DeepHit
DeepHit ni mfumo wa mtandao wa kina wa akili kwa uchambuzi wa maisha na hatari zinazoshindana. Ilianzishwa na Lee et al. mwaka 2018, inapanua DeepSurv kushughulikia mazingira ambapo matukio mengi, yasiyo ya kipekee yanaweza kutokea, kama vile kifo kinachohusiana na ugonjwa dhidi ya kifo kutokana na sababu nyingine. DeepHit inatatua changamoto ya utabiri wa hatari ya kibinafsi wakati washiriki wanaweza kupata aina tofauti za matukio ya mwisho, hali ya kawaida katika matumizi ya kimatibabu na uhalali.
Soma mbinu kamili
Ingia kwa akaunti ya bure ili kusoma sehemu hii.
Method map
The neighbourhood of related methods — select a node to explore.
Vyanzo
- Lee, C., Zame, W., Yoon, J., & van der Schaar, M. (2018). DeepHit: A deep learning approach for dynamic survival analysis with competing risks. AAAI Conference on Artificial Intelligence, 32(1), 2314–2321. link ↗
- Fine, J. P., & Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94(446), 496–509. DOI: 10.1080/01621459.1999.10474144 ↗
- Katzman, J. L., et al. (2018). DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. Journal of Machine Learning Research, 40, 40–51. DOI: 10.1186/s12874-018-0482-1 ↗
Jinsi ya kunukuu ukurasa huu
ScholarGate. (2026, June 3). Deep Learning for Competing Risks. ScholarGate. https://scholargate.app/sw/survival/deephit
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →Umeona tatizo kwenye ukurasa huu? Ripoti au pendekeza marekebisho →