# AI for Healthcare

#### NANODEGREE PROGRAM SYLLABUS

## Overview

```

Play a critical role in enhancing clinical decision-making with machine learning to build the treatments of

the future. Learn to build, evaluate, and integrate predictive models that have the power to transform

patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis

and then move on to modeling patient outcomes with electronic health records to optimize clinical trial

testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the

wearer’s pulse rate in the presence of motion.

```

```

A graduate of this program will be able to:

```

- Recommend appropriate imaging modalities for common clinical applications of 2D medical imaging

- Perform exploratory data analysis (EDA) on 2D medical imaging data to inform model training and

explain model performance

- Establish the appropriate ‘ground truth’ methodologies for training algorithms to label medical images

- Extract images from a DICOM dataset

- Train common CNN architectures to classify 2D medical images

- Translate outputs of medical imaging models for use by a clinician

- Plan necessary validations to prepare a medical imaging model for regulatory approval

- Detect major clinical abnormalities in a DICOM dataset

- Train machine learning models for classification tasks using real-world 3D medical imaging data

- Integrate models into a clinician’s workflow and troubleshoot deployments

- Build machine learning models in a manner that is compliant with U.S. healthcare data security and

privacy standards

- Use the TensorFlow Dataset API to scalably extract, transform, and load datasets that are aggregated

at the line, encounter, and longitudinal (patient) data levels

- Analyze EHR datasets to check for common issues (data leakage, statistical properties, missing values,

high cardinality) by performing exploratory data analysis with TensorFlow Data Analysis and Validation

library

- Create categorical features from Key Industry Code Sets (ICD, CPT, NDC) and reduce dimensionality for

high cardinality features

- Use TensorFlow feature columns on both continuous and categorical input features to create derived

features (bucketing, cross-features, embeddings)

- Use Shapley values to select features for a model and identify the marginal contribution for each

selected feature

- Analyze and determine biases for a model for key demographic groups

- Use the TensorFlow Probability library to train a model that provides uncertainty range predictions in

order to allow for risk adjustment/prioritization and triaging of predictions

- Preprocess data (eliminate “noise”) collected by IMU, PPG, and ECG sensors based on mechanical,

physiology and environmental effects on the signal.

- Create an activity classification algorithm using signal processing and machine learning techniques

- Detect QRS complexes using one-dimensional time series processing techniques

- Evaluate algorithm performance without ground truth labels

- Generate a pulse rate algorithm that combines information from the PPG and IMU sensor streams

```

Prerequisites :

Intermediate

Python, and

Experience with

Machine Learning

```

**Flexible Learning** :

Self-paced, so

you can learn on

the schedule that

works best for you.

**Estimated Time** :

4 Months at

15 hours / week

```

Need Help?

udacity.com/advisor

Discuss this program

with an enrollment

advisor.

```

## Course 1: Applying AI to 2D Medical Imaging

## Data

2D imaging, such as X-ray, is widely used when making critical decisions about patient care and accessible by

most healthcare centers around the world. With the advent of deep learning for non-medical imaging data

over the past half decade, the world has quickly turned its attention to how AI could be specifically applied to

medical imaging to improve clinical decision-making and to optimize workflows. Learn the fundamental skills

needed to work with 2D medical imaging data and how to use AI to derive clinically-relevant insights from

data gathered via different types of 2D medical imaging such as x-ray, mammography, and digital pathology.

Extract 2D images from DICOM files and apply the appropriate tools to perform exploratory data analysis

on them. Build different AI models for different clinical scenarios that involve 2D images and learn how to

position AI tools for regulatory approval.

##### Course Project

##### Pneumonia Detection

##### from Chest X-Rays

```

Chest X-ray exams are one of the most frequent and cost-effective

types of medical imaging examinations. Deriving clinical diagnoses

from chest X-rays can be challenging, however, even by skilled

radiologists. When it comes to pneumonia, chest X-rays are the best

available method for point-of-care diagnosis. More than 1 million

adults are hospitalized with pneumonia and around 50,000 die

from the disease every year in the US alone. The high prevalence

of pneumonia makes it a good candidate for the development of a

deep learning application for two reasons: 1) Data availability in a

high enough quantity for training deep learning models for image

classification 2) Opportunity for clinical aid by providing higher

accuracy image reads of a difficult-to-diagnose disease and/or reduce

clinical burnout by performing automated reads of very common

scans. In this project, you will analyze data from the NIH Chest

X-ray dataset and train a CNN to classify a given chest X-ray for the

presence or absence of pneumonia. First, you’ll curate training and

testing sets that are appropriate for the clinical question at hand from

a large collection of medical images. Then, you will create a pipeline

to extract images from DICOM files that can be fed into the CNN for

model training. Lastly, you’ll write an FDA 501(k) validation plan that

formally describes your model, the data that it was trained on, and a

validation plan that meets FDA criteria in order to obtain clearance of

the software being used as a medical device.

```

###### LEARNING OUTCOMES

###### LESSON ONE

```

Introduction to

AI for 2D Medical

Imaging

```

- Explain what AI for 2D medical imaging is and why it is relevant.

###### LESSON TWO

```

Clinical

Foundations of 2D

Medical Imaging

```

- Learn about different 2D medical imaging modalities and their

clinical applications

- Understand how different types of machine learning

algorithms can be applied to 2D medical imaging

- Learn how to statistically assess an algorithm’s performance

- Understand the key stakeholders in the 2D medical imaging

space.

###### LESSON THREE

```

2D Medical Imaging

Exploratory Data

Analysis

```

- Learn what the DICOM standard it is and why it exists

- Use Python tools to explore images extracted from DICOM files

- Apply Python tools to explore DICOM header data

- Prepare a DICOM dataset for machine learning

- Explore a dataset in preparation for machine learning

###### LESSON FOUR

```

Classification

Models of 2D

Medical Images

```

- Understand architectures of different machine learning and

deep learning models, and the differences between them

- Split a dataset for training and testing an algorithm

- Learn how to define a gold standard

- Apply common image pre-processing and augmentation

techniques to data

- Fine-tune an existing CNN architecture for transfer learning

with 2D medical imaging applications

- Evaluate a model’s performance and optimize its parameters

###### LESSON FIVE

```

Translating AI

Algorithms for

Clinical Settings

with the FDA

```

- Learn about the FDA’s risk categorization for medical devices

and how to define an Intended Use statement

- Identify and describe algorithmic limitations for the FDA

- Translate algorithm performance statistics into clinically

meaningful information that can trusted by professionals

- Learn how to create an FDA validation plan

## Course 2: Applying AI to 3D Medical Imaging

## Data

3D medical imaging exams such as CT and MRI serve as critical decision-making tools in the clinician’s

everyday diagnostic armamentarium. These modalities provide a detailed view of the patient’s anatomy and

potential diseases, and are a challenging though highly promising data type for AI applications. Learn the

fundamental skills needed to work with 3D medical imaging datasets and frame insights derived from the

data in a clinically relevant context. Understand how these images are acquired, stored in clinical archives, and

subsequently read and analyzed. Discover how clinicians use 3D medical images in practice and where AI holds

most potential in their work with these images. Design and apply machine learning algorithms to solve the

challenging problems in 3D medical imaging and how to integrate the algorithms into the clinical workflow.

###### LEARNING OUTCOMES

```

LESSON ONE Introduction to

AI for 3D Medical

Imaging

```

- Explain what AI for 3D medical imaging is and why it is

relevant

##### Course Project

##### Hippocampal Volume

##### Quantification in

##### Alzheimer’s Progression

```

Hippocampus is one of the major structures of the human brain

with functions that are primarily connected to learning and

memory. The volume of the hippocampus may change over time,

with age, or as a result of disease. In order to measure hippocampal

volume, a 3D imaging technique with good soft tissue contrast is

required. MRI provides such imaging characteristics, but manual

volume measurement still requires careful and time consuming

delineation of the hippocampal boundary. In this project, you will

go through the steps that will have you create an algorithm that will

help clinicians assess hippocampal volume in an automated way

and integrate this algorithm into a clinician’s working environment.

First, you’ll prepare a hippocampal image dataset to train the U-net

based segmentation model, and capture performance on the test

data. Then, you will connect the machine learning execution code

into a clinical network, create code that will generate reports based

on the algorithm output, and inspect results in a medical image

viewer. Lastly, you’ll write up a validation plan that would help

collect clinical evidence of the algorithm performance, similar to

that required by regulatory authorities.

```

###### LESSON TWO

```

3D Medical

Imaging - Clinical

Fundamentals

```

- Identify medical imaging modalities that generate 3D images

- List clinical specialties who use 3D images to influence clinical

decision making

- Describe use cases for 3D medical images

- Explain the principles of clinical decision making

- Articulate the basic principles of CT and MR scanner operation

- Perform some of the common 3D medical image analysis

tasks such as windowing, MPR and 3D reconstruction

###### LESSON THREE

```

3D Medical

Imaging

Exploratory Data

Analysis

```

- Describe and use DICOM and NIFTI representations of 3D

medical imaging data

- Explain specifics of spatial and dimensional encoding of 3D

medical images

- Use Python-based software packages to load and inspect 3D

medical imaging volumes

- Use Python-based software packages to explore datasets

of 3D medical images and prepare it for machine learning

pipelines

- Visualize 3D medical images using open software packages

###### LESSON FOUR

```

3D Medical

Imaging - Deep

Learning Methods

```

- Distinguish between classification and segmentation

problems as they apply to 3D imaging

- Apply 2D, 2.5D and 3D convolutions to a medical imaging

volume

- Apply U-net algorithm to train an automatic segmentation

model of a real-world CT dataset using PyTorch

- Interpret results of training, measure efficiency using Dice and

Jaccard performance metrics

###### LESSON FIVE

```

Deploying AI

Algorithms in the

Real World

```

- Identify the components of a clinical medical imaging network

and integration points as well as DICOM protocol for medical

image exchange

- Define the requirements for integration of AI algorithms

- Use tools for modeling of clinical environments so that

it is possible to emulate and troubleshoot real-world AI

deployments

- Describe regulatory requirements such as FDA medical device

framework and HIPAA required for operating AI for clinical

care

- Provide input into regulatory process, as a data scientist

## Course 3: Applying AI to EHR Data

```

With the transition to electronic health records (EHR) over the last decade, the amount of EHR data has increased

exponentially, providing an incredible opportunity to unlock this data with AI to benefit the healthcare system.

Learn the fundamental skills of working with EHR data in order to build and evaluate compliant, interpretable

machine learning models that account for bias and uncertainty using cutting-edge libraries and tools including

TensorFlow Probability, Aequitas, and Shapley. Understand the implications of key data privacy and security

standards in healthcare. Apply industry code sets (ICD10-CM, CPT, HCPCS, NDC), transform datasets at different

EHR data levels, and use TensorFlow to engineer features.

```

###### LEARNING OUTCOMES

###### LESSON ONE

```

EHR Data Security

and Analysis

```

- Understand U.S. healthcare data security and privacy best

practices (e.g. HIPAA, HITECH) and how they affect utilizing

protected health information (PHI) data and building

models

- Analyze EHR datasets to check for common issues

(data leakage, statistical properties, missing values, high

cardinality) by performing exploratory data analysis

```

LESSON TWO EHR Code Sets

```

- Understand the usage and structure of key industry code

sets (ICD, CPT, NDC).

- Group and categorize data within EHR datasets using code

sets.

##### Course Project

##### Patient Selection for

##### Diabetes Drug Testing

```

EHR data is becoming a key source of real-world evidence (RWE)

for the pharmaceutical industry and regulators to make decisions

on clinical trials. In this project, you will act as a data scientist

for an exciting unicorn healthcare startup that has created a

groundbreaking diabetes drug that is ready for clinical trial

testing. Your task will be to build a regression model to predict the

estimated hospitalization time for a patient in order to help select/

filter patients for your study. First, you will perform exploratory

data analysis in order to identify the dataset level and perform

feature selection. Next, you will build necessary categorical and

numerical feature transformations with TensorFlow. Lastly, you will

build a model and apply various analysis frameworks, including

TensorFlow Probability and Aequitas, to evaluate model bias and

uncertainty.

```

###### LESSON THREE

```

EHR Transformations

& Feature

Engineering

```

- Use the TensorFlow Dataset API to scalably extract,

transform, and load datasets

- Build datasets aggregated at the line, encounter, and

longitudinal(patient) data levels

- Create derived features (bucketing, cross-features,

embeddings) utilizing TensorFlow feature columns on both

continuous and categorical input features

###### LESSON FOUR

```

Building, Evaluating,

and Interpreting

Models

```

- Analyze and determine biases for a model for key

demographic groups by evaluating performance metrics

across groups by using the Aequitas framework.

- Train a model that provides an uncertainty range with the

TensorFlow Probability library

- Use Shapley values to select features for a model and

identify the marginal contribution for each selected feature

## Course 4: Applying AI to Wearable Device Data

Wearable devices are an emerging source of physical health data. With continuous, unobtrusive monitoring

they hold the promise to add richness to a patient’s health information in remarkable ways. Understand the

functional mechanisms of three sensors (IMU, PPG, and ECG) that are common to most wearable devices

and the foundational signal processing knowledge critical for success in this domain. Attribute physiology

and environmental context’s effect on the sensor signal. Build algorithms that process the data collected by

multiple sensor streams from wearable devices to surface insights about the wearer’s health.

###### LEARNING OUTCOMES

###### LESSON ONE

```

Intro to Digital

Sampling & Signal

Processing

```

- Describe how to digitally sample analog signals

- Apply signal processing techniques (eg. filtering,

resampling, interpolation) to time series signals.

- Apply frequency domain techniques (eg. FFT, STFT,

spectrogram) to time series signals

- Use matplotlib’s plotting functionality to visualize signals

###### LESSON TWO

```

Introduction to

Sensors

```

- Describe how sensors convert a physical phenomenon into

an electrical one.

- Understand the signal and noise characteristics of the IMU

and PPG signals

##### Course Project

##### Motion Compensated

##### Pulse Rate Estimation

```

Wearable devices have multiple sensors all collecting information

about the same person at the same time. Combining these

data streams allows us to accomplish many tasks that would be

impossible from a single sensor. In this project, you will build an

algorithm which combines information from two of the sensors

that are covered in this course -- the IMU and PPG sensors -- that

can estimate the wearer’s pulse rate in the presence of motion.

First, you’ll create and evaluate an activity classification algorithm

by building signal processing features and a random forest model.

Then, you will build a pulse rate algorithm that uses the activity

classifier and frequency domain techniques, and also produces

an associated confidence metric that estimates the accuracy

of the pulse rate estimate. Lastly, you will evaluate algorithm

performance and iterate on design until the desired accuracy is

achieved.

```

**LESSON THREE Activity Classification**

- Perform exploratory data analysis to understand class

imbalance and subject imbalance

- Gain an intuitive understanding signal characteristics and

potential feature performance

- Write code to implement features from literature

- Recognize the danger overfitting of technique (esp.

on small datasets), not simply of model parameters or

hyperparameters

**LESSON FOUR ECG Signal Processing**

- Understand the electrophysiology of the heart at a basic

level

- Understand the signal and noise characteristics of the ECG

- Understand how atrial fibrillation manifests in the ECG

- Build a QRS complex detection algorithm

- Build an arrhythmia detection algorithm from a wearable

ECG signal

- Understand how models can be cascaded together to

achieve higher-order functionality

## Our Classroom Experience

###### REAL-WORLD PROJECTS

```

Build your skills through industry-relevant projects. Get

personalized feedback from our network of 900+ project

reviewers. Our simple interface makes it easy to submit

your projects as often as you need and receive unlimited

feedback on your work.

```

###### KNOWLEDGE

```

Find answers to your questions with Knowledge, our

proprietary wiki. Search questions asked by other students,

connect with technical mentors, and discover in real-time

how to solve the challenges that you encounter.

```

###### STUDENT HUB

```

Leverage the power of community through a simple, yet

powerful chat interface built within the classroom. Use

Student Hub to connect with your fellow students in your

Executive Program.

```

###### WORKSPACES

```

See your code in action. Check the output and quality of

your code by running them on workspaces that are a part

of our classroom.

```

###### QUIZZES

```

Check your understanding of concepts learned in the

program by answering simple and auto-graded quizzes.

Easily go back to the lessons to brush up on concepts

anytime you get an answer wrong.

```

###### CUSTOM STUDY PLANS

```

Preschedule your study times and save them to your

personal calendar to create a custom study plan. Program

regular reminders to keep track of your progress toward

your goals and completion of your program.

```

###### PROGRESS TRACKER

```

Stay on track to complete your Nanodegree program with

useful milestone reminders.

```

## Learn with the Best

### Nikhil Bikhchandani

```

DATA SCIENTIST

AT VERILY LIFE SCIENCES

Nikhil spent five years working with

wearable devices at Google and Verily Life

Sciences. His work with wearables spans

many domains including cardiovascular

disease, neurodegenerative diseases, and

diabetes. Before Alphabet, he earned a

B.S. and M.S. in Electrical Engineering and

Computer Science at Carnegie Mellon.

```

### Mazen Zawaideh

```

RADIOLOGIST

AT UNIVERSITY OF WASHINGTON

Mazen Zawaideh is a Neuroradiology

Fellow at the University of Washington,

where he focuses on advanced diagnostic

imaging and minimally invasive

therapeutics. He also served as a Radiology

Consultant for Microsoft Research for AI

applications in oncologic imaging.

```

### Emily Lindemer

```

DIRECTOR OF DATA SCIENCE &

ANALYTICS AT WELLFRAME

Emily is an expert in AI for both medical

imaging and digital healthcare. She holds

a PhD from Harvard-MIT’s Health Sciences

& Technology division and founded her

own digital health company in the opioid

space. She now runs the data science

division of a digital healthcare company in

Boston called Wellframe.

```

### Ivan Tarapov

```

SR. PROGRAM MANAGER

AT MICROSOFT RESEARCH

At Microsoft Research, Ivan works on robust

auto-segmentation algorithms for MRI and CT

images. He has worked with Physio-Control,

Stryker, Medtronic, and Abbott, where he

helped develop external and internal cardiac

defibrillators, insulin pumps, telemedicine,

and medical imaging systems.

```

## Learn with the Best

### Michael Dandrea

```

PRINCIPAL DATA SCIENTIST

AT GENENTECH

```

```

Michael is on the Pharma Development

Informatics team at Genentech (part of

the Roche Group), where he works on

improving clinical trials and developing

safer, personalized treatments with

clinical and EHR data. Previously, he was

a Lead Data Scientist on the AI team at

McKesson’s Change Healthcare.

```

## All Our Nanodegree Programs Include:

###### EXPERIENCED PROJECT REVIEWERS

```

REVIEWER SERVICES

```

- Personalized feedback & line by line code reviews

- 1600+ Reviewers with a 4.85/5 average rating

- 3 hour average project review turnaround time

- Unlimited submissions and feedback loops

- Practical tips and industry best practices

- Additional suggested resources to improve

###### TECHNICAL MENTOR SUPPORT

```

MENTORSHIP SERVICES

```

- Questions answered quickly by our team of

technical mentors

- 1000+ Mentors with a 4.7/5 average rating

- Support for all your technical questions

###### PERSONAL CAREER SERVICES

```

CAREER COACHING

```

- Personal assistance in your job search

- Monthly 1-on-1 calls

- Personalized feedback and career guidance

- Interview preparation

- Resume services

- Github portfolio review

- LinkedIn profile optimization

## Frequently Asked Questions

PROGRAM OVERVIEW

**WHY SHOULD I ENROLL?**

Artificial Intelligence has revolutionized many industries in the past decade,

and healthcare is no exception. In fact, the amount of data in **healthcare has

grown 20x in the past 7 years** , causing an expected surge in the Healthcare AI

market from **$2.1 to $36.1 billion by 2025** at an annual growth rate of 50.4%. AI

in Healthcare is transforming the way patient care is delivered, and is impacting

all aspects of the medical industry, including early detection, more accurate

diagnosis, advanced treatment, health monitoring, robotics, training, research and

much more.

By leveraging the power of AI, providers can deploy more precise, efficient,

and impactful interventions at exactly the right moment in a patient’s care. In

light of the worldwide COVID-19 pandemic, there has never been a better time

to understand the possibilities of artificial intelligence within the healthcare

industry and learn how you can make an impact to better the world’s healthcare

infrastructure.

###### WHAT JOBS WILL THIS PROGRAM PREPARE ME FOR?

This program will help you apply your Data Science and Machine Learning

expertise in roles including Physician Data Scientist; Healthcare Data Scientist;

Healthcare Data Scientist, Machine Learning; Healthcare Machine Learning

Engineer, Research Scientist, Machine Learning, and more roles in the healthcare

and health tech industries that necessitate knowledge of AI and machine learning

techniques.

###### HOW DO I KNOW IF THIS PROGRAM IS RIGHT FOR ME?

If you are interested in applying your data science and machine learning

experience in the healthcare industry, then this program is right for you.

Additional job titles and backgrounds that could be helpful include Data Scientist,

Machine Learning Engineer, AI Specialist, Deep Learning Research Engineer, and AI

Scientist. This program is also a good fit for Researchers, Scientists, and Engineers

who want to make an impact in the medical field.

ENROLLMENT AND ADMISSION

###### DO I NEED TO APPLY? WHAT ARE THE ADMISSION CRITERIA?

There is no application. This Nanodegree program accepts everyone, regardless of

experience and specific background.

## FAQs Continued

###### WHAT ARE THE PREREQUISITES FOR ENROLLMENT?

To be best prepared to succeed in this program, students should be able to:

Intermediate Python:

- Read, understand, and write code in Python, including language constructs

such as functions and classes.

- Read code using vectorized operations with the NumPy library.

Machine Learning:

- Build a machine learning model for a supervised learning problem and

understand basic methods to represent categorical and numerical features

as inputs for this model

- Perform simple machine learning tasks, such as classification and

regression, from a set of features

- Apply basic knowledge of Python data and machine learning frameworks

(Pandas, NumPy, TensorFlow, PyTorch) to manipulate and clean data for

consumption by different estimators/algorithms (e.g. CNNs, RNNs, tree-

based models).

**IF I DO NOT MEET THE REQUIREMENTS TO ENROLL, WHAT SHOULD I DO?**

To best prepare for this program, we recommend the **AI Programming with

Python Nanodegree program** and the **Deep Learning Nanodegree program** or

the **Intro to Machine Learning with PyTorch Nanodegree program** or the **Intro

to Machine Learning with TensorFlow Nanodegree program**.

TUITION AND TERM OF PROGRAM

**HOW IS THIS NANODEGREE PROGRAM STRUCTURED?**

The AI for Healthcare Nanodegree program is comprised of content and

curriculum to support four projects. Once you subscribe to a Nanodegree

program, you will have access to the content and services for the length of time

specified by your subscription. We estimate that students can complete the

program in four months, working 15 hours per week.

Each project will be reviewed by the Udacity reviewer network. Feedback will be

provided and if you do not pass the project, you will be asked to resubmit the

project until it passes.

**HOW LONG IS THIS NANODEGREE PROGRAM?**

Access to this Nanodegree program runs for the length of time specified in

the payment card on the Nanodegree program overview page. If you do not

graduate within that time period, you will continue learning with month to

month payments. See the **Terms of Use** for other policies around the terms of

access to our Nanodegree programs.

## FAQs Continued

###### CAN I SWITCH MY START DATE? CAN I GET A REFUND?

Please see the Udacity Program **Terms of Use** and **FAQs** for policies on

enrollment in our programs.

SOFTWARE AND HARDWARE

**WHAT SOFTWARE AND VERSIONS WILL I NEED IN THIS PROGRAM?**

For this Nanodegree program, you will need a desktop or laptop computer

running recent versions of Windows, Mac OS X, or Linux and an unmetered

broadband Internet connection. For an ideal learning experience, a computer

with Mac or Linux OS is recommended.

You will use Python, PyTorch, TensorFlow, and Aequitas in this Nanodegree

program.