| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000332511 |
| 005 | | 20241202173500 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438063235 |
| 035 | |
▼a (MiAaPQ)AAI10787837 |
| 035 | |
▼a (MiAaPQ)unc:17640 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Luckett, Daniel J. |
| 245 | 10 |
▼a Machine Learning for Data-driven Biomedical Decision Making. |
| 260 | |
▼a [S.l.] :
▼b The University of North Carolina at Chapel Hill.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 152 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Michael R. Kosorok. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2018. |
| 520 | |
▼a The big data age has brought with it challenges and opportunities for biomedical decision making. New technologies allow for collecting large data sets that can be used to tailor treatment. In this dissertation, we develop machine learning metho |
| 520 | |
▼a Many problems in biomedical decision making can be expressed as classification problems. The costs of false positives and false negatives differ across application domains and this trade-off is often displayed using a receiver operating characte |
| 520 | |
▼a Precision medicine is the paradigm of incorporating individual patient factors into treatment decisions, formalized through individualized treatment regimes (ITR's), or maps from the covariate space into the treatment space. The optimal ITR is d |
| 520 | |
▼a Clinical decision making often requires balancing trade-offs between multiple outcomes while accounting for patient preferences, creating a disconnect with the traditional definition of the optimal ITR. If an instrument to elicit patient prefere |
| 520 | |
▼a Direct search methods, such as outcome weighted learning (OWL), estimate the optimal ITR by maximizing an inverse probability weighted estimator (IPWE) over a class of ITR's. In the final chapter, we show that the IPWE objective function is a pr |
| 590 | |
▼a School code: 0153. |
| 650 | 4 |
▼a Biostatistics. |
| 690 | |
▼a 0308 |
| 710 | 20 |
▼a The University of North Carolina at Chapel Hill.
▼b Biostatistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0153 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997419
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |