Module3: Representing Time Timing Events For Clinical Data Mining #
1 Time, timelines, timescales and representations of time #
Q: Why is it useful to place patient events on a timeline? #
A: Timelines integrate diverse patient data sources, helping visualize when each event occurred, enabling analysis of sequence and duration.
Q: What are two key reasons time matters in healthcare data? #
A:
- Patient age: Impacts diagnosis, treatment decisions, metabolism, and insurance access.
- Event order: Helps infer causality — exposures should precede outcomes.
Q: How do timescales vary in medical questions? #
A: Medical events can span milliseconds (e.g., EKG signals), days (e.g., symptom onset), or decades (e.g., chronic disease progression), requiring careful scale selection.
➡️ How do we choose relevant units of time in clinical analysis?
2 Timescale: Choosing the relevant units of time #
Q: Why is timescale selection important in healthcare data? #
A: Because relevant time units in clinical contexts can range from milliseconds to decades, depending on the nature of disease, technology, and healthcare system organization.
Q: What factors influence the appropriate timescale? #
A:
- The biological process (e.g., acute vs chronic disease)
- Measurement resolution of available technology
- Clinical workflow and timing of interventions
Q: How can timescales vary in practice? #
A: Heart rate variability may be tracked in milliseconds, while cancer progression may be observed over years or decades.
➡️ What influences the choice of timescale in a clinical study?
3 What affects the timescale #
Q: What determines the appropriate timescale in clinical analysis? #
A: Two main factors:
- The research question being asked
- The type of data being analyzed
Q: How do data types influence timescale? #
A:
- Lab tests may be relevant over days to weeks.
- Diagnoses may span days to a lifetime.
- Procedures could have immediate or long-term effects.
Q: How do disease types affect timescale? #
A:
- Acute diseases (e.g., flu) involve short timelines.
- Chronic diseases (e.g., diabetes) require long-term tracking.
Q: Why is this interaction important? #
A: It informs feature selection, granularity, and the scope of data needed for analysis.
➡️ How is time represented in clinical datasets?
4 Representation of time #
Q: Why is time representation important in healthcare data? #
A: Because clinical timelines must eventually be converted into a structured format — typically a patient-feature matrix — for analysis.
Q: What is a patient-feature matrix? #
A: A table where each row represents a patient, and each column captures a feature (e.g., diagnosis, lab result, blood pressure).
Q: Where does time representation come into play? #
A: It influences how temporal data from a patient’s timeline are encoded into the matrix — requiring different formats and strategies depending on the use case.
➡️ How do time series differ from non-time series data in clinical contexts?
5 Time series and non-time series data #
Q: What is a time series in healthcare data? #
A: A set of measurements sampled at regular intervals, usually of the same type (e.g., continuous EKG signals).
Q: Where are time series especially common in clinical settings? #
A: In intensive care units (ICUs), where patient vitals are continuously monitored via sensors.
Q: What kind of methods are used to analyze time series? #
A: Techniques from signal processing and electrical engineering.
Q: How is non-time series data different? #
A: Most clinical data are sampled irregularly, based on clinical need (e.g., labs, vitals), not clock time — requiring different representation methods.
➡️ How is the order of events captured and why does it matter?
6 Order of events #
Q: Why is the order of clinical events important? #
A: It allows researchers to reason about relationships between conditions, treatments, and outcomes (e.g., “Did condition A precede condition B?”).
Q: What makes reasoning about event order complex? #
A: When events span time intervals (e.g., chronic illnesses), we must consider overlaps and relative start/end times — not just simple time points.
Q: What are different ways to interpret ‘A before B’? #
A:
- A ends before B starts (no overlap)
- A starts before B starts (may overlap)
- A and B occur simultaneously (partial or full overlap)
➡️ How is time represented implicitly in healthcare data?
7 Implicit representations of time #
Q: What is an implicit representation of time in clinical data? #
A: It involves ignoring exact timestamps and instead summarizing events over defined intervals (e.g., event counts in time bins).
Q: What is binning in this context? #
A: Dividing the patient timeline into intervals (bins) and counting occurrences of events within each bin. These counts become features in the analysis matrix.
Q: What are key decisions in binning? #
A:
- Number and size of bins
- Interval granularity based on the clinical question’s timescale
- Whether to treat each bin as a separate feature or summarize them
Q: What is the benefit of implicit time representation? #
A: It simplifies complex event timelines into structured, analyzable data while preserving temporal context.
➡️ What are the different ways to place data into time bins?
8 Different ways to put data in bins #
Q: What are common ways to summarize data within time bins? #
A:
- Count of events
- Binary indicators (e.g., presence vs. absence)
- Aggregates like average, maximum, or most recent value
Q: How do you choose the binning method? #
A: It depends on the clinical item being measured and the nature of the research question.
Q: Can you give a practical example? #
A: For monitoring diabetes, the HBA1C lab test reflects average glucose levels over months. Thus, a single value may suffice instead of multiple timestamped entries.
➡️ How do we consider the timing of exposures and outcomes in analysis?
9 Timing of exposures and outcomes #
Q: What is a cohort in clinical data analysis? #
A: A group of patients meeting certain inclusion criteria, typically based on a shared exposure (e.g., condition, drug, or procedure).
Q: What qualifies as an exposure? #
A: Any condition or event that might affect the patient — such as diseases, medications, procedures, or behaviors like drinking coffee.
Q: What qualifies as an outcome? #
A: Any event that happens after the exposure and is of interest — such as complications, recovery, cost, or survival time.
Q: Why is timing crucial in analyzing exposures and outcomes? #
A: To establish a meaningful temporal relationship and investigate associations (or causality), it’s essential to ensure exposures precede outcomes and to define observation windows accurately.
➡️ Why are clinical processes considered non-stationary over time?
10 Clinical processes are non-stationary #
Q: What does it mean for clinical data to be non-stationary? #
A: It means that the distributions of data — and the associations within them — change over time due to evolving clinical practices, medications, coding systems, and care standards.
Q: What is a stationary vs. non-stationary process? #
A:
- Stationary: Data distributions remain consistent over time.
- Non-stationary: Data distributions (and thus patterns and associations) change over time.
Q: Why does non-stationarity matter in clinical data mining? #
A: Because models trained on past data may become invalid as treatments, diagnostics, and data collection methods evolve. Analysts must carefully consider data timeframes and changes in system behavior.