A review on current advances in machine learning based diabetes prediction

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes.     

Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms

Purpose

The purpose of this study is to propose an efficient coal workers' pneumoconiosis (CWP) clinical prediction system and put it into clinical use for clinical diagnosis of pneumoconiosis.

Methods

Patients with CWP and dust-exposed workers who were enrolled from August 2021 to December 2021 were included in this study. Firstly, we chose the embedded method through using three feature selection approaches to perform the prediction analysis. Then, we performed the machine learning algorithms as the model backbone and combined them with three feature selection methods, respectively, to determine the optimal predictive model for CWP.

Results

Through applying three feature selection approaches based on machine learning algorithms, it was found that AaDO₂ and some pulmonary function indicators played an important role in prediction for identifying CWP of early stage. The support vector machine (SVM) algorithm was proved as the optimal machine learning model for predicting CWP, with the ROC curves obtained from three feature selection methods using SVM algorithm whose AUC values of 97.78%, 93.7%, and 95.56%, respectively.

Conclusion

We developed the optimal model (SVM algorithm) through comparisons and analyses among the performances of different models for the prediction of CWP as a clinical application.     

Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes. Timely diagnosis and intervention are essential for treatment that reduces the risk of vision loss. A good color retinal (fundus) photograph can be used as a surrogate for face-to-face evaluation of DR. The use of computers to assist or fully automate DR evaluation from retinal images has been studied for many years. Early work showed promising results for algorithms in detecting and classifying DR pathology. Newer techniques include those that adapt machine learning technology to DR image analysis. Challenges remain, however, that must be overcome before fully automatic DR detection and analysis systems become practical clinical tools.     

How long will my mouse live? Machine learning approaches for prediction of mouse life span

Prediction of individual life span based on characteristics evaluated at middle-age represents a challenging objective for aging research. In this study, we used machine learning algorithms to construct models that predict life span in a stock of genetically heterogeneous mice. Life-span prediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data. Using a combination of body weight and T-cell subset measures evaluated before 2 years of age, we show that the life-span quartile to which an individual mouse belongs can be predicted with an accuracy of 35.3% (+/-0.10%). This result provides a new benchmark for the development of life-span-predictive models, but improvement can be expected through identification of new predictor variables and development of computational approaches. Future work in this direction can provide tools for aging research and will shed light on associations between phenotypic traits and longevity.     

Predictive modeling of 30-day readmission risk of diabetes patients by logistic regression,artificial neural network,and EasyEnsemble

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and Easy Ensemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features(4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods(oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.     

Clinical approaches for integrating machine learning for patients with lymphoma: Current strategies and future perspectives

Machine learning (ML) approaches have been applied in the diagnosis and prediction of haematological malignancies. The consideration of ML algorithms to complement or replace current standard of care approaches requires investigation into the methods used to develop relevant algorithms and understanding the accuracy, sensitivity and specificity of such algorithms in the diagnosis and prognosis of malignancies. Here we discuss methods used to develop ML algorithms and review original research studies for assessing the use of ML algorithms in the diagnosis and prognosis of lymphoma.     

Applied machine learning in hematopathology

An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.     

The Promise of Machine Learning: When Will it be Delivered?

BackgroundThe real-life applications of machine learning clinical decision making is currently lagging behind its promise. One of the critics on machine learning is that it doesn't outperform more traditional statistical approaches in every problem.Methods and ResultsAuthors of “Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics: Insights From the UNOS Database” presented in the current issue of the Journal of Cardiac Failure that machine learning approaches do not provide significantly higher performance when compared to more traditional statistical approaches in predicting mortality following heart transplant. In this brief report, we provide an insight on the possible reasons for why machine learning methods do not outperform more traditional approaches for every problem and every dataset.ConclusionsMost of the performance-focused critics on machine learning are because the bar is set unfairly too high for machine learning. In most cases, machine learning methods provides at least as good results as traditional statistical methods do. It is normal for machine learning models to provide similar performance with linear models if the actual underlying input-outcome relationship is linear. Moreover, machine learning methods outperforms linear statistical models when the underlying input-output relationship is not linear and if the dataset is large enough and include predictors capturing that nonlinear relationship.     

A review of machine learning in obesity

Rich sources of obesity‐related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity‐related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high‐level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.     

Inverse Design of Materials by Machine Learning

It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples’ current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.     

The Role of Machine Learning in Cardiovascular Pathology

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don’t always agree, and thus “truth” may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power.     

Utilizing state‐of‐the‐art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease

Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)—diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking—has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large‐scale multi‐omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.     

Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.     

Artificial intelligence in Hematology

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms.

The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis.

Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction.

In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.     

Acute myeloid leukemia and artificial intelligence,algorithms and new scores

Artificial intelligence, and more narrowly machine-learning, is beginning to expand humanity's capacity to analyze increasingly large and complex datasets. Advances in computer hardware and software have led to breakthroughs in multiple sectors of our society, including a burgeoning role in medical research and clinical practice. As the volume of medical data grows at an apparently exponential rate, particularly since the human genome project laid the foundation for modern genetic inquiry, informatics tools like machine learning are becoming crucial in analyzing these data to provide meaningful tools for diagnostic, prognostic, and therapeutic purposes. Within medicine, hematologic diseases can be particularly challenging to understand and treat given the increasingly complex and intercalated genetic, epigenetic, immunologic, and regulatory pathways that must be understood to optimize patient outcomes. In acute myeloid leukemia (AML), new developments in machine learning algorithms have enabled a deeper understanding of disease biology and the development of better prognostic and predictive tools. Ongoing work in the field brings these developments incrementally closer to clinical implementation.     

Artificial intelligence in hematology

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.     

A practical framework for encouraging and supporting positive behaviour change in diabetes

A wide range of diabetes‐directed interventions – including novel medications, devices and comprehensive education programmes – have been shown to be effective in clinical trials. But in the real world of diabetes care their efficacy is often dependent upon on how well a clinician is able to support personal engagement and motivation of the person with diabetes to use these new tools and knowledge consistently, and as directed. Although many person‐centred motivational and behavioural strategies have been developed, for example, action planning, motivational interviewing and empowerment‐based communication, the sheer number and apparent lack of clear differences among them have led to considerable confusion. The primary goal of this review, therefore, is to provide a practical framework that organizes and structures these programmes to enhance their more systematic use in clinical care. Its purpose is to enhance clinician efforts to respectfully encourage and support engagement and motivation for behaviour change in people with diabetes. The three‐step framework for organizing and describing the specific clinical processes involved is based on self‐determination theory and includes: clinician preparation for a different type of clinical encounter, clinician/person with diabetes relationship building, and clinician utilization of specific behavioural tools. We conclude with practical considerations for application of this framework to the real world of clinical care.     

Assessment of the Quality and Mechanical Parameters of Castings Using Machine Learning Methods

The aim of the work is to investigate the effectiveness of selected classification algorithms and their extensions in assessing microstructure of castings. Experiments were carried out in which the prepared algorithms and machine learning methods were tested in various conditions and configurations, as well as for various input data, which are photos of castings (photos of the microstructure) or information about the material (e.g., type, composition). As shown by the literature review, there are few scientific papers on this subject (i.e., in the use of machine learning to assess the quality of the microstructure and the obtained strength properties of cast iron). The effectiveness of machine learning algorithms in assessing the quality of castings will be tested using the most universal methods. Results obtained by classic machine learning methods and by neural networks will be compared with each other, taking into account aspects such as interpretability of results, ease of model implementation, algorithm simplicity, and learning time.     

Automation,machine learning,and artificial intelligence in echocardiography: A brave new world

Automation, machine learning, and artificial intelligence (AI) are changing the landscape of echocardiography providing complimentary tools to physicians to enhance patient care. Multiple vendor software programs have incorporated automation to improve accuracy and efficiency of manual tracings. Automation with longitudinal strain and 3D echocardiography has shown great accuracy and reproducibility allowing the incorporation of these techniques into daily workflow. This will give further experience to nonexpert readers and allow the integration of these essential tools into more echocardiography laboratories. The potential for machine learning in cardiovascular imaging is still being discovered as algorithms are being created, with training on large data sets beyond what traditional statistical reasoning can handle. Deep learning when applied to large image repositories will recognize complex relationships and patterns integrating all properties of the image, which will unlock further connections about the natural history and prognosis of cardiac disease states. The purpose of this review article was to describe the role and current use of automation, machine learning, and AI in echocardiography and discuss potential limitations and challenges of in the future.