Praveen Kumar M

Application of Python in the Healthcare Technology

Medical Data Analysis and Developing ML/AI Models

• Broschiertes Buch

Produktbeschreibung

Application of Python in Healthcare Technology: Medical Data Analysis and Developing ML/AI Models provides comprehensive coverage of the Python programming language from a medical-based perspective. With a strong focus on utility-based learning rather than technical-based learning of Python, this title clarifies how the application of Python in the healthcare technology industry has a meaningful impact on patient care through the development of deep learning models for early prediction of diseases and the development of Clinical Decision Support System (CDS). In 13 chapters, an extensive range of topics pertaining to the intersection of Python and medical sciences is covered. Coverage defines how to set up an environment to work with python, describes Python class, methods, and functions, explains both basic and advanced statistical analysis in Python, and provides a tool to assist in the development of deep learning models such as API, Git, Github, NumPy, Pandas, and SciPy.

Produktdetails

• Verlag: Elsevier Health Sciences
• Seitenzahl: 180
• Erscheinungstermin: 1. Dezember 2025
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443219191
• ISBN-10: 0443219192
• Artikelnr.: 68543221

Autorenporträt

Dr Praveen Kumar M is a super-specialist (DM) in Clinical Pharmacology and graduated from Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh. He works as Clinical research manager at Nference, a US based company which aims to make the world's biomedical knowledge computable. He has published more than 75 PubMed index papers with 765 citations.. He is well trained in R, python, Unix and SQL languages and employs it daily.

Inhaltsangabe

1. Requirements to be installed for setting up an environment to work with
python
2. Built-in Data structures in python
3. Python Class, Methods and Functions
4. Analyzing data using Pandas and numpy
5. Basic statistical analysis in Python
6. Advanced statistical analysis in Python
7. Graphical visualization using matplotlib
8. Developing machine learning models using scikit-learn
9. Developing deep learning models using tensorflow
10. Developing image classifier and image identifier model using deep
learning techniques
11. Accessing web using Active Programming Interface (API)
12. Version control system using Git and Github
13. Websites for staying abreast with the latest developments in python