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Data science as a multidisciplinary field of study. Diagram reprinted with permission from Robert (Bob) Hoyt, MD, FACP, FAMIA, ABPM-CI.
Source publication
The inefficiencies of the healthcare sector continue to be a barrier to achieving the quadruple aim of healthcare quality improvement. The 4th Industrial Revolution has been characterized by rapid transformations due to information technology, data volume, ubiquity, and increased computer processing power. Despite all the promises and hopes of Digi...
Contexts in source publication
Context 1
... science is the term used to describe the scientific study of the creation, validation, and transformation of data to create meaning. [20] It is composed of multiple disciplines like statistics, mathematics, and computer science (Figure 1). Data science is an overarching field that underlies many DH innovations like artificial intelligence (AI), machine learning (ML), deep learning, reinforcement learning, and data mining (Figure 2). ...
Context 2
... science is the term used to describe the scientific study of the creation, validation, and transformation of data to create meaning. [20] It is composed of multiple disciplines like statistics, mathematics, and computer science (Figure 1). Data science is an overarching field that underlies many DH innovations like artificial intelligence (AI), machine learning (ML), deep learning, reinforcement learning, and data mining (Figure 2). ...
Citations
... Unfortunately, sometimes, deep learning models may suffer from overfitting problems, cause high bias because they extract unknown and abstract features, and need high-dimensional datasets to obtain higher performance. [15][16][17] To overcome such problems, some researchers used pretrained transfer learning models to take advantage of the potential of deep learning techniques. [18] For a specific problem, more meaningful and more known features designed can be extracted manually using handcrafted features. ...
Introduction:
The objective of the present study is to classify chest X-ray (CXR) images into COVID-positive and normal categories with the optimal number of features extracted from the images. The successful optimal feature selection algorithm that can represent images and the classification algorithm with good classification ability has been determined as the result of experiments.
Materials and methods:
This study presented a framework for the automatic detection of COVID-19 from the CXR images. To enhance small details, textures, and contrast of the images, contrast limited adaptive histogram equalization was used. Features were extracted from the first-order statistics, Gray-Level Co-occurrence Matrix, Gray-Level Run Length Matrix, local binary pattern, Law's Texture Energy Measures, Discrete Wavelet Transform, and Zernikes' Moments using an image feature extraction tool "pyFeats. For the feature selection, three nature-inspired optimization algorithms, Grey Wolf Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm, were used. For classification, Random Forest classifier, K-Nearest Neighbour classifier, support vector machine (SVM) classifier, and light gradient boosting model classifier were used.
Results and discussion:
For all the feature selection methods, the SVM classifier gives the most accurate and precise result compared to other classification models. Furthermore, in feature selection methods, PSO gives the best result as compared to other methods for feature selection. Using the combination of the SVM classifier with the PSO method, it was observed that the accuracy, precision, recall, and F1-score were 100%.
Conclusion:
The result of the study indicates that with optimal features with the best choice of the classifier algorithm, the most accurate computer-aided diagnosis of CXR can be achieved. The approach presented in this study with optimal features may be utilized as a complementary tool to assist the radiologist in the early diagnosis of disease and making a more accurate decision.
... There has been a paradigm shift in health care stakeholders' goals of quality improvement in recent years, with an emphasis on achieving better outcomes at lower costs, while improving the efficiency of care delivery and prioritizing personalized care [6]. This change, resulting in the use of AI and ML algorithms, has also been driven by regulators and payers demanding high-value care rather than volume-based care, as well as the changing role of patients as consumers [7]. ...
Background: With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety.
... [22] Moreover, AI-based methods are still not included in the evidence-based clinical practices. [23] Such limitations may restrict the use of AI-based method in a small demographic region. In majority of the cases, for COVID-19 detection from X-ray images by computational methods, codes are not available. ...
... There is also a lack of necessary mandate of using AI in clinical application to seek evidence-based medical practices, especially in the era of 4IR. [23] Although in recent time, vaccination initiative has started, several persons are still getting infected even after receiving vaccine. Hence, to start treatment early and to prevent community transmission further, still there is a demand of cheap and easier diagnostic procedure of COVID-19 infection. ...
Purpose:
Many artificial intelligence-based computational procedures are developed to diagnose COVID-19 infection from chest X-ray (CXR) images, as diagnosis by CXR imaging is less time consuming and economically cheap compared to other detection procedures. Due to unavailability of skilled computer professionals and high computer architectural resource, majority of the employed methods are difficult to implement in rural and poor economic settings. Majority of such reports are devoid of codes and ignores related diseases (pneumonia). The absence of codes makes limitation in applying them widely. Hence, validation testing followed by evidence-based medical practice is difficult. The present work was aimed to develop a simple method that requires a less computational expertise and minimal level of computer resource, but with statistical inference.
Materials and methods:
A Fast Fourier Transform-based (FFT) method was developed with GNU Octave, a free and open-source platform. This was employed to the images of CXR for further analysis. For statistical inference, two variables, i.e., the highest peak and number of peaks in the FFT distribution plot were considered.
Results:
The comparison of mean values among different groups (normal, COVID-19, viral, and bacterial pneumonia [BP]) showed statistical significance, especially when compared to normal, except between viral and BP groups.
Conclusion:
Parametric statistical inference from our result showed high level of significance (P < 0.001). This is comparable to the available artificial intelligence-based methods (where accuracy is about 94%). Developed method is easy, availability with codes, and requires a minimal level of computer resource and can be tested with a small sample size in different demography, and hence, be implemented in a poor socioeconomic setting.
... Satu pandangan dari banyak aktivitas data mining skala besar adalah bahwa mereka terutama merupakan pemfilteran dan reduksi data. Meskipun beberapa subdisiplin statistik telah memeriksa kasus khusus dari masalah ini, sebagian besar pekerjaan pada deteksi pola hingga saat ini bersifat komputasi, dengan penekanan pada algoritma (Umar Otokiti, 2022). ...
Crime still occurs in every region in Indonesia. Various classifications of crimes that occur and cause unrest for people in every region in Indonesia. The purpose of this study is to analyze and visualize data on crimes that occur in every region in Indonesia so as to facilitate the Indonesian government in making decisions. The method used for this research is the clustering method. The clustering stage is carried out by grouping data on crimes that occur in Indonesia which are classified from each region. The results of the data that have been visualized show that the crime rate in each area is different according to the classification of crime, so it is necessary to increase security in each area that has a level of criminality data according to the classification of crime.
Background
With an increase in the number of artificial intelligence (AI) and machine learning (ML) algorithms available for clinical settings, appropriate model updating and implementation of updates are imperative to ensure applicability, reproducibility, and patient safety.
Objective
The objective of this scoping review was to evaluate and assess the model-updating practices of AI and ML clinical models that are used in direct patient-provider clinical decision-making.
Methods
We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and the PRISMA-P protocol guidance in addition to a modified CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist to conduct this scoping review. A comprehensive medical literature search of databases, including Embase, MEDLINE, PsycINFO, Cochrane, Scopus, and Web of Science, was conducted to identify AI and ML algorithms that would impact clinical decision-making at the level of direct patient care. Our primary end point is the rate at which model updating is recommended by published algorithms; we will also conduct an assessment of study quality and risk of bias in all publications reviewed. In addition, we will evaluate the rate at which published algorithms include ethnic and gender demographic distribution information in their training data as a secondary end point.
Results
Our initial literature search yielded approximately 13,693 articles, with approximately 7810 articles to consider for full reviews among our team of 7 reviewers. We plan to complete the review process and disseminate the results by spring of 2023.
Conclusions
Although AI and ML applications in health care have the potential to improve patient care by reducing errors between measurement and model output, currently there exists more hype than hope because of the lack of proper external validation of these models. We expect to find that the AI and ML model-updating methods are proxies for model applicability and generalizability on implementation. Our findings will add to the field by determining the degree to which published models meet the criteria for clinical validity, real-life implementation, and best practices to optimize model development, and in so doing, reduce the overpromise and underachievement of the contemporary model development process.
International Registered Report Identifier (IRRID)
PRR1-10.2196/37685
Aim & Objective
This article is aimed to understand the gradual development of cancer systems medicine and how this provides a better therapeutic strategy (in terms of drug selection, dose and duration) and patients care. Hence, this study is focused to understand the need and the evolving nature of the analytical models for the assessment of the outcome of different cancer therapeutics.
Background
Presently, cancer is viewed from a quantitative standpoint; hence, several analytical models on different cancers have developed. From the information of cancer development to therapeutic advantage, mathematical oncology has contributed significantly. With a fewer number of variables, models in this area have successfully synchronized the model output with real-life dynamical data. However, with the availability of large scale data for different cancers, systems biology has gained importance. It provides biomedical insights among a large number of variables. And to get information for clinically relevant variables especially, the controlling variable(s), cancer systems medicine is suggested.
Methods
In this article, we have reviewed the gradual development of the field from mathematical oncology to cancer systems biology to cancer systems medicine. An intensive search with PubMed, IEEE Xplorer and Google for cancer model, analytical model and cancer systems biology was made and the latest developments have been noted.
Results
Gradual development of cancer systems biology entails the importance of the development of models towards a unified model of cancer treatment. For this, the model should be flexible so that different types of cancer and/or its therapy can be included within the same model. With the existing knowledge, relevant variables are included in the same model, followed by simulation studies that will enrich the knowledge base further. Such a deductive approach in the modelling and simulations efforts can help to tackle the adversity of individual cancer cases in future. This approach is indeed important to encompass the fourth industrial revolution in health sector.
Conclusion
Towards the development of a unified modelling effort, a multi-scale modelling approach could be suitable; so that different researchers across the globe can add their contribution to enrich the same model. Moreover, with this, the identification of controlling variables may be possible. Towards this goal, middle-out rationalist approach (MORA) is working on analytical models for cancer treatment.
