AI for Healthcare with Keras and Tensorflow 2.0: Design, Develop, and Deploy Machine Learning Models Using Healthcare Data

Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries.
This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q&A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask.
By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry.

What You Will Learn

• Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies
• Look at different problem areas within the healthcare industry and solve them in a code-first approach
• Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks
• Understand the industry and learn ML

Who This Book Is For
Data scientists and software developers interested in machine learning and its application in the healthcare industry

這本書將透過TensorFlow 2.0和其他機器學習（ML）庫的真實案例研究，介紹人工智慧對醫療生態系統的影響。書籍首先解釋了醫療市場的動態，包括醫療專業人員、患者和支付者等利益相關者的角色。然後進入案例研究。案例研究從電子健康記錄（EHR）數據開始，介紹了如何在處理任何下游任務時使用多任務設置來考慮子人群。您還將嘗試使用相同的數據預測ICD-9代碼。您將研究Transformer模型，並了解在使用聯邦學習時將現代機器學習技術應用於高度敏感的醫療數據時面臨的挑戰。您將研究在低訓練數據環境中使用的半監督方法，這在醫療等專業領域中經常出現。您將介紹高級主題的應用，例如圖卷積網絡，以及在使用2D和3D醫學影像時如何開發和優化圖像分析流程。最後一節將向您展示如何使用重新排序、強大的問答設置等方法來構建和設計一個封閉領域的問答系統。最後，在討論網絡和服務器技術如何使擴展和部署變得容易之後，將使用Flask和Docker部署一個機器學習應用程序供全世界觀看。
通過閱讀本書，您將清楚了解醫療系統的運作方式，以及如何將機器學習和深度學習工具和技術應用於醫療行業。
您將學到什麼

• 全面、清晰且詳盡地介紹與案例研究相關的算法和技術


• 從代碼優先的角度探索和解決醫療行業中的不同問題領域


• 了解和探索高級主題，如多任務學習、Transformer和圖卷積網絡


• 了解行業並學習機器學習

適合閱讀對象
對機器學習及其在醫療行業中應用感興趣的數據科學家和軟件開發人員。

Anshik has a deep passion for building and shipping data science solutions that create great business value. He is currently working as a senior data scientist at ZS Associates and is a key member on the team developing core unstructured data science capabilities and products. He has worked across industries such as pharma, finance, and retail, with a focus on advanced analytics. Besides his day-to-day activities, which involve researching and developing AI solutions for client impact, he works with startups as a data science strategy consultant. Anshik holds a bachelor's degree from Birla Institute of Technology & Science, Pilani. He is a regular speaker at AI and machine learning conferences. He enjoys trekking and cycling.

Anshik 是一位對於建立和推出能夠創造極大商業價值的數據科學解決方案充滿熱情的人。他目前在 ZS Associates 擔任高級數據科學家，是開發核心非結構化數據科學能力和產品的團隊中的重要成員。他在製藥、金融和零售等多個行業工作過，專注於高級分析。除了日常的工作，他還作為數據科學策略顧問與初創企業合作。Anshik 擁有比爾拉理工學院的學士學位。他經常在人工智能和機器學習的會議上發表演講。他喜歡徒步旅行和騎自行車。