scholarly journals Data science and machine learning in anesthesiology

2020 ◽  
Vol 73 (4) ◽  
pp. 285-295 ◽  
Author(s):  
Dongwoo Chae
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Logistic Regression Model ◽  
Low Cost ◽  
Predictive Performance ◽  
Data Driven ◽  
Complex Interactions ◽  
Rich Information ◽  
Classification Tasks ◽  
Basic Concepts

Machine learning (ML) is revolutionizing anesthesiology research. Unlike classical research methods that are largely inference-based, ML is geared more towards making accurate predictions. ML is a field of artificial intelligence concerned with developing algorithms and models to perform prediction tasks in the absence of explicit instructions. Most ML applications, despite being highly variable in the topics that they deal with, generally follow a common workflow. For classification tasks, a researcher typically tests various ML models and compares the predictive performance with the reference logistic regression model. The main advantage of ML lies in its ability to deal with many features with complex interactions and its specific focus on maximizing predictive performance. However, emphasis on data-driven prediction can sometimes neglect mechanistic understanding. This article mainly focuses on the application of supervised ML to electronic health record (EHR) data. The main limitation of EHR-based studies is in the difficulty of establishing causal relationships. However, the associated low cost and rich information content provide great potential to uncover hitherto unknown correlations. In this review, the basic concepts of ML are introduced along with important terms that any ML researcher should know. Practical tips regarding the choice of software and computing devices are also provided. Towards the end, several examples of successful ML applications in anesthesiology are discussed. The goal of this article is to provide a basic roadmap to novice ML researchers working in the field of anesthesiology.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities

2022 ◽  
Author(s):  
Renáta Németh ◽  
Fanni Máté ◽  
Eszter Katona ◽  
Márton Rakovics ◽  
Domonkos Sik
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Data Science ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Online Health Communities ◽  
Health Communities ◽  
Open Question

AbstractSupervised machine learning on textual data has successful industrial/business applications, but it is an open question whether it can be utilized in social knowledge building outside the scope of hermeneutically more trivial cases. Combining sociology and data science raises several methodological and epistemological questions. In our study the discursive framing of depression is explored in online health communities. Three discursive frameworks are introduced: the bio-medical, psychological, and social framings of depression. ~80 000 posts were collected, and a sample of them was manually classified. Conventional bag-of-words models, Gradient Boosting Machine, word-embedding-based models and a state-of-the-art Transformer-based model with transfer learning, called DistilBERT were applied to expand this classification on the whole database. According to our experience ‘discursive framing’ proves to be a complex and hermeneutically difficult concept, which affects the degree of both inter-annotator agreement and predictive performance. Our finding confirms that the level of inter-annotator disagreement provides a good estimate for the objective difficulty of the classification. By identifying the most important terms, we also interpreted the classification algorithms, which is of great importance in social sciences. We are convinced that machine learning techniques can extend the horizon of qualitative text analysis. Our paper supports a smooth fit of the new techniques into the traditional toolbox of social sciences.

Doing more with less

2021 ◽  
Vol 14 (11) ◽  
pp. 2059-2072
Author(s):  
Fatjon Zogaj ◽  
José Pablo Cambronero ◽  
Martin C. Rinard ◽  
Jürgen Cito
Keyword(s):  
Machine Learning ◽  
Execution Time ◽  
Time Budget ◽  
Empirical Evaluation ◽  
Predictive Performance ◽  
Performance Metric ◽  
Automated Machine Learning ◽  
Classification Tasks ◽  
The Impact ◽  
User Intervention

Automated machine learning (AutoML) promises to democratize machine learning by automatically generating machine learning pipelines with little to no user intervention. Typically, a search procedure is used to repeatedly generate and validate candidate pipelines, maximizing a predictive performance metric, subject to a limited execution time budget. While this approach to generating candidates works well for small tabular datasets, the same procedure does not directly scale to larger tabular datasets with 100,000s of observations, often producing fewer candidate pipelines and yielding lower performance, given the same execution time budget. We carry out an extensive empirical evaluation of the impact that downsampling - reducing the number of rows in the input tabular dataset - has on the pipelines produced by a genetic-programming-based AutoML search for classification tasks.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

2021 ◽  
pp. 000456322110468
Author(s):  
Anudeep P P ◽  
Suchitra Kumari ◽  
Aishvarya S Rajasimman ◽  
Saurav Nayak ◽  
Pooja Priyadarsini
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Random Forests ◽  
Predictive Performance ◽  
Support Vector ◽  
Learning Models ◽  
Complex Interactions ◽  
Clinical Biochemistry Laboratory ◽  
Study Laboratory ◽  
Machine Learning Models

Background LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models—random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. Methods The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019–2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. Results LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. Conclusion Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

Evaluation of Machine Learning Methods for Prediction of Multiphase Production Rates

2021 ◽  
Author(s):  
Anton Gryzlov ◽  
Liliya Mironova ◽  
Sergey Safonov ◽  
Muhammad Arsalan
Keyword(s):  
Machine Learning ◽  
Focal Point ◽  
Low Cost ◽  
Continuous Production ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Flow Metering ◽  
Production Monitoring

Abstract Multiphase flow metering is an important tool for production monitoring and optimization. Although there are many technologies available on the market, the existing multiphase meters are only accurate to a certain extend and generally are expensive to purchase and maintain. Virtual flow metering (VFM) is a low-cost alternative to conventional production monitoring tools, which relies on mathematical modelling rather than the use of hardware instrumentation. Supported by the availability of the data from different sensors and production history, the development of different virtual flow metering systems has become a focal point for many companies. This paper discusses the importance of flow modelling for virtual flow metering. In addition, main data-driven algorithms are introduced for the analysis of several dynamic production data sets. Artificial Neural Networks (ANN) together with advanced machine learning methods such as GRU and XGBoost have been considered as possible candidates for virtual flow metering. The obtained results indicate that the machine learning algorithms estimate oil, gas and water rates with acceptable accuracy. The feasibility of the data-driven virtual metering approach for continuous production monitoring purposes has been demonstrated via a series of simulation-based cases. Amongst the used algorithms the deep learning methods provided the most accurate results combined with reasonable time for model training.

scholarly journals Predicting human health from biofluid-based metabolomics using machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ethan D. Evans ◽  
Claire Duvallet ◽  
Nathaniel D. Chu ◽  
Michael K. Oberst ◽  
Michael A. Murphy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Data Driven ◽  
Charge Ratio ◽  
High Dimensional ◽  
Data Sets ◽  
Health State ◽  
Invasive Diagnostics ◽  
Machine Learning Models ◽  
Human Metabolomics

Abstract Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified—where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.

scholarly journals Dealing with Randomness and Concept Drift in Large Datasets

Data ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Kassim S. Mwitondi ◽  
Raed A. Said
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Data Science ◽  
Concept Drift ◽  
Real Life ◽  
Quality Education ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Address Data ◽  
The Impact

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.

scholarly journals Predicting human health from biofluid-based metabolomics using machine learning

2020 ◽  
Author(s):  
Ethan D. Evans ◽  
Claire Duvallet ◽  
Nathaniel D. Chu ◽  
Michael K. Oberst ◽  
Michael A. Murphy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
Data Driven ◽  
Health Conditions ◽  
Health State ◽  
Sample Type ◽  
Health States ◽  
Experimental Conditions ◽  
Combining Data ◽  
Invasive Diagnostics

AbstractBiofluid-based metabolomics enables the profiling of thousands of molecules and has the potential to provide highly accurate, minimally invasive diagnostics for a range of health conditions. However, typical metabolomics studies focus on only a few statistically significant features. We study the applicability of machine learning for health state-prediction across 35 human mass spectrometry-based metabolomics studies. Models trained on all features outperform those using only significant features and frequently provide high predictive performance across nine health states, despite disparate experimental conditions and disease contexts. Combining data from different experimental settings (e.g. sample type, instrument, chromatography) within a study minimally alters predictive performance, suggesting information overlap between different methods. Using only non-significant features, we still often obtain high predictive performance. To facilitate further advances, we provide all data online. This work highlights the applicability of biofluid-based metabolomics with data-driven analysis for health state diagnostics.

scholarly journals Machine learning identifies SNPs predictive of advanced coronary artery calcium in ClinSeq® and Framingham Heart Study cohorts

2017 ◽  
Author(s):  
Cihan Oguz ◽  
Shurjo K Sen ◽  
Adam R Davis ◽  
Yi-Ping Fu ◽  
Christopher J O’Donnell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Data Science ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Medical Decision ◽  
Learning Tools ◽  
Operating Characteristics ◽  
Replication Cohort ◽  
Discovery Cohort

ABSTRACTOne goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits and requires systems level approaches. To this end, we employed random forests (RF) and neural networks (NN) for predictive modeling of coronary artery calcification (CAC), which is an intermediate end-phenotype of coronary artery disease (CAD). Model inputs were derived from advanced cases in the ClinSeq® discovery cohort (n=16) and the FHS replication cohort (n=36) from 89th−99th CAC score percentile range, and age-matching controls (ClinSeq® n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables (CLIN), genotypes of 57 SNPs associated with CAC in past GWAS (SNP Set-1), and an alternative set of 56 SNPs (SNP Set-2) ranked highest in terms of their nominal correlation with advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristics curves (AUC). Within the discovery cohort, RF models generated AUC values of 0.69 with CLIN, 0.72 with SNP Set-1, and 0.77 with their combination. In the replication cohort, SNP Set-1 was again more predictive (AUC=0.78) than CLIN (AUC=0.61), but also more predictive than the combination (AUC=0.75). In contrast, in both cohorts, SNP Set-2 generated enhanced predictive performance with or without CLIN (AUC> 0.8). Using the 21 SNPs of SNP Set-2 that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq® data and tested with FHS data and replicated the high predictive accuracy (AUC>0.8) with several topologies, thereby identifying several potential susceptibility loci for advanced CAD. Several CAD-related biological processes were found to be enriched in the network of genes constructed from these loci. In both cohorts, SNP Set-1 derived from past CAC GWAS yielded lower performance than SNP Set-2 derived from “extreme” CAC cases within the discovery cohort. Machine learning tools hold promise for surpassing the capacity of conventional GWAS-based approaches for creating predictive models utilizing the complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders.

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