Health Data Flashcards

1
Q

Name the types of health data?

A

Patient data
- appointment dates, test results, treatment details

Specific Instruments
- questionnaires, rating scales

Data from blood/tissue samples e.g. DNA, Histology

Data from images e.g. X-Rays, CT, MRI

Health and Fitness Data e.g. heart rate, activity monitoring

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2
Q

Name two methods of data collection?

A

Conventional study designs

  • Observational Studies
    • cross-sectional study
    • case-controls study
    • cohort study
  • Intervention Study

Big Data

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3
Q

What is a cross sectional study?

A

Measures variables of interest - risk factors and disease at the same time

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4
Q

What are the strengths and weaknesses of a cross sectional study?

A

Strengths

  • Relatively easy and cheap to conduct
  • Provide important information on the distribution and burden of exposures and outcomes

Weaknesses

  • only measure prevalent, not incident cases. Therefore, only limited value for identifying risk factors
  • It can be difficult to establish the time sequence of events
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5
Q

What is a case control study?

A

Starts with groups with and without a disease and looks back to see who had the exposure in the past. Used to identify risk factors for diseases with long latent periods

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6
Q

What are the strengths and weaknesses of a case-control study?

A

Strengths

  • Quick and relatively cheap
  • Good for studying rare diseases
  • Good for diseases with ling latent periods of time between exposure and outcome

Weaknesses

  • Prone to selection bias
  • Prone to information bias
  • Cannot establish the sequence of events - was risk factor present before disease?
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7
Q

What are cohort studies?

A

Studies begin with a group of people
- without the disease, measure exposures and then follow up over time to see who gets disease

  • with a particular disease, measure characteristics and then see who gets particular outcomes
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8
Q

What are the strengths and weaknesses of a cohort studies?

A

Strengths

  • exposure/prognostic factors are measured at the start of study before outcome occurs, so measurement is not biased by the presence or absence of outcome
  • can provide data on time course of the development of the outcomes
  • multiple outcomes can be explained

Weaknesses

  • 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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9
Q

What is Randomised Controlled Trial?

A

Gold standard interventional study

Comparison gorups should be similar with respect to cofounders

Prevents bias in allocation of participants to treatment/control

Difference between the intervention and control groups will be whether or not they received the intervention therefore any difference in outcome should be attributable to the intervention

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10
Q

What opportunities does Big Data present?

A

Wide application
More comprehensive data
More detailed data
Costs/efficiency

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11
Q

What challenges does Big Data present?

A

Privacy and security
- it is vital that identifiable personal data is held securely to protect

Quality of Data

  • Researchers generally have less control of data
  • Missing Data
  • Bias
  • Risk of change findings due to multiple comparisons
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12
Q

What is data linkage?

A

Linking datasets that harness the breadth of data are available

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13
Q

What is Artificial Intelligence?

A

Science of mimicking human intelligence

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14
Q

What is machine learning?

A

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

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15
Q

What is deep learning?

A

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

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16
Q

What is supervised machine learning ?

A

Train a machine by showing it examples instead of programming it

17
Q

Name some applications of deep learning

A
Self-driving cars
Facial recognition
Translation
Personal assistants
Content understanding for filtering e.g. hate speech
Anomaly detection in financial security
18
Q

Applications of deep learning in healthcare?

A

Automated fracture detection on wrist x-rays
Categorisation of lung nodules as benign
Classification of histology samples
Determination of nature of skin lesions

Data monitoring in ICU

Prediction of bowel cancer survival

19
Q

Name the ethical principles relating to health data?

A
Privacy
Public Interest
Consent
Transparency 
Security
Proportionality
20
Q

What are the ethical issues relating to big data?

A

Identifiability