding of how drugs work in the real world can complement clinical trials

Evaluation and prediction of groundwater quality for irrigation using an integrated water quality indices, machine learning models and GIS approaches: a representative case study

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments

Fuzzy Membership Functions Tuning For Speed Controller Of Induction Motor Drive: Performance Improvement.

Fuzzy logic controller (FLC) has gained high interest in the field of speed control of machine drives in both academic and industrial communities. This is due to the features of FLC of handling non-linearity and variations. FLC system consists of three main elements: scaling factors (SFs), membership functions (MFs), and rule-base. Fuzzy MFs can be designed with different types and sizes. For induction motor (IM) speed control, (3x3), (5x5) and (7x7) MFs are the most used MFs sizes, and normally designed based on symmetrical distribution. However, changing the width and peak position of MFs design enhance the performance. In this paper, tuning of MFs of FLC speed control of IM drives is considered. Considering (3x3), (5x5) and (7x7) MFs sizes, the widths and peak positions of these MFs are asymmetrically distributed to improve the performance of IM drive. Based on these MFs sizes, the widths and peak positions are moved toward the origin (zero), negative and positive side that produces a controller less sensitive to the small error variations. Based on simulation and performance evaluations, improvement of 5% in settling time (Ts), 0.5% in rise time and 20% of steady-state improvement achieved with the tuned MFs compared to original MFs

The influence of injection molding parameter on properties of thermally conductive plastic

Thermally conductive plastic is the composite between metal-plastic material that is becoming popular because if it special characteristic. Injection moulding was regarded as the best process for mass manufacturing of the plastic composite due to its low production cost. The objective of this research is to find the best combination of the injection parameter setting and to find the most significant factor that effect the strength and thermal conductivity of the composite. Several parameter such as the volume percentage of copper powder, nozzle temperature and injection pressure of injection moulding machine were investigated. The analysis was done using Design Expert Software by implementing design of experiment method. From the analysis, the significant effects were determined and mathematical models of only significant effect were established. In order to ensure the validity of the model, confirmation run was done and percentage errors were calculated. It was found that the best combination parameter setting to maximize the value of tensile strength is volume percentage of copper powder of 3.00%, the nozzle temperature of 195oC and the injection pressure of 65%, and the best combination parameter settings to maximize the value of thermal conductivity is volume percentage of copper powder of 7.00%, the nozzle temperature of 195oC and the injection pressure of 65% as recommended

Reduction of metastasis using a non-volatile buffer

The tumor microenvironment is acidic as a consequence of upregulated glycolysis and poor perfusion and this acidity, in turn, promotes invasion and metastasis. We have recently demonstrated that chronic consumption of sodium bicarbonate increased tumor pH and reduced spontaneous and experimental metastases. This occurred without affecting systemic pH, which was compensated. Additionally, these prior data did not rule out the possibility that bicarbonate was working though effects on carbonic anhydrase, and not as a buffer per se. Here, we present evidence that chronic ingestion of a non-volatile buffer, 2-imidazole-1-yl-3-ethoxycarbonylpropionic acid (IEPA) with a pKa of 6.9 also reduced metastasis in an experimental PC3M prostate cancer mouse model. Animals (n = 30) were injected with luciferase expressing PC3M prostate cancer cells either subcutaneously (s.c., n = 10) or intravenously (i.v., n = 20). Four days prior to inoculations, half of the animals for each experiment were provided drinking water containing 200 mM IEPA buffer. Animals were imaged weekly to follow metastasis, and these data showed that animals treated with IEPA had significantly fewer experimental lung metastasis compared to control groups (P < 0.04). Consistent with prior work, the pH of treated tumors was elevated compared to controls. IEPA is observable by in vivo magnetic resonance spectroscopy and this was used to measure the presence of IEPA in the bladder, confirming that it was orally available. The results of this study indicate that metastasis can be reduced by non-volatile buffers as well as bicarbonate and thus the effect appears to be due to pH buffering per se

Assessment of aortic stiffness by cardiovascular magnetic resonance following the treatment of severe aortic stenosis by TAVI and surgical AVR

Aortic stiffness is increasingly used as an independent predictor of adverse cardiovascular outcomes. We sought to compare the impact of transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) upon aortic vascular function using cardiovascular magnetic resonance (CMR) measurements of aortic distensibility and pulse wave velocity (PWV).A 1.5 T CMR scan was performed pre-operatively and at 6 m post-intervention in 72 patients (32 TAVI, 40 SAVR; age 76 ± 8 years) with high-risk symptomatic severe aortic stenosis. Distensibility of the ascending and descending thoracic aorta and aortic pulse wave velocity were determined at both time points. TAVI and SAVR patients were comparable for gender, blood pressure and left ventricular ejection fraction. The TAVI group were older (81 ± 6.3 vs. 72.8 ± 7.0 years, p < 0.05) with a higher EuroSCORE II (5.7 ± 5.6 vs. 1.5 ± 1.0 %, p < 0.05). At 6 m, SAVR was associated with a significant decrease in distensibility of the ascending aorta (1.95 ± 1.15 vs. 1.57 ± 0.68 × 10(-3)mmHg(-1), p = 0.044) and of the descending thoracic aorta (3.05 ± 1.12 vs. 2.66 ± 1.00 × 10(-3)mmHg(-1), p = 0.018), with a significant increase in PWV (6.38 ± 4.47 vs. 11.01 ± 5.75 ms(-1), p = 0.001). Following TAVI, there was no change in distensibility of the ascending aorta (1.96 ± 1.51 vs. 1.72 ± 0.78 × 10(-3)mmHg(-1), p = 0.380), descending thoracic aorta (2.69 ± 1.79 vs. 2.21 ± 0.79 × 10(-3)mmHg(-1), p = 0.181) nor in PWV (8.69 ± 6.76 vs. 10.23 ± 7.88 ms(-1), p = 0.301) at 6 m.Treatment of symptomatic severe aortic stenosis by SAVR but not TAVI was associated with an increase in aortic stiffness at 6 months. Future work should focus on the prognostic implication of these findings to determine whether improved patient selection and outcomes can be achieved