Computational Precision Health (COMP HLTH)
COMP HLTH 200A Computational Precision Health Cornerstone (3 Units) Fall
Instructor(s): Adam Yala
Instructor(s): Adam Yala
Prerequisite(s): N/A
Restrictions: First-year CPH graduate students are given priority.
Activities: Lecture, Project
CPH students will develop skills and expertise in both the computational sciences and health sciences. In this three-part course series, students learn to integrate core foundations of computational precision health in small multidisciplinary teams using a Problem Based Learning pedagogical approach. Students will learn core computational foundations including in machine learning and causal inference and they will obtain deep exposure to multiple clinical areas including cancer and cardiology.
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- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: Letter Grade
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? No
COMP HLTH 200B Computational Precision Health Cornerstone (3 Units) Winter
Instructor(s): Ahmed Alaa
Instructor(s): Ahmed Alaa
Prerequisite(s): COMP HLTH 200A
Restrictions: CPH first-year graduate students are given priority
Activities: Lecture, Project
CPH students will develop skills and expertise in both the computational sciences and health sciences. In this three-part course series, students learn to integrate core foundations of computational precision health in small multidisciplinary teams using a Problem-Based Learning pedagogical approach. Students will learn core computational foundations including in machine learning and causal inference and they will obtain deep exposure to multiple clinical areas including cancer and cardiology.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: Letter Grade
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? No
COMP HLTH 200C Computational Precision Health Cornerstone (3 Units) Spring
Instructor(s): Irene Chen
Instructor(s): Irene Chen
Prerequisite(s): COMP HLTH 200A, COMP HLTH 200B
Restrictions: First-year CPH graduate students are given priority
Activities: Lecture, Project
In part 3 of the three-part course series, students will focus on diabetes and ethical machine learning. The students will first learn about modeling chronic diseases including working with time-series data, treatment management, and sequential decision making. Along the way, we'll examine questions of equity and fairness within these questions including access to healthcare, social determinants of health, and trust in the healthcare system.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: Letter Grade
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? No
COMP HLTH 201A CPH Practicum (3 Units) Fall
Instructor(s): Ida Sim
Instructor(s): Ida Sim
Prerequisite(s): COMP HLTH 200A, COMP HLTH 200B, COMP HLTH 200C, or permission of the instructor.
Restrictions: Only CPH students in their 2nd year, or with instructor permission.
Activities: Seminar, Clinical, Lab skills
CPH 201a provides the foundations for understanding and engaging with inpatient and outpatient clinical care. Student will gain deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? Yes
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? No
- Repeat course for credit? No
COMP HLTH 201B CPH Practicum (3 Units) Winter
Instructor(s): Irene Chen
Instructor(s): Irene Chen
Prerequisite(s): COMP HLTH 200A, COMP HLTH 200B, COMP HLTH 200C, COMP HLTH 201A, or permission of the Instructor
Restrictions: CPH students in their 2nd year, or by permission.
Activities: Seminar, Clinical, Lab skills
A 2-semester course series taken during the second year of Computational Precision Health, augmenting the Cornerstone course to provide deep and continuing exposure to the clinical and public health contexts in which CPH advances are to be deployed. Students will have in-depth real world exposure relevant to problem area(s) covered in the problem-based learning core, including clinical, research, and operational work in inpatient, outpatient, community health, and/or public health settings.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? No
- Repeat course for credit? No
COMP HLTH 215 Lab Rotation (2-8 Units) Fall, Winter, Spring, Summer
Instructor(s): Staff
Instructor(s): Staff
Prerequisite(s): Instruction permission.
Restrictions: For first year graduate students in the Joint UCSF/UC Berkeley CPH PhD program.
Activities: Independent Study, Lab science
For first-year CPH graduate students, this course will provide an introduction to experimental methods and research approaches in the different areas of Computational Precision Health. Ten week laboratory rotations spread out over the fall and spring quarters, summer on a needed basis. Research is conducted under the direction of an individual faculty member.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? Yes
- Does enrollment in this course require instructor approval? Yes
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes
COMP HLTH 250 Independent Research (1-9 Units) Fall, Winter, Spring, Summer
Instructor(s): Staff
Instructor(s): Staff
Prerequisite(s): Students working on dissertation research only
Restrictions: Restricted to Computational Precision Health students
Individual research under the supervision of a faculty member.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? Yes
- Does enrollment in this course require instructor approval? Yes
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes
COMP HLTH 270 Computational Precision Health Seminar (3 Units) Fall, Winter, Spring
Instructor(s): Ida Sim, Ahmed Alaa, Adam Yala, Irene Chen
Instructor(s): Ida Sim, Ahmed Alaa, Adam Yala, Irene Chen
Prerequisite(s): None.
Restrictions: Enrollment is limited to students with standing in the Computational Precision Health PhD, accepted students in the Designated Emphasis in Computational Precision Health, or by consent of the instructor.
Activities: Seminar, Discussion
Computational precision health is a rapidly evolving field at the intersection of computational (computer science, data science, statistics) and health sciences (clinical medicine, population health, clinical research). The seminar consists of discussion of recent literature in CPH, guest speakers from across the programs faculty and beyond, presentations by second-year students on work completed during lab rotations, and presentations by third-year students on active dissertation research.
View full course details:
- School: Graduate Division
- Department: Computational Precision Health Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? Yes
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes