Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-learn, and TensorFlow
暫譯: 醫療專業人員的實用人工智慧：使用 Numpy、Scikit-learn 和 TensorFlow 的機器學習

Suri, Abhinav

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

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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.

商品描述(中文翻譯)

實用的醫療專業人員人工智慧

人工智慧（AI）如今在醫療領域中是一個熱門詞彙。然而，關於AI實際上是什麼以及它如何運作的概念，往往並未被深入討論。此外，關於AI實施的信息通常是針對經驗豐富的程式設計師，而非醫療專業人員或初學者編碼者。本書介紹了醫療領域中的實用AI，重點關注現實生活中的臨床問題、如何用實際代碼解決這些問題，以及如何評估這些解決方案的有效性。您將首先學習如何將問題診斷為可以用AI解決的問題和無法用AI解決的問題。接著，您將學習計算機科學算法、神經網絡的基本知識，以及何時應該應用它們。然後，您將處理與數據處理和製作AI程序相關的基本Python編程的必要部分。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，您就可以深入進入包含代碼、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來解決問題，例如根據急診室分診和患者人口統計數據預測住院的概率。然後，我們將使用深度學習來判斷患者是否患有肺炎，這將使用胸部X光影像。

本書涵蓋的主題不僅包括AI已經在醫療領域中發揮重要作用的領域，還旨在涵蓋與醫療診斷相關的AI的盡可能多的內容。在這個過程中，讀者可以期待學習數據處理、如何概念化可以用AI解決的問題，以及如何編程解決這些問題的方案。掌握這些技能的醫生和其他醫療專業人員將能夠引領基於AI的研究和診斷工具的開發，最終使無數患者受益。

作者簡介

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.

作者簡介(中文翻譯)

Abhinav “Abhi” Suri 是加州大學洛杉磯分校大衛·格芬醫學院的現任醫學生。他在賓夕法尼亞大學完成了計算機科學和生物學的本科學位，並在哥倫比亞大學梅爾曼公共衛生學院獲得公共衛生碩士學位（流行病學）。Abhi 一直致力於探索計算機科學與醫學之間的交集。作為本科生，他進行並指導了有關深度學習算法的研究，該算法用於檢測脊椎畸形以及檢測增加慢性阻塞性肺病（COPD）風險的遺傳因素。他的公共衛生研究集中在紐約州的鴉片類藥物使用趨勢以及在 COVID-19 大流行期間開發/利用地理空間儀表板來監測人口疾病趨勢。

在課堂和研究之外，Abhi 是一位熱衷的程序員，開發了幾個應用程序，解決坦尚尼亞醫療工作者的接入問題，幫助反對工資盜竊案件的發現過程，並促進資金不足的學區的藝術課程接入。他還開發並目前維護一個受歡迎的開源庫 Flask-Base，該庫在 Github 上擁有超過 2,000 顆星。他也喜歡教學（曾講授 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超過 200,000 人。

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