Background And Rationale Of ROC Analysis Flashcards

1
Q

When was ROC originated?

A

ROC analysis originated in the early 1950’s with electronic signal detection theory.

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2
Q

Why and how?

A

One of the first applications was in radar, to separate observer variability from the innate delectability of signal.

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3
Q

When did psychologists adapt ROC method? What was the task?

A

In the early 1950’s in order to determine the relationship between properties of physical stimuli and the attributes of psychological experience (sensory or perceptual)

The task of observers is to detect a weak signal in the presence of noise; e.g. whether the “signal” was caused by some sensory event.

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4
Q

The applications of ROC metho. in radionucline imaging and diagnostic radiology was calculated by and when used?

A

diagnostic radiology and radionuclide imaging date back to the early 1960’s. The first ROC curve in diagnostic radiology was calculated by Lusted (1960)

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5
Q

What did Lusted do?

A

Lusted (1960) who re-analyzed the previously published data on the detection of pulmonary tuberculosis and showed the reciprocal (mutual, molemminpuoleisenm keskinäinen) relationship between the percentage of false positive and of false negative results from the different studies of chest film interpretations

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6
Q

Whose work was a pioneering step toward objective curve fitting and the use of computerized software in ROC analysis?

A

The work of Dorfman and Alf

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7
Q

What was developed in 1968?

A

An automated program of maximum likelihood approach under binormal assumption was developed in 1968

Automaattinen ohjelma suurimman todennäköisyyden lähestymistavasta binormaalien oletusten mukaisesti kehitettiin vuonna 1968

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8
Q

During the past four decades, ROC analysis has become?

A

siitä on tullut suosittu menetelmä lääketieteellisten diagnostiikkajärjestelmien tarkkuuden arvioimiseksi. ROC-analyysin halutuin ominaisuus on, että tästä tekniikasta johdetut tarkkuusindeksit eivät vääristä mielivaltaisesti valittujen päätöksentekokriteerien tai raja-arvojen käytöstä johtuvia vaihteluita. Toisin sanoen tarkkuusindekseihin ei vaikuta päätöskriteeri (ts. Lukijan tai tarkkailijan taipumus valita tietty kynnys erotinmuuttujalle) ja / tai “signaalin” aikaisempi todennäköisyys

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9
Q

AUC? Summarizes what?

A

The area under the curve.

käyrän alla oleva alue (AUC) määrittää testin luontaisen kyvyn erottaa
sairaiden ja terveiden populaatioiden välillä (21). Käyttämällä tätä diagnostisen suorituskyvyn mittana voidaan verrata yksittäisiä testejä tai arvioida, voivatko testien erilaiset yhdistelmät (esim. Kuvantamistekniikoiden yhdistelmä tai lukijoiden yhdistelmät) parantaa diagnoosin tarkkuutta.

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