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Prediction of Liver Enzyme Elevation Using Supervised Machine Learning in Patients With Rheumatoid Arthritis on … – Cureus

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Machine-learning model for predicting oliguria in critically ill patients | Scientific Reports – Nature.com

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This retrospective cohort study used the electronic health record data of consecutive patients admitted to the ICU at Chiba University Hospital, Japan, from November 2010 to March 2019. The annual number of patients admitted to the 22-bed surgical/medical ICU ranged from 1,541 to 1,832. We excluded patients on maintenance dialysis and those without a documented body weight. This study was approved by the Ethical Review Board of Chiba University Graduate School of Medicine (approval number: 3380) in accordance with the Declaration of Helsinki. The Ethical Review Board of Chiba University Graduate School of Medicine waived the requirement for written informed consent in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan.

We defined oliguria as urine output of less than 0.5mL/kg/h according to the Kidney Disease: Improving Global Outcomes stage I criteria. AKI was diagnosed based on an increase in serum creatinine level of at least 0.3mg/dL from the baseline or oliguria38.

Patient records from the ICU data system contained 1,031 input variables, including (A) physiological measurements acquired every minute (heart rate, blood pressure, respiratory rate, peripheral oxygen saturation, and body temperature), (B) blood tests (complete blood count, biochemistry, coagulation, and blood gas analysis), (C) name and dosage of medications, (D) type and amount of blood transfusion, (E) patient observation record, and (F) patient care record. The minute-by-minute time-series tables were aggregated into hourly time-series tables. In the process of aggregating the tables, the median value was used for physiological measurements and the blood test values were obtained from the most recent test. For patient excretion values, urine and stool volumes were calculated as one-hour sums. The following six calculated variables were added to the dataset: hourly intake, hourly output, hourly total balance, hourly urine volume (mL/kg), oliguria (urine volume of less than 0.5mL/kg/h), and oliguria for six consecutive hours. A total of 222 background information variables, including age, sex, and admission diagnosis, were also added to the dataset. Consequently, the dataset contained 1,127 variables. We treated the missing values as a separate group or excluded them from the analysis. To remove potential collinearity values, we performed a multicollinearity test and analyzed the data without these values.

The dataset was randomly divided: 80% for training and 20% for testing. We developed a sequential machine-learning model to predict oliguria at any given time during the ICU stay using hourly variables and baseline information (Fig.1). For the values that were not continuously obtained, we used the most recent ones for the model development. The input variables were updated to encompass a 1-h window of the preceding values for the physiological measurements, blood tests, and medications. The primary and secondary outcome variables were oliguria at 6 and 72h after an arbitrary time point from ICU admission to discharge, respectively. Accordingly, we used variables recorded until 6 or 72h before ICU discharge corresponding to each outcome variable. The outcome variable was not incorporated as a predictor in the final model. After constructing the algorithm with the training data, the model predictions were validated using the test data. We validated the model performance with a fivefold cross validation. To ensure that the estimated model probabilities aligned with the actual probabilities of oliguria occurrence, we plotted the calibration curve of the model. The curve indicated that our model was well calibrated (Supplementary File 1: Fig. S4).

We selected four representative machine learning classifiers: LightGBM, category boosting (CatBoost), random forest, and extreme gradient boosting (XGboost). Before developing the prediction model, we compared the computational performances and model accuracies using the four classifiers (Supplementary File 1: Table S2). To develop the machine learning algorithm, we used a cloud computer (Google Collaboratory memory 25GB) to evaluate the accuracy of the model. The AUC values based on the receiver operating calibrating curves, sensitivity, specificity, and F1 score were calculated. Among the machine learning classifiers, LightGBM showed the best computation speed and AUC and the second-best F1 score with a marginal difference from XGboost (XGboost 0.899, LightGBM 0.896). Based on these results, we decided to use LightGBM for the analysis in this study. After developing a prediction model with all the variables, we reduced the number of variables for prediction by selecting clinically relevant variables (Supplementary File: Table S2). Subsequently, we compared the performances of the LightGBM model using the selected variables and all the variables. As a sensitivity analysis, we re-analyzed the data using a different computer environment, Amazon Web Service Sagemaker. The computer settings included the following: image: Data Science 3.0, kernel: python 3, and instance type: ml.t3.medium (memory 64GB).

To evaluate the important variables contributing to building the prediction model, we used the SHAP value. The SHAP value indicates the impact of each feature on the model output, with higher interpretability in machine learning models. We expressed the SHAP value as an absolute number with a positive or negative association between the variable and outcome. SHAP individual force plots showed several features at scale with a color bar that indicated the feature contribution to the onset of oliguria in individual instances, enhancing the interpretability regarding the connection between traits and the occurrence of oliguria. For the subgroup analyses, we compared the accuracies of the models in predicting oliguria based on sex, age (65 or>66years), and furosemide administration. To quantify the differences in the AUC plots of the two groups, the absolute values of the differences in the AUCs of each group from 6 to 72h were summed and averaged to obtain the MAE.

Data were expressed as medians with interquartile ranges for continuous values and as absolute numbers and percentages for categorical values. A P value<0.05 was considered as statistically significant. The main Python packages used in the analysis to create the machine learning algorithms were Python 3.10.11, pandas 1.5.3, numpy 1.22.4, matplotlib 3.7.1, scikit-learn 1.2.2, XGboost 1.7.2, lightgbm 2.2.3, catboost 1.1.1, and shap 0.41.0.

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Machine learning approaches for estimating interfacial tension between oil/gas and oil/water systems: a performance … – Nature.com

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Machine learning and computer vision can boost urban renewal – Hello Future Orange – Hello Future

Monday 8th of January 2024

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In the 2010s, the city of New York set an example for urban authorities when it used big data to optimise public services. Since then, progress in machine learning has led to further advances in the field of data analysis. A new computer vision project has notably demonstrated how Google Street View images can now be used to monitor urban decay.

In a ground-breaking project in the 2010s, the city of New York reorganized a wide range of public services to take into account the analysis of big data collected by local authorities. These included measures to prune the citys trees, and to investigate buildings with high levels of fire risk, properties managed by slumlords, and restaurants illegally dumping cooking oil into public sewers. Since then, progress in the field of machine learning has continued to extend the potential for data-driven public initiatives, and scientists are also investigating the use of new data sources on which they could be based, among them two researchers from the universities of Stanford (California) and Notre-Dame (Indiana), who recently presented a new approach for the monitoring of urban decay in the journal Scientific Reports.

We wanted to highlight the flexibility of the approach rather than propose a method with a fixed set of features.

The algorithm developed by their project identifies eight visual features of urban decay in street-view images: potholes, barred or broken windows, dilapidated facades, tents, weeds, graffiti, garbage, and utility markings. Until now, the researchers note, the measurement of urban change has largely centred on quantifying urban growth, primarily by examining land use, land cover dynamics and changes in urban infrastructure.

The idea of their project was not so much to show all that can be done with street-view images, but rather to test the use of a single algorithm trained on data from several cities, and if necessary to retrain it without modifying its underlying structure. At the same time, it should be noted that the data being used was not collected by public authorities, but from a new source: Big data and machine learning are increasingly being used for public policies, points out Yong Suk Lee, an assistant professor at Notre-Dame, specializing in technology and urban economics. Our proposed method is complementary to these approaches. Our paper highlights the potential to add street-viewImages to the increasing toolkit of urban data analytics.

As the researchers explain, the automated analysis of images can facilitate the evaluation of the scope of deterioration: The measurement of urban decay is further complicated by the fact that on the ground measurements of urban environments are often expensive to collect, and can at times be more difficult, and even dangerous, to collect in deteriorating parts of the city..

The research project focused on images from three urban areas: the Tenderloin and Mission districts in San Francisco, Colonia Doctores and the historic centre of Mexico City, and the western part of South Bend, Indiana, an average size American town.

A single algorithm (YOLO) was trained twice on, on two different corpora. The first of these was composed of manually collected pictures from the streets of San Francisco and images of graffiti captured in Athens (Greece) from the STORM corpus. This dataset also included Google Street View shots of San Francisco, Los Angeles and Oakland with homeless peoples tents and tarps, and images of Mexico City. All of these were sourced from a multiyear period to measure ongoing change. Subsequently the Mexican pictures were withdrawn to create a second training dataset.

We initially worked with US data but decided to compare if adding data from Mexico City made a difference, explains Yong Suk Lee. Not surprisingly, the larger consolidated data set was better. Also, we tried different model sizes (number of parameters) to see the trade-offs between speed and performance. For example, the algorithm was better able to detect potholes and broken windows in San Francisco when the training data included images from Mexico City.

However, due to a lack of similar images of in its training corpus, the algorithm significantly underperformed when tested on more suburban spaces in South Bend, although it was largely successful in following local changes signalled by dilapidated facades and weeds. The results showed that towns of this type require a specially adapted training corpus. The features identifying decay could differ in other places. That is what we wanted to convey as well, by comparing different cities, points out the Notre-Dame researcher. We wanted to highlight the flexibility of the approach rather than propose a method with a fixed set of features. With its inherent flexibility and a vast amount of readily available source data in Google Street View, this new approach will likely feature many more future research projects.

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The 3 Best Machine Learning Stocks to Buy in January 2024 – InvestorPlace

Machine learning is transforming sectors including healthcare and transportation, offering lucrative opportunities in the best machine learning stocks. However, investors should approach cautiously, as not all stocks in this sector ensure returns. Discernment is key, as many firms claim advanced machine learning needs more solid business models or definitive applications.

Moreover, this sector branches into specialized niches, including data analysis and artificial intelligence (AI), with machine learning being a key driver. Some businesses have made remarkable strides in this space, demonstrating commendable growth and innovation. Their work within machine learning is remarkable, effectively reshaping the way we interact with technology. Subsequently, Statista projects that the machine-learning market will reach $204.30 billion by 2024.

Furthermore, machine learning stocks are gaining momentum, reflecting a growing fascination with AI. This expanding field holds substantial growth prospects, offering investors opportunities to support the innovators shaping our tech future. For those seeking the next breakthrough, machine learning stocks could be the secret to forge the billionaires of tomorrow.

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Amazon (NASDAQ:AMZN) has impressively evolved from a garage startup to the worlds second-largest company by revenue. A significant part of its 2023 success was achieving the fastest delivery speeds ever, particularly boosting its appeal in the consumables and everyday essentials market.

Impressively, Amazon shows robust growth in its financial performance, notably in the third quarter, with EPS of 94 cents, smashing the 60 cents forecast. The company revenue soared by 12.6% year over year (YOY) to $143.1 billion, beating expectations by $1.54 billion and showcasing its market strength and efficiency.

Furthermore, Amazon is boosting its Prime Video game, bringing in a pro from Walt Disney for its advertising push. Additionally, Amazon has been focused on developing a platform that appeals to businesses for machine learning purposes, creating a workflow pipeline to onboard companies of various sizes. This effort leverages AWS cloud technology to build AI models.

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Nvidia (NASDAQ:NVDA) is pushing the frontiers of quantum computing with its cuQuantum project, revolutionizing qubit simulation.

Simultaneously, its spicing up the AI realm with the Omniverse Cloud, enabling developers to master Isaac AMRs for sophisticated, AI-enhanced robotics. This fusion of high-tech and utility delivers innovation with a snazzy edge.

In the third quarter, Nvidias financials were impressive. Their non-GAAP earnings per share soared to $4.02, surpassing estimates by 63 cents. Revenue rocketed to $18.12 billion, up an astonishing 205.6% YOY. Also, data center revenue hit a new high of $14.51 billion, cementing Nvidias strong standing in the tech sector.

Furthermore, unveiling the GeForce RTX 4090D GPU in China gave Nvidias stock an additional boost. Analyst Vivek Arya, holding a confident $700 price target, forecasts the company will generate an impressive $100 billion incremental free cash flow over 2024 and 2025. Nvidia is not just playing in the tech arena; its setting new benchmarks, making it a standout choice for investors.

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Advanced Micro Devices (NASDAQ:AMD), with a market capitalization of 244 billion, solidifies its prominent status in the semiconductor sector. Endorsed by investment firm UBS alongside Micron Technology (NASDAQ:MU) for 2024, AMDs robust market presence and growth prospects are recognized, signaling a promising future.

Financially, In the third quarter, AMDs non-GAAP earnings per share reached 70 cents, exceeding estimates by 2 cents. Revenue rose to $5.8 billion, a 4.1% increase from last year, beating expectations by $110 million. Particularly, client segment revenue, driven by robust Ryzen mobile processor sales, soared to $1.5 billion, up 42% YOY.

Moreover, AMD isnt just riding the wave. Its making its own with the MI300 chips, poised as rivals to Nvidias H100. This strategic move has attracted tech giants like Meta Platforms (NASDAQ:META) and Microsoft (NASDAQ:MSFT), who are lining up for AMDs innovative chips. In the high-stakes semiconductor game, AMD is not just playing. Its setting the pace.

On the date of publication, Muslim Farooque did not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Muslim Farooque is a keen investor and an optimist at heart. A life-long gamer and tech enthusiast, he has a particular affinity for analyzing technology stocks. Muslim holds a bachelors of science degree in applied accounting from Oxford Brookes University.

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Data Science Salon Seattle Spotlights Generative AI and Machine Learning in Retail and E-commerce – GlobeNewswire

SEATTLE, Jan. 11, 2024 (GLOBE NEWSWIRE) -- Data Science Salon (DSS), recognized as the most diverse data science and machine learning community in the U.S., is delighted to announce its upcoming Seattle event. Scheduled for January 24th, 2024, at the modern Block 41 venue, DSSSEA is designed to spark transformative and innovative conversations about the application of AI and Machine Learning in the retail and e-commerce sectors.

DSS Seattle is dedicated to unraveling the complexities and potential of generative AI and machine learning within retail and e-commerce. Industry professionals will gather to explore pivotal topics, including:

This one-day, 200-person conference provides expert talks with leading data scientists from prominent companies such as Nordstrom, eBay, Amazon, Pinterest and Google and ample opportunities for networking, and collaborative discussions. All sessions will be recorded and made available on-demand within two hours post-event, ensuring that the insights and learnings are accessible to a wider audience beyond the day of the conference. Pre-recorded virtual sessions will also be available prior to the event to get our attendees ready for all DSSSEA has to offer.

I am thrilled to be speaking about experimentation at the Data Science Salon in Seattle. I hope to learn about the latest trends and techniques in data science experimentation, and to share my own experiences and insights with fellow attendees. I am excited to connect with like-minded professionals and to further develop my skills in this fast-paced and rapidly evolving field, says Benjamin Skrainka, Data Science Manager at eBay and virtual speaker for DSSSEA.

We invite data science practitioners, retail strategists, and e-commerce specialists to join us at DSSSEA for a day of identifying new ways to use AI and ML in your field. Registration is now open.

For more information and to reserve your seat for the in-person or on-demand event, please visit https://www.datascience.salon/seattle/.

About Data Science Salon Data Science Salon elevates the conversation in data science and machine learning by connecting industry experts and practitioners in a collaborative, community-focused environment. With a commitment to diversity and the advancement of the field, DSS is shaping the future of data-driven decision-making.

For Media and Sponsorship Inquiries: Anna Anisin Phone: +1 305-215-4527 Email: anna.a@formulatedby.com

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