Applied ML in Medical Treatment [Ref: TT10093]

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Improve your mastery with the Techtern Applied ML in Medical Prognosis and become job-ready

Patients respond to medical treatment strategies differently based on their prevailing health conditions.  Selecting the right treatment strategy can potentially make the difference between successful patient recovery and complications.  This project portfolio will introduce you d project, I'll recommend treatments more suited to individual patients using data from randomized control trials. And apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, Using natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.

Problem Statement :

Being able to predict the future health of a patient is very critical information both in patient care and treatment strategies. Medical data features nonlinear relationships and this behavior has to be accounted for in building any model that helps predict future health states. Patients respond to medical treatment strategies differently based on their prevailing health conditions. Selecting the right treatment strategy can potentially make the difference between successful patient recovery and complications. 

Improve your mastery with the Techtern Applied ML in Medical Treatment projects portfolio and become job-ready

Aim :

This project portfolio will introduce you to a set of techniques and tools to recommend treatments more suited to individual patients using data from randomized control trials. You will learn to apply machine-learning interpretation methods to explain the decision-making of complex models. You will also learn to implement an NLP-based entity extraction and question-answering methods to automate the task of labeling medical datasets.

Project Contents :

Dataset Information

Image pre-processing and Exploratory Data Analysis (EDA)

Text preprocessing and analytics

Modeling (NLP and ML)

Conclusion

What you will learn :

You will learn to Build a treatment effects predictor, apply model interpretation techniques, and use natural language processing to extract information from radiology reports.

Estimating Treatment Effect Using Machine Learning

Natural Language Entity Extraction

ML Interpretation

Dealing with class imbalance

Data augmentation techniques

Requirements:

Basic knowledge of Python required