📘 Course 2: Clinical Data #

🧭 Module 1: Asking and Answering Questions via Clinical Data Mining #

1. What’s the Problem?
Clinicians and researchers have important questions but lack a structured approach to answering them using clinical data.

2. Why Does It Matter?
Without a systematic workflow, decisions may rely on anecdotal evidence or outdated knowledge, leading to suboptimal care.

3. What’s the Core Idea?
The 4-step clinical data mining workflow: (1) Ask the right question → (2) Find suitable data → (3) Extract/transform data → (4) Analyze and iterate.

4. How Does It Work?
Start with a real clinical scenario, define inclusion/exclusion criteria, search EMRs using codes/tests, and compute outcomes. Use a timeline and patient-feature matrix to support decisions.

5. What’s Next?
This foundation enables accurate data selection (Module 2), temporal modeling (Module 3), and building datasets (Module 4).

🏥 Module 2: Data Available from Healthcare Systems #

1. What’s the Problem?
Healthcare data is fragmented, inconsistently coded, and filled with biases and errors.

2. Why Does It Matter?
Using flawed or incomplete data without understanding its origin can lead to misleading conclusions or unsafe decisions.

3. What’s the Core Idea?
Categorize and understand different healthcare data types, sources, and their limitations, including EMR, claims, registries, and patient-generated data.

4. How Does It Work?
Study the roles of key actors (patients, providers, payers), structured vs. unstructured data types, and typical biases (selection, misclassification, incentives).

5. What’s Next?
Provides the context for building timelines (Module 3) and feature matrices (Module 4) while recognizing biases that need correction.

🕰️ Module 3: Representing Time in Clinical Data #

1. What’s the Problem?
Most databases don’t represent or reason well about time, yet clinical reasoning depends heavily on event timing.

2. Why Does It Matter?
Incorrect ordering or missing timestamps can invalidate exposure-outcome relationships and confuse chronic vs. acute processes.

3. What’s the Core Idea?
Use patient timelines and time-aware logic to represent, bin, and reason about clinical events over time.

4. How Does It Work?
Define index times, use bins to aggregate events, calculate time-to-event, handle censoring, and test for non-stationarity.

5. What’s Next?
Establishes the temporal framework needed for building structured datasets (Module 4) and modeling disease progression (Module 6).

🧱 Module 4: Creating Analysis-Ready Datasets #

1. What’s the Problem?
Raw timelines are complex and inconsistent — they can’t be directly used in analysis or machine learning.

2. Why Does It Matter?
Poor feature engineering or ignoring missingness leads to weak, biased, or uninterpretable models.

3. What’s the Core Idea?
Build a patient-feature matrix by selecting, cleaning, imputing, and engineering features from structured/unstructured data.

4. How Does It Work?
Standardize features, reduce dimensionality, handle missingness with imputation or removal, and use domain knowledge or PCA to create meaningful features.

5. What’s Next?
Feeds directly into downstream modeling, classification (Module 6), and cohort identification with better interpretability.

📄 Module 5: Handling Unstructured Data #

1. What’s the Problem?
Valuable clinical information is trapped in unstructured formats like notes, images, and signals.

2. Why Does It Matter?
Failing to extract this information limits your ability to detect key conditions, traits, or outcomes that are not coded elsewhere.

3. What’s the Core Idea?
Use text mining, signal processing, and image interpretation to turn unstructured data into usable features.

4. How Does It Work?
Apply NLP (e.g., negation/context detection), use knowledge graphs for term recognition, and process signals/images with appropriate tools.

5. What’s Next?
Enhances the patient-feature matrix (Module 4) and improves phenotyping accuracy and completeness (Module 6).

🧬 Module 6: Electronic Phenotyping #

1. What’s the Problem?
Identifying who truly has a disease or condition is challenging using only raw or coded data.

2. Why Does It Matter?
Misclassified patients lead to invalid cohorts, incorrect inferences, and flawed clinical decisions or model training.

3. What’s the Core Idea?
Define phenotypes using rule-based or probabilistic methods to accurately identify conditions of interest.

4. How Does It Work?
Use inclusion/exclusion criteria (rule-based) or train classifiers (probabilistic) with anchors, weak labels, and features from Modules 4–5.

5. What’s Next?
Enables reliable cohort creation for clinical trials, observational studies, and AI/ML applications.

⚖️ Module 7: Clinical Data Ethics #

1. What’s the Problem?
Using patient data without safeguards risks violating privacy, losing trust, and causing harm.

2. Why Does It Matter?
Unethical data use can lead to legal issues, exclusion of vulnerable groups, and poor public perception of healthcare AI.

3. What’s the Core Idea?
Apply ethical frameworks like the Belmont Report and Learning Health System to govern data use, consent, and fairness.

4. How Does It Work?
Ensure de-identification, obtain proper consent (or waiver), handle return of results thoughtfully, and consider justice in access and outcomes.

5. What’s Next?
Provides ethical boundaries and practices for applying all previous modules responsibly in real-world systems.