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Midterm Project: Ethics in Data Science and Healthcare: An Analytical Project

In this mid-term assignment, you will undertake a comprehensive project that blends the realms of big data analysis and ethical inquiry within the context of a Project Manager within the Data Science field. Your primary task is to develop and implement a predictive model aimed at automating the prediction for the length of stay in a hospital. Recognizing the inherent complexity of such problems, your mission extends beyond mere data analysis – you are entrusted with the responsibility of identifying and scrutinizing potential ethical issues, and crafting informed, thoughtful solutions or frameworks to address these concerns.

Project Objectives:

Analytical Goal: Examine a large healthcare dataset to unearth potential ethical issues, such as privacy concerns, biases, fairness, and the impact on diverse patient groups.

· Are you performing a supervised or unsupervised task?

· What is the difference between predictive and prescriptive analytics?

· What type of segmentations can be performed on this data set?

· What is a regression?

· What is your label in the data set?

· What variables were feature engineering performed on?

· Why is feature engineering important?

· Give an example of two other features that you would transform in the data set beyond what we did together in class and why.

· What is a Dummy variable and what is the dummy variable trap and k-1 Rule.

· Explain the coefficients meanings in relation to length of days stayed in the regression. (We reviewed in class).

· Analyze your data and variables.

· Create some plots and analysis.

· Give and in-depth analysis of the data.

Data Selection and background: MIMIC2 Dataset:

The original data from MIMIC2 (Multiparameter Intelligent Monitoring in Intensive Care) is a deidentified database. This database is a comprehensive collection of health-related data gathered from patients admitted to intensive care units (ICUs) at the Beth Israel Deaconess Medical Center in Boston, Massachusetts, USA.

MIMIC2 contains a wide range of clinical data, including vital signs, laboratory results, medications, procedures, demographic information, and clinical notes. The data is collected from various monitoring devices and electronic health records (EHRs) used in the ICU setting.

The purpose of MIMIC2 is to facilitate research and innovation in critical care medicine and related fields by providing a rich and diverse dataset for analysis and development of predictive models, decision support systems, and other healthcare applications.

Importantly, the data in MIMIC2 has been deidentified to protect patient privacy, meaning that personal identifiers have been removed or masked to ensure confidentiality while still preserving the utility of the data for research purposes.

Download Data:

MIMIC2 - Multiparameter Intelligent Monitoring in Intensive Care

Source: https://www.kaggle.com/datasets/drscarlat/mimic3d?resource=download

Also please find the pre-processed file from class attached.

Introduction and Ethics Overview:

Dive into specific ethical considerations relevant to data science, like privacy, bias, and fairness as well as other findings for example.

Data Exploration and Quality Assessment:

Conduct an in-depth exploration of the MIMIC2 dataset to understand its structure, content, and the context of data collection as mentioned above.

Assess the data's quality, checking for accuracy, completeness, and potential biases.

Ethical Analysis:

Identify and analyze ethical issues within the dataset, using statistical methods to detect biases or disparities.

Tips for REGRESSION IN EXCEL: https://www.youtube.com/watch?v=9wX1a1J4WOI

(performed in class)

Technical Analysis and Ethical Solution Development:

Apply predictive modeling techniques, or business analytics and consider how different approaches may influence ethical outcomes. Propose technical or policy-based solutions to the ethical issues you've identified as PM. We created a regression in class together, please analyze the outputs.

Presentation and Discussion:

Present your findings and solutions in a detailed report documenting your analysis, findings, and proposed solutions, accompanied by a presentation to the class.

Reflection and Broader Implications:

Reflect on the ethical dimensions of your work and consider the role of data scientists/project managers in promoting ethical standards. Discuss the wider implications of your project for the healthcare sector, data science, and societal well-being. Formulate and propose actionable solutions, recommendations, ethical frameworks, or policy recommendations to tackle the identified ethical issues.

Finally, Reflect on the relevance of the current environment with machine learning and AI implementation and course material (ethical frameworks, consequential, deontological approaches, psychological frameworks etc.) Engage in a critical debate on the trade-offs, potential unintended consequences, and broader implications of your solution.

Evaluation Criteria

Your project will be assessed based on the depth and rigor of your ethical analysis, the creativity and feasibility of your solutions, and the clarity and persuasiveness of your presentation.

Peer feedback will be an integral part of the evaluation process, providing you with diverse perspectives on your approach and conclusions.

Resources and Support

You will have access to a variety of resources, including data analysis tools (EXCEL, Python, R, SQL) and a library of ethical frameworks and case studies.

****Summary of Midterm Questions and Expectations for submission: ****

Project Objectives:

Data Exploration and Quality Assessment:

Conduct an in-depth exploration of the MIMIC2 dataset to understand its structure, content, and the context of data collection as mentioned above.

Assess the data's quality, checking for accuracy, completeness, and potential biases.

Analytical Goal: Examine a large healthcare dataset to unearth potential ethical issues, such as privacy concerns, biases, fairness, and the impact on diverse patient groups.

· Are you performing a supervised or unsupervised task?

· What is a regression?

· What is your label in the data set?

· What variables were feature engineering performed on?

· Why is feature engineering important?

· Give an example of two other features that you would transform in the data set beyond what we did together in class and why.

· What is a Dummy variable and what is the dummy variable trap and k-1 Rule.

· Explain the coefficients meanings in relation to length of days stayed in the regression. (We reviewed in class).

· Analyze your data and variables.

· Create some plots and analysis.

· Give and in-depth analysis of the data.

Technical Analysis and Ethical Solution Development:

Apply predictive modeling techniques, or business analytics and consider how different approaches may influence ethical outcomes. Propose technical or policy-based solutions to the ethical issues you've identified as PM. We created a regression in class together, please analyze the outputs of the regression and explain what the outputs mean in relation to length of stay. Is the model a good model?

Write an Essay:

Presentation and Discussion:

Present your findings and solutions in a detailed report documenting your analysis, findings, and proposed solutions, accompanied by a presentation to the class.

Reflection and Broader Implications:

Reflect on the ethical dimensions of your work and consider the role of data scientists/project managers in promoting ethical standards. Discuss the wider implications of your project for the healthcare sector, data science, and societal well-being. Formulate and propose actionable solutions, recommendations, ethical frameworks, or policy recommendations to tackle the identified ethical issues.

Finally, Reflect on the relevance of the current environment with machine learning and AI implementation and course material (ethical frameworks, consequential, deontological approaches, psychological frameworks etc.) Engage in a critical debate on the trade-offs, potential unintended consequences, and broader implications of your solution.

Evaluation Criteria

Your project will be assessed based on the depth and rigor of your ethical analysis, the creativity and feasibility of your solutions, and the clarity and persuasiveness of your presentation.

Peer feedback will be an integral part of the evaluation process, providing you with diverse perspectives on your approach and conclusions.