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007			cr nn 008mamaa
008			160909s2016 gw | s |||| 0|eng d
020			_a9783319437422
040			_aES-MaUEC
050		4	_aR864 _b2016 EB
082	0	4	_a502.85
100	1		_aMIT Critical Data. _999947 _0Local
245	1	0	_aSecondary Analysis of Electronic Health Records _cby MIT Critical Data
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXI, 427 páginas) _b108 ilustraciones, 100 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction to the Book -- Objectives of secondary analysis of EHR data -- Review of clinical database -- Challenges and opportunities -- Secondary Analysis of EHR Data Cookbook -- Overview -- Step 1: Formulate research question -- Step 2: Data extraction and preprocessing -- Step 3: Exploratory Analysis -- Step 4: Data analysis -- Step 5: Validation and sensitivity analysis -- Missing Data -- Noise vs. Outliers -- Case Studies -- Introduction -- Predictive Modeling: outcome prediction (discrete) -- Predictive Modeling: dose optimization (regression) -- Pharmacovigilance (classification) -- Comparative effectiveness: propensity score analysis -- Comparative effectiveness: instrumental variable analysis -- Decision and Cost Effectiveness Analysis: Hidden Markov models and Monte Carlo simulation -- Time series analysis: Gaussian processes (ICP modelling) -- Time series analysis: Bayesian inference (Motif discovery in numerical signals) -- Time Series analysis: Optimization techniques for hyperparameter selection -- Signal processing: analysis of waveform data -- Signal processing: False alarm reduction.
506	0		_aOpen Access
520	3		_aThis book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a zdata deserty when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
650		7	_aMedicina _0comprobar BNE19900959047 _2embne _9405021
650		7	_aÉtica _0comprobar BNE19900985013 _2embne _9406946
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-43742-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319437422
907			_a.b1295598x _b10-10-17 _c21-11-16
988			_aSpringer_Medicine_2016
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aR864 .M583 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11597975 _z06-04-17
999			_c86320 _d86320 _x1