J Am Med Inform Assoc - Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data.

Tópicos

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Resumo

CKGROUND: Prognostic studies of breast cancer survivability have been aided by machine learning algorithms, which can predict the survival of a particular patient based on historical patient data. However, it is not easy to collect labeled patient records. It takes at least 5 years to label a patient record as 'survived' or 'not survived'. Unguided trials of numerous types of oncology therapies are also very expensive. Confidentiality agreements with doctors and patients are also required to obtain labeled patient records.PROPOSED METHOD: These difficulties in the collection of labeled patient data have led researchers to consider semi-supervised learning (SSL), a recent machine learning algorithm, because it is also capable of utilizing unlabeled patient data, which is relatively easier to collect. Therefore, it is regarded as an algorithm that could circumvent the known difficulties. However, the fact is yet valid even on SSL that more labeled data lead to better prediction. To compensate for the lack of labeled patient data, we may consider the concept of tagging virtual labels to unlabeled patient data, that is, 'pseudo-labels,' and treating them as if they were labeled.RESULTS: Our proposed algorithm, 'SSL Co-training', implements this concept based on SSL. SSL Co-training was tested using the surveillance, epidemiology, and end results database for breast cancer and it delivered a mean accuracy of 76% and a mean area under the curve of 0.81.

Resumo Limpo

ckground prognost studi breast cancer surviv aid machin learn algorithm can predict surviv particular patient base histor patient data howev easi collect label patient record take least year label patient record surviv surviv unguid trial numer type oncolog therapi also expens confidenti agreement doctor patient also requir obtain label patient recordspropos method difficulti collect label patient data led research consid semisupervis learn ssl recent machin learn algorithm also capabl util unlabel patient data relat easier collect therefor regard algorithm circumv known difficulti howev fact yet valid even ssl label data lead better predict compens lack label patient data may consid concept tag virtual label unlabel patient data pseudolabel treat labeledresult propos algorithm ssl cotrain implement concept base ssl ssl cotrain test use surveil epidemiolog end result databas breast cancer deliv mean accuraci mean area curv

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