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Medical Informatics > Key Phrases > Flashcards

Key Phrases Flashcards

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USMLE® Step 1 flashcards
1
Q

Operating memory vs storing device

A

Operating memory is internal, quick and low capacity
RAM- read access mem (lost after turning on)
ROM- read only memory (turn on comp)

Storing device external, slow and high capacity

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2
Q

System programs
End-user applications

A

Bios (basic input/output systemto getcomps started)
Operating systems
Drivers (software inte rface to hardware device)
Programming language

Last program used

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3
Q

operating system

A

set of software that controls the hardware of computer, the device, resource and common services for computer programs to function. (windows, UNIX, linux, android, ios, mac)

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4
Q

intranet vs internet

A

intranet- IPT internet protocol technology to share info and resources within an organization

internet- a global network of interconnected networks use standard internet protocol.

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5
Q

peer-to-peer architecture VS client-server architecture

A

peer to peer: network where computers are equally privileged, equipotent participants in app. ex: BitTorrent, skyoe, Bitcoin, I2P overlay

client-server: server is computer program provide services. client require service on server (cant change roles)

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6
Q

ranges of networks

A

PAN personal area network (bluetooth, cell phone, printer)
LAN local area network (one/few buildings)
MAN metropolitan area network (hospitals, cities)
WAN wide area network (internet)

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7
Q

descriptive VS inductive statistics

A

descriptive: summarize sample using median, mode, avg, standard dev

inductive: transfer fata of sample of population to whole population

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8
Q

incident VS prevalence VS mortality rate VS lethality of disease

A

incident is freq of disease
prevalence proportion of population have disease

mortality rate is rate of death in population
lethality is proportion of death among people sick w specific disease

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9
Q

inferential statistics

A

generalise properties of population from properties of sample (ex: test hypothesis +derive estimates)

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10
Q

sensitivity VS specificity

A

sensitivity is true positives (correct diagnose w disease)

specificity is true negative (diagnose correctly w out disease)

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11
Q

meta-analysis

A

quantitative method of combine result from studies (published) and making conclusions to evaluate effectiveness

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12
Q

brightness VS contrast

A

brightness- pixels
contrast- diff of brightness between 2 pixels

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13
Q

virus
spyware
malicious software

A

virus is malicous software (malfare) w small pieces of code
spyware- infiltration software that monitors users. to get info (pass, id)
malicious software brings harm to system. form of worm, virus, rojans, spyware, adware and rootkits. F: steal

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14
Q

image format and compression
formats of compression algorithms

A

resolution, image size (number of pixels) and colour depth (bits/pixel)
JPEG, PNG, BMP

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15
Q

cryptography

A

convert data into format that is unreadable

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16
Q

aliasing
stroboscopic effect

A

alliasing: causes diff signals to be distinguished (distortions)
stroboscopic effect: visual phenomenon caused by aliasing when motion represented by series of samples

17
Q

frequency filtration

thresholding

A

remove f to suppress tettering signals +reduce background noise

method of image segmentation (grey scale to binary)

18
Q

pixels VS voxel

A

pixel smallest ocntrollable element in picture=2D
(1 pixel =8 bit)
voxel is volumetric pixel =3D, used to visualize medical data

19
Q

firewall

A

maintain security of private network

20
Q

spam
phishing
pharming

A

unrequested electronic messages

fraudulent act to get private info by tricking email recipients

redirect website traffic to fake site

21
Q

cracking vs hacking

A

cracking is just breaking in and hacking use this to destroy/steal data

22
Q

patch vs service pack

A

patch inserting a code into program;temporary fixes
service pack updates to software version that fixes problem

23
Q

Types of machine learning

A

Supervised: labeled data acts as supervisor. Learns acc to given data. Predict output
Unsupervised: no labeled data set. Tries to find patterns+ structures and makes clustered of similar data types
Reinforced: tries to find optimized sol. Reward+punishment based learning.

24
Q

Artificial neural networks

A

Consists of neurons, synapses, weights, biases and functions. Has 3 layers
- input layer: taking input layer from external source and passes onto hidden layers of network. No computation.
- hidden layer: computation performed. Passes to output layer
- output layer: computes+ gives input to outside world

25
Q

Deep neural network

A

Type of Ann w/ many neural network
- CNN: convulational network: computer vision + acoustic modeling for automatic speech recognition
- rnn recurrent neural network: data flows in any direction, language modeling or handwriting recognition

26
Q

Computer vision cv, patching, graphics processing units GPU

A

Cv - file of computer science that mimics human vision often use CNN )
Patching - divide age slide (16GB) in smaller patches
Gpu- chip in graphics card to rapidly images

27
Q

Natural language processing NLP

A

Understanding contents + language to correctly extract info.
Challenges: natural language understanding, speech recognition and natural language generation

28
Q

Chat GPT

A

RLHF - reinforced learning from human feedback. using supervised
Classify, generate, segment = supervised
Search= unsupervised

29
Q

PACS

A

Picturing archiving and communicating system- storing, retrieval and distribution of images
Integrate w’ hospital info system and radiology info system

30
Q

DICOM

A

Digital imaging and communication in medicine
- standard for medical imaging
- network communications network

31
Q

Sampling
quantization

A

Sampling is dividing image to pixels
Quantization is assign # to pixel which represents the color ( brightening) 8 bit quantization offers 28 = 256 pixel

32
Q

Binary representation

A

Binary representation is coding the image in bites for writing into data file
Non Compressed/loose: raw BMP TIFF png
Compressedb losely : jpg, JPEG, LWF- wavelet transformation , GIF (8 bit)

33
Q

Brightness

A

Grayscale images (1 bite for each pixel= 256 shades of grey)
Colour images- red channel or red or green is 256 bites
3 bites= 24 bites therefore truecolour image is 224

34
Q

probability distribution

A

binomial- n of sucesses in yes/no w same probability of sucess
poisson- same but have diff success probability
gaussian-normal distribution

Medical Informatics (1 decks)

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