000			01534 a2200433 4500
005			20250518054041.0
264		0	_c20200713
008			202007s 0 0 eng d
022			_a2168-6262
024	7		_a10.1001/jamasurg.2019.2979 _2doi
040			_aNLM _beng _cNLM
100	1		_aHyer, J Madison
245	0	0	_aNovel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery. _h[electronic resource]
260			_bJAMA surgery _c11 2019
300			_a1014-1021 p. _bdigital
500			_aPublication Type: Journal Article; Observational Study
650	0	4	_aAged
650	0	4	_aElective Surgical Procedures
650	0	4	_aFemale
650	0	4	_aHealth Expenditures _xstatistics & numerical data
650	0	4	_aHumans
650	0	4	_aMachine Learning
650	0	4	_aMale
650	0	4	_aMedicare _xeconomics
650	0	4	_aPatient Acceptance of Health Care _xstatistics & numerical data
650	0	4	_aPostoperative Care _xeconomics
650	0	4	_aPreoperative Care _xmethods
650	0	4	_aRetrospective Studies
650	0	4	_aRisk Assessment _xmethods
650	0	4	_aRisk Factors
650	0	4	_aUnited States
700	1		_aEjaz, Aslam
700	1		_aTsilimigras, Diamantis I
700	1		_aParedes, Anghela Z
700	1		_aMehta, Rittal
700	1		_aPawlik, Timothy M
773	0		_tJAMA surgery _gvol. 154 _gno. 11 _gp. 1014-1021
856	4	0	_uhttps://doi.org/10.1001/jamasurg.2019.2979 _zAvailable from publisher's website
999			_c30004580 _d30004580