Data Collection for Machine Learning

 

Assignment 1B: Data Collection for Machine Learning Model

• Two multi-part, multiple-choice questions.

• Details of the Q1 & Q2 m/c questions are shown in the attached question sheet.

• Lecture notes on Machine Learning in Healthcare for your reference

Phase 1 Data Collection

· Describe the opportunities and challenges for utilization of clinical data.

· Apply the framework for conceptualizing data usage in healthcare.

·
Select the correct answer with font size 14 with an explanation in 2 or 3 sentences

Q 1

Part 1.

How are images commonly represented when given to a deep learning model?

As a 1-dimensional vector, where each number is a hand-picked feature

As layers of number grids, where each number is pixel intensity

As a sequence of 1-dimensional vectors, where each number is pixel intensity

As a 1-dimensional vector, where each number is pixel intensity

Part 2.

What deep neural network architecture is most commonly used for image classification?

Recurrent Neural Network (RNN)

Multi-layer Perceptron (MLP)

Generative Adversarial Network (GAN)

Convolutional Neural Network (CNN)

Part 3.

What is the kind of question being answered via the COVID detector?

Multi-label classification

Binary classification

Sequence-to-sequence translation

Linear regression

Part 4.

You are interested in further leveraging hospital resources in order to boost the performance of your COVID detector. Which of the following actions would improve the likelihood of a high performing model?
Check all that apply.

Giving the machine learning team segmentation labels for a small subset of the COVID chest x-ray dataset.

Giving the machine learning team access to an existing COVID detector.

Giving the machine learning team a large dataset of chest x-rays, even if they do not originate from COVID-positive patients.

Giving the machine learning team the text reports associated with each of the COVID chest x-ray examinations.

Q 2

Part 1.

How can we represent a patient’s electronic health record, a form of structured data, to a machine learning model?

As layers of number grids, where each number is pixel intensity

As a 1-dimensional vector, where each number is a hand-picked feature

As a 1-dimensional vector, where each number is pixel intensity

As a sequence of 1-dimensional vectors, where each number is pixel intensity

Part 2.

Given the type of data available, which of the following are reasonable alternative framings of task at hand, from a machine learning perspective?
Check all that apply.

A regression model that predicts the patient’s date of death.

A model that predicts the number of days before a patient requires invasive mechanical ventilation. This model would be trained only on patients who required invasive mechanical ventilation.

A binary classification model that predicts whether or not the patient will require hospitalization.

A model that predicts what range of days it will take for a patient to require invasive mechanical ventilation. The 4 categories include: [“0-4 days”, “5-9 days”, “10-14 days”, “14+ days or will not need one”]

Part 3.

Given that we are training a model to predict whether or not the patient requires invasive mechanical ventilation, which of these values should NOT be passed into the model as a feature?
Check all that apply.

Ferritin

Invasive mechanical ventilation date

Patient birth date

D-DIMER

White Blood Cell count

Ventilator setting

Patient inpatient arrival date

Part 4.

Imagine the path that the patient data took through the healthcare system. What are some possible errors that might have gotten introduced to the data before it was published?
Check all that apply.

The patient was a recent transfer from another system

The patient comes to ED and gets immediately intubated, thus no labs are provided

Labs are logged AFTER the invasive mechanical ventilation

The patient had been to the hospital multiple times

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Class notes on Machine Learning and AI in a healthcare setting

Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system — such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.

In this course, we’ll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. Here is a list of the learning objectives for a quick reference.

1) Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system.

This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements. We will also discuss the challenges of healthcare cost management, quality of care, and access to care. While the course focuses on the U.S. healthcare system, we will also refer to healthcare systems in other developed countries.

AI in healthcare use case: Natural language processing

When subject matter experts help train AI algorithms to detect and categorize certain data patterns that reflect how language is actually used in their part of the health industry, this natural language processing (NLP) enables the algorithm to isolate meaningful data. This helps decision-makers with the information they need to make informed care or business decisions quickly.

Healthcare payers

For healthcare payers, this NLP capability can take the form of a virtual agent using conversational AI to help connect health plan members with personalized answers at scale. 

View the resource

.

Government health and human service professionals

For government health and human service professionals, a case worker can use AI solutions to quickly mine case notes for key concepts and concerns to support an individual’s care.

Clinical operations and data managers

Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding, which can help reduce the cycle time to start, amend, and manage clinical studies.

2) This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.Only by training AI to correctly perceive information and make accurate decisions based on the information provided, can you ensure your AI will perform the way it’s intended.

3 Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.

This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.

4 With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

5 This last course includes a project that takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient’s journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make — such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline — affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner.

6 How does AI training work?

AI training starts with data. While the actual size of the dataset needed is dependent on the project, all machine learning projects require high-quality, well-annotated data in order to be successful. It’s the old GIGO rule of computer science — garbage in, garbage out. If you train your AI using poor-quality or incorrectly tagged data, you’ll end up with poor-quality AI.

Once the quality assurance phase is complete, the AI training process has three key stages:

1. Training

2. Validation

3. Testing

Keys to successful AI training

You need three ingredients to train AI well: high-quality data, accurate data annotation, and a culture of experimentation.

High-quality data

Bad data skews AI’s judgment and produces undesirable results. It can even create AI that is biased.

Accurate data annotation

Not only do you need to have plenty of high-quality data, but you must also accurately annotate it. Otherwise, your AI will have no contextual guidance to help it properly interpret the data, let alone learn from it. For example, correctly annotated images can help teach AI programs to tell the difference between suspected skin cancer and benign birthmarks.

A culture of experimentation

7 Why is it important to evaluate your machine learning algorithm?

Evaluating your machine learning algorithm is an essential part of any project. Your model may give you satisfying results when evaluated using a metric say accuracy score but may give poor results when evaluated against other metrics such as logarithmic loss or any other such metric.

Choose an evaluation metric depending on your use case. Different metrics work better for different purposes. Selecting the appropriate metrics also allows you to be more confident in your model when presenting your data and findings to others. On the flip side, using the wrong evaluation metric can be detrimental to a machine learning use case. A common example is focusing on accuracy, with an imbalanced dataset.

Healthcare privacy is a central ethical concern involving the use of big data in healthcare, with vast amounts of personal information widely accessible electronically.

Medical records and prescription data are being used, and even sold, for a variety of purposes. As long as it’s de-identified, patients’ permission isn’t needed. However, some medical ethicist argues, “There is a need for sound regulation to guide and oversee this brave new world of algorithmic-based healthcare.”