DS402: Data Science in Healthcare (remote), Spring Semester 2021
College of Information Sciences and Technology, Pennsylvania State University
Instructors
- Fenglong Ma (lead instructor; fenglong[at]psu.edu)
- Suhas Bettapalli Nagaraj (instructional assistant; suhas[at]psu.edu)
Meeting Times and Locations
- Mondays and Wednesdays 4:00 PM – 5:15 PM (Eastern Time), Zoom
- Fenglong Office Hours: Tuesdays 2:00 – 4:00 PM (Eastern Time), Zoom
- Suhas Office Hours: Wednesdays 8:00 -10:00 AM (Eastern Time), Zoom
Topic Schedule
| Week | Date | Topic | Assigned |
|---|---|---|---|
| 1 | 01/20 | Logistics | |
| 2 | 01/25 | Machine Learning for Healthcare: Regression and Classification | |
| 01/27 | Machine Learning for Healthcare: Clustering | ||
| 3 | 02/01 | Overview of Healthcare Data | |
| 02/03 | Data Imputation | ||
| 4 | 02/08 | Lab1: Data Imputation | Lab 1 |
| 02/10 | Risk Stratification 1 | Project 1 | |
| 02/14 | Due: Lab 1 (11:59 PM) | ||
| 5 | 02/15 | Risk Stratification 2 | |
| 02/17 | Lab 2: Risk Stratification 1 | Lab 2 | |
| 6 | 02/22 | Lab 3: Risk Stratification 2 | |
| 02/24 | Survival Analysis | ||
| 02/28 | Due: Lab 2&3 (11:59 PM) | ||
| 7 | 03/01 | Nature Language Processing 1 | Lab 3 |
| 03/03 | Nature Language Processing 2 | Project2 | |
| 03/05 | Due: Project 1 (11:59 PM) | ||
| 8 | 03/08 | Project 1 Presentation | |
| 03/10 | Project 1 Presentation | ||
| 9 | 03/15 | Nature Language Processing 3 | |
| 03/17 | Lab 4: Medical NLP & PyTorch | Lab 4 | |
| 10 | 03/22 | Lab: LSTM Implementation | |
| 03/23 | Due: Lab 4 (11:59 PM) | ||
| 03/24 | Medical Image Analysis | ||
| 03/26 | Due: Project 2 (11:59 PM) | ||
| 11 | 03/29 | Project 2 Presentation | |
| 03/31 | Project 2 Presentation | Project 3 | |
| 12 | 04/05 | Lab 5: Medical Image Analysis | Lab 5 |
| 04/07 | No Class (Wellness Day) | ||
| 04/11 | Due: Lab 5 (11:59 PM) | ||
| 13 | 04/12 | Advanced CNN models and transfer learning | |
| 04/14 | Interpretability | ||
| 14 | 04/19 | Causal Inference | |
| 04/21 | Fairness | ||
| 04/23 | Due: Project 3 (11:59 PM) | ||
| 15 | 04/26 | Project 3 Presentation | |
| 04/28 | Project 3 Presentation |
Course Texts
- Healthcare Data Analytics, Chandan K. Reddy, Charu C. Aggarwal, 2015. (free online copy)
- Machine Learning, Tom Mitchell, McGraw-Hill, 1997.
- Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006. (free online copy)
- A Course in Machine Learning, Hal Daume III, 2017. (free online copy)
- Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, 2011.
- Introduction to Data Mining (2nd Edition), Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, 2019.
Grading
-
- Class Participation – 5%
- Hands-on Labs – 20% (4% * 5)
- Group-based Projects – 60% (20% * 3)
- Project Presentations – 15% (5% * 3)
- Final grade
- A [93%, 100%]
- A- [90%, 93%)
- B+ [87%, 90%)
- B [83%, 87%)
- B- [80%, 83%)
- C+ [77%, 80%)
- C [70%, 77%)
- D [60%, 70%)
- F [0%, 60%)
The instructor reserves the right to modify the grading scale so as to improve the letter grade if warranted by the circumstances (e.g., unusually high level of difficulty of problem sets).
Expectations
At the completion of this course, students are expected to obtain the following:
- Broad understanding of the principles of advanced machine learning algorithms and their applications in healthcare data analytics
- Capability to identify, formulate and solve exploratory healthcare data analysis and predictive modeling problems that arise in practical applications
- Capability to adapt or combine key elements of existing algorithms to design new algorithms as needed
- Hands-on experience with the applications of several representative algorithms in a high-level programming language (e.g., Python)
- Hands-on experience in participating in online data mining/machine learning competitions (e.g., Kaggle)
Assignment Submission Policy
- Assignments must be TYPED and dropped to proper CANVAS drop boxes
- Students can submit late with the penalty of 25% deduction for every 12 hours late (up to 2 days)
- After 2 days, no more late submission is allowed
Academic Integrity
According to the Penn State Principles and University Code of Conduct: Academic integrity is a basic guiding principle for all academic activity at Penn State University, allowing the pursuit of scholarly activity in an open, honest, and responsible manner. In accordance with the University’s Code of Conduct, you must not engage in or tolerate academic dishonesty. This includes, but is not limited to cheating, plagiarism, fabrication of information or citations, facilitating acts of academic dishonesty by others, unauthorized possession of examinations, submitting work of another person, or work previously used without informing the instructor, or tampering with the academic work of other students. Any violation of academic integrity will be investigated, and where warranted, punitive action will be taken. For every incident when a penalty of any kind is assessed, a report must be filed.
Plagiarism (Cheating): Talking over your ideas and getting comments on your writing from friends are NOT examples of plagiarism. Taking someone else’s words (published or not) and calling them your own IS plagiarism. Plagiarism has dire consequences, including flunking the paper in question, flunking the course, and university disciplinary action, depending on the circumstances of the office. The simplest way to avoid plagiarism is to document the sources of your information carefully.
Project & Lab: When discussing laboratory assignments, you may:
- Discuss the material presented in class or included in assigned readings, documentation, user manual, etc.
- Assist another student in understanding the statement of the problem (e.g., you may assist a non-native speaker by translating some English phrases unfamiliar to that student)
- Discuss high-level ideas about how to complete the lab assignment, including problem specification, general strategies for the solution, strategies for debugging and testing code, etc. without examining code written by other students, or sharing code written by you with other students.
It is expected that you have independently arrived at solutions that you turn in for laboratory assignments. The following are examples of activities that are PROHIBITED:
- Examining, copying of code or code fragments from someone else (including online sources), other than the code that is provided to you by the instructor or included in the reference books.
- Sharing code or code fragments (via email, discussion groups, social media, whiteboard, handwritten or printed copies, etc.)
If a “friend” asks you to show him/her your code (especially if the request is to receive a copy of your code), you are opening the door wide for a possible charge of academic misconduct for both of you. I have seen friendships crumble when student A innocently supplies a copy of his/her code to student B, who then plagiarizes it, getting both in trouble. Do not be an accessory; truly help a friend by saying no. The best source for help on these assignments is the instructor or the teaching assistant. We are experienced in providing the right kind of information and help.
! Warning
- Violation of Academic Integrity policy will result in an automatic F for the concerning submission.
- Two violations ⇒ fail grade in the course
Disability Access Statement
Americans with Disabilities Act: The School of Information Sciences and Technology welcomes persons with disabilities to all of its classes, programs, and events. If you need accommodations or have questions about access to buildings where IST activities are held, please contact us in advance of your participation or visit. If you need assistance during a class, program, or event, please contact the member of our staff or faculty in charge. Access to IST courses should be arranged by contacting the Office of Human Resources, 332 IST Building: (814) 865-8949.
Students with Disabilities: It is Penn State’s policy to not discriminate against qualified students with documented disabilities in its educational programs. (You may refer to the Nondiscrimination Policy in the Student Guide to University Policies and Rules.) If you have a disability-related need for reasonable academic adjustments in this course, contact the Office for Disability Services (ODS) at 814-863-1807 (V/TTY). For further information regarding ODS, please visit the Office for Disability Services Web site at http://equity.psu.edu/ods/.
In order to receive consideration for course accommodations, you must contact ODS and provide documentation (see documentation guidelines at http://equity.psu.edu/ods/guidelines/documentation-guidelines). If the documentation supports the need for academic adjustments, ODS will provide a letter identifying appropriate academic adjustments. Please share this letter and discuss the adjustments with your instructor as early in the course as possible. You must contact ODS and request academic adjustment letters at the beginning of each semester.
Statement on Nondiscrimination & Harassment (Policy AD42)
The Pennsylvania State University is committed to the policy that all persons shall have equal access to programs, facilities, admission and employment without regard to personal characteristics not related to ability, performance, or qualifications as determined by University policy or by state or federal authorities. It is the policy of the University to maintain an academic and work environment free of discrimination, including harassment. The Pennsylvania State University prohibits discrimination and harassment against any person because of age, ancestry, color, disability or handicap, national origin, race, religious creed, sex, sexual orientation, gender identity or veteran status. Discrimination or harassment against faculty, staff or students will not be tolerated at The Pennsylvania State University. You may direct inquiries to the Office of Multicultural Affairs, 332 Information Sciences and Technology Building, University Park, PA 16802; Tel 814-865-0077 or to the Office of Affirmative Action, 328 Boucke Building, University Park, PA 16802-5901; Tel 814-865-4700/V, 814-863-1150/TTY.
For reference to the full policy (Policy AD42: Statement on Nondiscrimination and Harassment): http://guru.psu.edu/policies/AD42.html