Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-learn, and TensorFlow

Practical AI for Healthcare Professionals

Artificial Intelligence (AI) is a buzzword in the healthcare sphere today. However, notions of what AI actually is and how it works are often not discussed. Furthermore, information on AI implementation is often tailored towards seasoned programmers rather than the healthcare professional/beginner coder. This book gives an introduction to practical AI in the medical sphere, focusing on real-life clinical problems, how to solve them with actual code, and how to evaluate the efficacy of those solutions. You’ll start by learning how to diagnose problems as ones that can and cannot be solved with AI. You’ll then learn the basics of computer science algorithms, neural networks, and when each should be applied. Then you’ll tackle the essential parts of basic Python programming relevant to data processing and making AI programs. The Tensorflow/Keras library along with Numpy and Scikit-Learn are covered as well.

Once you’ve mastered those basic computer science and programming concepts, you can dive into projects with code, implementation details, and explanations. These projects give you the chance to explore using machine learning algorithms for issues such as predicting the probability of hospital admission from emergency room triage and patient demographic data. We will then use deep learning to determine whether patients have pneumonia using chest X-Ray images.

The topics covered in this book not only encompass areas of the medical field where AI is already playing a major role, but also are engineered to cover as much as possible of AI that is relevant to medical diagnostics. Along the way, readers can expect to learn data processing, how to conceptualize problems that can be solved by AI, and how to program solutions to those problems. Physicians and other healthcare professionals who can master these skills will be able to lead AI-based research and diagnostic tool development, ultimately benefiting countless patients.

實用人工智慧給醫療專業人士

人工智慧（AI）是當今醫療領域的熱門詞彙。然而，對於AI的實際定義和運作方式往往未被討論。此外，有關AI實施的資訊通常是針對經驗豐富的程式設計師，而非醫療專業人士或初學者。本書介紹了醫療領域中實用的AI，重點放在實際的臨床問題上，以及如何使用實際的程式碼解決這些問題，以及如何評估這些解決方案的效果。您將首先學習如何將問題診斷為可以或不可以使用AI解決的問題。然後，您將學習計算機科學算法、神經網絡的基礎知識，以及何時應該應用每個算法。然後，您將學習與數據處理和製作AI程序相關的基本Python編程的基本部分。本書還涵蓋了Tensorflow/Keras庫以及Numpy和Scikit-Learn。

一旦您掌握了這些基本的計算機科學和編程概念，您可以深入研究具有代碼、實施細節和解釋的項目。這些項目讓您有機會探索使用機器學習算法解決問題，例如根據急診分流和患者人口統計數據預測住院機率。然後，我們將使用深度學習來確定患者是否患有肺炎，並使用胸部X射線圖像。

本書涵蓋的主題不僅包括AI已在醫療領域中發揮重要作用的領域，還旨在涵蓋與醫學診斷相關的盡可能多的AI。在此過程中，讀者可以期望學習數據處理、如何概念化可以通過AI解決的問題以及如何編程解決這些問題的方法。能夠掌握這些技能的醫生和其他醫療專業人士將能夠引領基於AI的研究和診斷工具的開發，最終造福無數患者。

Abhinav “Abhi” Suri is a current medical student at the UCLA David Geffen School of Medicine. He completed his undergraduate degree at the University of Pennsylvania with majors in Computer Science and Biology. He also completed a Masters in Public Health (in Epidemiology) at Columbia University Mailman School of Public Health. Abhihas been dedicated to exploring the intersection between computer science and medicine. As an undergraduate, he carried out and directed research on deep learning algorithms for the detection of vertebral deformities and the detection of genetic factors that increase risk of COPD. His public health research focused on opioid usage trends in NY State and the development/utilization of geospatial dashboards for monitoring demographic disease trends in the COVID-19 pandemic.

Outside of classes and research, Abhi is an avid programmer and has made applications that address healthcare worker access in Tanzania, aid the discovery process for anti-wage theft cases, and facilitate access to arts classes in underfunded school districts. He also developed (and currently maintains) a popular open-source repository, Flask-Base, which has over 2,000 stars on Github. He also enjoys teaching (lectured a course on JavaScript) and writing. So far, his authored articles and videos have reached over 200,000 people across a variety of platforms.

Abhinav "Abhi" Suri是目前就讀於UCLA David Geffen School of Medicine的醫學生。他在賓夕法尼亞大學獲得了計算機科學和生物學的學士學位，並在哥倫比亞大學Mailman公共衛生學院獲得了流行病學的公共衛生碩士學位。Abhi一直致力於探索計算機科學和醫學之間的交叉領域。在本科期間，他進行並指導了有關深度學習算法用於檢測脊柱畸形和檢測增加COPD風險的基因因素的研究。他的公共衛生研究集中在紐約州的阿片類藥物使用趨勢以及開發/利用地理空間儀表板監測COVID-19大流行中的人口疾病趨勢。

在課程和研究之外，Abhi是一位熱衷的程式設計師，開發了應用程式，解決坦桑尼亞醫療工作者的訪問問題，協助發現反工資盜竊案件的過程，並促進在資金不足的學區中獲得藝術課程的機會。他還開發（並目前維護）一個受歡迎的開源存儲庫Flask-Base，在Github上擁有超過2,000個星星。他還喜歡教學（曾講授過JavaScript課程）和寫作。到目前為止，他撰寫的文章和視頻已經在各種平台上觸及了超過200,000人。