opportunities and challenges for utilization of clinical data.

Phase 1 Data Collection

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

· Apply the framework for conceptualizing data usage in healthcare.

Q 1 (Select the correct answer with an explanation in 2 or 3 sentences)

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 Data Collection ( check the correct answer)

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

Describe the opportunities and challenges for utilization of clinical data.

Introduction

Clinical data is a type of data that can be used to improve current treatments. Clinical data refers to information about patients’ health and the results of their treatments, including medical records, patient surveys, medical tests and procedures performed on an individual patient. Clinical data may contain errors but these can be detected and corrected for a more accurate picture of health trends in an organization or industry.

Data can be structured and organized in different ways making it easier to mine and use.

Data can be structured and organized in different ways making it easier to mine and use. Data can be structured using a variety of formats, including relational databases, flat files, XML files and JSON documents. The structure of your data will determine how you access it, store it and analyze its contents.

In this section we’ll discuss some general issues that arise when dealing with clinical data such as annotations (comments), versioning or privacy concerns:

Clinical data can be used to develop new drugs and diagnostics.

Clinical data can be used to develop new drugs and diagnostics.

• Drug delivery: Clinical data can be used to improve drug delivery, for example by using imaging methods such as ultrasound or magnetic resonance imaging (MRI) or by tailoring molecules for a specific patient’s physiology. For example, one study found that adding a molecule called N-acetylaspartylglutamate (NAG) to an albumin-based formulation increased its concentration in the blood stream of mice by almost 50%. This could lead to better treatment outcomes because it allows more of the medicine to reach its target tissues within the body than does conventional drug administration techniques such as intravenous injection into muscle tissue or intramuscular injection through veins near muscles.

Clinical data can provide insights into the effectiveness of treatments.

Clinical data can be used to improve the effectiveness of treatments. For example, clinical data could be used to develop new drugs and diagnostics that target specific disease subtypes. In addition, this information can help in developing new treatments for patients with specific conditions or outcomes that were not previously understood.

Clinical data also has the potential to improve patient outcomes and reduce costs associated with their treatment programs by identifying appropriate targets for interventions based on individualized risk profiles or other biological markers that may predict poor outcomes.

Clinical data can be used to improve patient outcomes and reduce costs.

Clinical data can be used to improve patient outcomes, reduce costs and improve the quality of care. It can also be used to increase efficiency, safety and management of healthcare services.

Clinical data is information about patients that has been collected during their interaction with healthcare providers. This includes:

• Medical history (previous medical conditions)
• Physical examination findings (including lab results)
• Diagnostic tests performed on patients like X-rays or bloodwork tests for example

Clinical data may contain errors, but these can be detected and corrected for a more accurate picture of health trends.

Clinical data can be used to detect and correct errors in clinical data. The term “error” is used here to refer to any inaccuracy in a test or measurement, whether caused by human error or due to equipment malfunction. Errors can occur when a person’s answers are not consistent with what they actually said during the survey, or when there was an error in recording the results of tests on different days.

When it comes to clinical studies, many researchers believe that machine learning will be key for detecting and correcting errors in their data sets because it allows computers to learn how best predict outcomes based on previous results (machine learning), rather than relying solely on humans who may not have experience interpreting these kinds of findings correctly all the time!

With the help of clinical data, we can create a better picture of health trends and improve current treatments

Clinical data can be used to develop new drugs and diagnostic tests. Drug development is a challenging process, as there are many factors that affect the effectiveness of a drug. Clinical data can help in identifying these factors, which will increase the chances for success for new medications. In addition, clinical data may provide insights into the effectiveness of treatments for different diseases or conditions.

Clinical research studies often use patient-reported outcomes (PRO) as an outcome measure because they are easy to collect through surveys rather than measuring biomarkers or laboratory tests on tissue samples.[11] However, PRO’s have been criticized for lack of validity due to subjective nature of responses.[12] However, PRO’s have been found useful when combined with other measures like biological markers or imaging techniques such as CT scans.[13] For example:

• A study showed that patients who had lower levels of inflammation were less likely than others with higher inflammation levels at baseline

Conclusion

The key takeaway here is that clinical data can help us create a more accurate picture of health trends. By using these insights, we can improve current treatments and create new drugs and diagnostics. Clinical data also provides insights into the effectiveness of treatments and reduces costs for both patients and healthcare organizations alike.