| 000 | 01651 a2200505 4500 | ||
|---|---|---|---|
| 005 | 20250518034225.0 | ||
| 264 | 0 | _c20190822 | |
| 008 | 201908s 0 0 eng d | ||
| 022 | _a2157-846X | ||
| 024 | 7 |
_a10.1038/s41551-018-0304-0 _2doi |
|
| 040 |
_aNLM _beng _cNLM |
||
| 100 | 1 | _aLundberg, Scott M | |
| 245 | 0 | 0 |
_aExplainable machine-learning predictions for the prevention of hypoxaemia during surgery. _h[electronic resource] |
| 260 |
_bNature biomedical engineering _c10 2018 |
||
| 300 |
_a749-760 p. _bdigital |
||
| 500 | _aPublication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S. | ||
| 650 | 0 | 4 | _aAdult |
| 650 | 0 | 4 | _aAged |
| 650 | 0 | 4 | _aAged, 80 and over |
| 650 | 0 | 4 |
_aAnesthesia, General _xadverse effects |
| 650 | 0 | 4 |
_aAnesthesiologists _xpsychology |
| 650 | 0 | 4 | _aArea Under Curve |
| 650 | 0 | 4 | _aElectronic Health Records |
| 650 | 0 | 4 | _aFemale |
| 650 | 0 | 4 | _aHumans |
| 650 | 0 | 4 |
_aHypoxia _xetiology |
| 650 | 0 | 4 | _aMachine Learning |
| 650 | 0 | 4 | _aMale |
| 650 | 0 | 4 | _aMiddle Aged |
| 650 | 0 | 4 | _aROC Curve |
| 650 | 0 | 4 | _aRisk Factors |
| 650 | 0 | 4 | _aSurgical Procedures, Operative |
| 700 | 1 | _aNair, Bala | |
| 700 | 1 | _aVavilala, Monica S | |
| 700 | 1 | _aHoribe, Mayumi | |
| 700 | 1 | _aEisses, Michael J | |
| 700 | 1 | _aAdams, Trevor | |
| 700 | 1 | _aListon, David E | |
| 700 | 1 | _aLow, Daniel King-Wai | |
| 700 | 1 | _aNewman, Shu-Fang | |
| 700 | 1 | _aKim, Jerry | |
| 700 | 1 | _aLee, Su-In | |
| 773 | 0 |
_tNature biomedical engineering _gvol. 2 _gno. 10 _gp. 749-760 |
|
| 856 | 4 | 0 |
_uhttps://doi.org/10.1038/s41551-018-0304-0 _zAvailable from publisher's website |
| 999 |
_c29604651 _d29604651 |
||