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PPB > Health Data Science > Flashcards

Health Data Science Flashcards

1
Q

Give some categories of health data

A 
Patient data
Specific instruments (Questionnaires, rating scales)
Data from blood and tissue samples
Data from images
Health and fitness devices
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2
Q

What is measured in a cross- sectional study?

A

Measures variables of interest at the same time

eg classically exposures (Risk factors) and outcomes (disease)

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3
Q

Give some examples of cross-sectional study?

A
  • Prevalence studies

- Aetiological studies

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4
Q

List some strengths of cross-sectional studies?

A
  • Relatively easy/cheap to conduct

- Provide distribution/burden of exposure/outcome information

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5
Q

List some weaknesses of cross-sectional studies?

A
  • Only measures prevalence, not incidence

- Can be difficult to establish time-sequence

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6
Q

What is a case-control study?

A

Starts with cases and controls and look to see who had the exposure (Risk factor) in the past

Often used for diseases with a long latent period.

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7
Q

List some strengths of case-control studies?

A
  • Quick and relatively cheap (compared to cohort)
  • Good for studying rare diseases
  • Good for diseases with long latent periods
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8
Q

List some weaknesses of case-control studies?

A
  • Prone to selection bias (ie unrepresentative controls)
  • Prone to information bias
  • Cannot establish the sequence of events
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9
Q

What is a cohort study?

A
  • Aetiological research - people without a disease, risk-factors measures and then follow-up for disease
  • Prognostic research - People with a disease, characteristics measured, follow-up for outcomes
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10
Q

List some strengths of cohort studies?

A
  • Exposures/Risk factors measured at start of study before outcome occurs - no measurement bias.
  • Can provide data on time course
  • Multiple outcomes can be measured
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11
Q

List some weaknesses of cohort studies?

A
  • Slow and potentially expensive
  • Inefficient for rare diseases
  • Exposure status may change during study
  • Differential-loss to follow up may introduce bias
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12
Q

What would be the gold-standard interventional study?

A

Randomised controlled trial

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13
Q

What are the benefits of proper randomisation?

A
  • Comparison groups should be similar with respect to confounders, both measured and unmeasured
  • Prevents bias in the allocation of participants to treatment/control
  • Only difference between groups should be if they received the intervention - therefore any difference should be attributable to the intervention
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14
Q

What should be considered in RCT risk of bias?

A
  • Was randomisation sequence unbiased?
  • Was allocation concealed until enrolment?
  • Were participants/outcome assessors aware of treatment group?
  • Have participants deviated from intended interventions?
  • Are they missing data which could introduce bias?
  • Was measurement of outcome unbiased?
  • Was the pre-specified primary outcome reported?
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15
Q

What are some opportunities provided by ‘big data’?

A
  • Wide applications - predictive modelling, clinical decision report, safety monitoring, public health
  • Potentially more comprehensive data
  • More-detailed data (eg wearable devices)
  • Costs/efficiency
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16
Q

What are some challenges provided by ‘big data’?

A
  • Privacy/security

- Quality of data (Missing data, Biases)

17
Q

What is machine learning?

A

An automated way to find patterns in data without being explicitly programmed where to look or what to include

18
Q

What is deep learning?

A

Subset of machine learning using more complex computational techniques to learn complex patterns in large amounts of data

19
Q

What is supervised machine learning?

A

Training a machine by showing it examples instead if programming it.
Parameters can be altered.

20
Q

List some applications of deep learning in healthcare?

A

-Diagnosis (Automated fracture detection, categorisation of benign vs malignant/histology/skin lesions etc)

  • Data monitoring in ICU
  • Prognostication
21
Q

List some ethical principles relating to health data?

A
  • Privacy
  • Public interest
  • Consent
  • Transparency
  • Security
  • Proportionality
  • Identifiability
22
Q

What can be done to reduce the effect of chance in a trial?

A

Increasing sample size

23
Q

What does a low p-value mean?

A

Lower the p-value, the less likely that a particular finding is due to chance

24
Q

What is selection bias?

A

Systematic error in selecting study population.

ie Those recruited are not representative of reference population, or comparison groups are not comparable

25
Q

What is information bias?

A

Systematic error in measurement

26
Q

What is confounding?

A

A distortion of an association due to other factors rather than the factor of interest.

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