Health data science Flashcards

1
Q

Why is health data science important?

A

Helps us understand causes, prevalence, characteristics and outcomes of disease
Identify new treatment methods

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2
Q

What are different types of health data?

A
Patient data
Specific instruments ie questionnaires
Data from blood or tissue samples
Data from images
Health and fitness devices
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3
Q

How can health data be collected?

A

Observational studies - case-control study, etc
Intervention study
Big data - Routinely collected data, data linkage, AI

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4
Q

What is a cross-sectional study?

A

Study that measures variable interests - classically exposures and outcomes - at the same time

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5
Q

What are strengths of cross-sectional studies?

A

Relatively easy and cheap

Provide important information

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6
Q

What are weaknesses of health data studies?

A

Oly measure prevalence, not incidence

Can be difficult to establish sequence of events ie did risk factor come before disease

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7
Q

What are strengths of case control studies?

A

Quick and relatively cheap
Good for studying rare disease
Good for disease with long latent periods between exposure and outomce

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8
Q

What are weaknesses of case-control studies?

A

Prone to selection bias
Prone to information bias
Can’t establish sequence of events

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9
Q

What is a cohort study?

A

Studies begin with a group of people who either all have or don’t have the disease, looking at exposures or characteristics respectively affecting disease

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10
Q

What are strengths of cohort studies?

A

Exposure or prognostic factors are measured at stat of study
Can provide data on course of development of outcomes
Multiple outcomes can be examined

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11
Q

What are weaknesses of cohort studies?

A

Slow and expensive
Inefficien for rare disease
Exposure status may change during study
Differential loss to follow-up may introduce bias

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12
Q

What is the gold standard interventional study?

A

Randomised control trial

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13
Q

What needs to be considered for risk of bias in RCTs?

A

Was generation of randomisation sequence unbiased?
Was allocation concealed until enrolment?
Were participants/assessors aware of treatment group?
Is there missing data which could introduce bias
Was measurement of outcome unbiased?
Was the pre-specified primary outcome reported?

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14
Q

What are challenges of big data?

A

Privacy and security

Quality of the data

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15
Q

What is data linkage?

A

Linking datasets harnessing the breadth of data that are available

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16
Q

What health data is routinely collected in Scotland?

A
Birth/death data
Hospital discharge coding
Prescribing data
Hospital imaging database
Laboratory data
Cancer registration
17
Q

What is supervised machine learning?

A

Train a machine by showing it examples instead of programming it

18
Q

What are applications of deep learning in healthcare?

A

Diagnosis - images, skin lesions, etc
Data monitoring in ICU
Prognostication

19
Q

What ethical principles relate to health data?

A
Privacy
Public interest
Consent
Transparency
Security
Proprtionality
20
Q

What are ethical issues regarding big data?

A

Identifiability

Identifiable data should not be released to researchers