Dr. Aadam Quraishi MD's Books

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

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INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

The capacity to understand and have trust in the results generated by models is one of the distinguishing characteristics of high-quality scientific research. Because of the significant impact that models and the outcomes of modeling will have on both our work and our personal lives, it is imperative that we have a solid understanding of models and have faith in the results of modeling. This is something that should be kept in mind by analysts, engineers, physicians, researchers, and scientists in general. Many years ago, picking a model that was transparent to human practitioners or customers often meant selecting basic data sources and simpler model forms such as linear models, single decision trees, or business rule systems. This was the case since selecting a model that was transparent required less processing power. This was the situation as a result of the fact that picking a model that was transparent to human practitioners or customers in general entailed picking a model. Even though these more easy approaches were typically the best option, and even though they continue to be the best option today, they are subject to failure in real-world circumstances in which the phenomena being replicated are nonlinear, uncommon or weak, or very distinctive to particular individuals. Despite the fact that they continue to be the best option, they are sensitive to failure in these kinds of scenarios. The conventional trade-off that existed between the precision of prediction models and the simplicity with which they could be interpreted has been abolished; nevertheless, it is likely that this trade-off was never truly required in the first place. There are technologies that are now accessible that can be used to develop modeling systems that are accurate and sophisticated, based on heterogeneous data and techniques for machine learning, and that can also aid human comprehension of and

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268

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Xoffencerpublication

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ISBN 10

8119534670

ISBN 13

9788119534678

MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

By: Dr. Faisal Alghayadh,Dr. Aadam Quraishi,Mr. Sivasubramanian,Dr. Rabinarayan Patnaik

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MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

By: Dr. Faisal Alghayadh,Dr. Aadam Quraishi,Mr. Sivasubramanian,Dr. Rabinarayan Patnaik

Within the ever-evolving realm of artificial intelligence (AI), the field of Machine Learning Interpretability (MLI) has surfaced as a crucial conduit, serving as a vital link between the intricate nature of sophisticated AI models and the pressing necessity for lucid decision-making procedures in practical scenarios. With the progressive integration of AI systems across various domains, ranging from healthcare to finance, there arises an escalating need for transparency and accountability concerning the operational mechanisms of these intricate models. The pursuit of interpretability in machine learning is of paramount importance in comprehending the enigmatic essence of artificial intelligence. It provides a structured methodology to unravel the intricate mechanisms of algorithms, thereby rendering their outputs intelligible to human stakeholders. The Multimodal Linguistic Interface (MLI) functions as a pivotal conduit, bridging the dichotomous domains of binary machine intelligence and the intricate cognitive faculties of human comprehension. Its primary purpose lies in fostering a mutually beneficial association, wherein the potential of artificial intelligence can be harnessed with efficacy and conscientiousness. The transition from perceiving AI as a "black box" to embracing a more transparent and interpretable framework represents a significant paradigm shift. This shift not only fosters trust in AI technologies but also empowers various stakeholders such as end-users, domain experts, and policymakers. By gaining a deeper understanding of AI model outputs, these stakeholders are equipped to make informed decisions with confidence. In the current epoch characterized by remarkable progress in technology, the importance of Machine Learning Interpretability is underscored as a pivotal element for the conscientious and ethical implementation of AI. This development heralds a novel era wherein artificial intelligence harmoniously interfaces with human intuition and expertise

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239

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Xoffencerpublication

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ISBN 10

8119534913

ISBN 13

9788119534913

MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

By: Bhargavi Posinasetty,Dr. Aadam Quraishi MD,Dr. Vikas Malgotra,Mr. Dayakar Babu Kancherla

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MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

By: Bhargavi Posinasetty,Dr. Aadam Quraishi MD,Dr. Vikas Malgotra,Mr. Dayakar Babu Kancherla

In the discipline of healthcare informatics, the study of how data relevant to healthcare may be obtained, transmitted, processed, stored, and retrieved is known as the study of how data can be gathered, transferred, processed, stored, and retrieved. In this area of study, early sickness prevention, early illness detection, early illness diagnosis, and early illness therapy are all crucial components. The only kinds of data that are regarded credible in the field of healthcare informatics are those that pertain to diseases, patient histories, and the computer operations that are necessary in order to analyze this data. Over the course of the past two decades, conventional medical practices across the United States have made major investments in cutting-edge technical and computational infrastructure in order to enhance their capacity to provide assistance to academics, medical professionals, and patients. There has been a significant investment of resources made in order to improve the level of medical care that may be offered by utilizing these various. The motivation for all of these many programs was the overarching goal of providing patients with access to healthcare that is not only affordably priced and of high quality, but also totally and entirely free of any and all fear. As a direct result of these efforts, the benefits and relevance of employing computational tools to aid with referrals and prescriptions, to set up and maintain electronic health records (EHR), and to make technical improvements in digital medical imaging have been clearer. This is a direct result of the fact that the advantages of applying computational tools have become more obvious. Electronic health records (EHR) are something that can be set up and managed with the assistance of these technologies. It has been demonstrated that computerized physician order entry, more frequently referred to as CPOE, may be able to increase the quality of care that is delivered to patients while

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268

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Xoffencerpublication

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ISBN 10

8119534778

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9788119534777

Book's by Dr. Aadam Quraishi MD

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

MACHINE LEARNING INTERPRETABILITY: EXPLAINING AI MODELS TO HUMANS

MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

MACHINE LEARNING IN HEALTH CARE DIAGNOSIS AND PROGNOSIS

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