Search Results - "Abbreviation"

Data science in the library Tools and Strategies for Supporting Data-Driven Research and Instruction

Table of Contents: “…Figures - Note on Contributors - Acknowledgements - Abbreviations - Introduction: The Rise of Data Science - PART 1: DATA SCIENCE AND RESEARCH LIBRARIES - PERSPECTIVES- 1 Sustainability and Success Models for Informal Data Science Training within Libraries Subject - 2 The Fundación Juan March Data Lab: A Data Science Unit within a Research Support Library - PART 2: DATA SCIENCE INSTRUCTION - 3 Toward Reproducibility: Academic Libraries and Open Science - 4 Start with Data Science - PART 3: DATA SCIENCE SERVICES - 5 In Support of Data-Intensive Science at the University of Washington - 6 From a Data Archive to Data Science: Supporting Current Research - PART 4: DESIGNING AND STAFFING DATA SCIENCE - 7 In-House Training as the First Step to Becoming a Data Savvy Librarian - 8 Designing for Data Science: Planning for Library Data Services - Index…”
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PLASTICS MATERIALS by Brydson, J. A.

Table of Contents: “…Abbreviations for Plastics and Rubbers -- 1. The Historical Development of Plastics Materials -- 2. …”
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CLINICAL PHARMACY AND THERAPEUTICS

Table of Contents: “…. - Rheumatoid arthritis and osteoarthritis - Gout and hyperuricaemia - Glaucoma - Drug-induced skin disorders - Eczema and psoriasis - Wounds - Section 4: Appendices - Medical abbreviations - Glossary - Index…”
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Key Topics in Anesthesia Clinical Aspects by Craft, T.M, Upton, P. M.

Table of Contents: “…Abbreviations - Preface - Name of medical substances - Adrenocortical disease - Adverse drug reaction - AIDS and hepatitis - Air embolism - Airway surgery - Alcohol - Anemia - Anaesthetic records - ARDS/ALI - Asthma - Audit-national - Blood - Blood salvage - Brain death and organ donation - Breast feeding - Breathing system - Burns - Carciniod syndrome - Cardiac arrhythmias - Cardiac assessment - Cardiac ischemia - Cardiac pacemakers - Cardiac surgery - Cardiac valvular disease - Cardioversion - Congenital heart disease - CPR - Critical incidents - Day surgery - Dental anaesthesia - Depth of anaesthesia - Diabetes - Diarthemy - Drowning - Drug of abuse - DVT and PE - ECT - Elderly patiemts - Emergency Anaesthesia - Epiglotittis and croup - Epilepsy - Ethics and duty of care - Evidence based medicine - Governance - Head injury - History of Anaesthesia - Hypertension - Hypotensive anaesthesia - Inherited conditions - Intubation-awake - Intubation-difficult - Laryngectomy - Laser surgery - Liver-the effect of anaesthesia - Liver disease and anaesthesia - Minimal invasive surgery - MRI - Myasthenia gravis - Myotonia - Neonatal surgery - Neuroanaesthesia - Neurological disease - Obesity - Obstetrics-analgesia - Obstetrics- deliver - Obstetrics-medical emergencies - Opthalmology - Organ transplantation - Orthopaedics - Peadiatric anaesthesia-basic principle - Peadiatric anaesthesia-practical - Pain relief-acute - Phaeochromocytoma - Porphyria - Positioning - Preoperation preparation - Pulmonary oedema - Pyloric stenosis - Renal disease - Rheumatoid artritis - Scoring systems - Sedation - Sepsis and SIRS - Sequeale of anaesthesia - Sickle cell disease - Sleep apnoea - Smoking-tobacco - Spinal and epidural anaesthesia - Spinal injury - Stress response to surgery - Temperature-hyperthermia - Temperature-hypothermia - Thoracic anaesthesia - Thyroid surgery - TIVA - Tracheostomy - Transfer of the critically ill - Urology - Vascular surgery - Ventilation - Vommiting - Index…”
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ORGANIC CHEMISTRY by Clayden, Jonathan, Greeves, Nick, Warren, Stuart, Wothers, Peter

Table of Contents: “…Cover; Contents; Abbreviations; Preface to the second edition; Organic chemistry and this book; 1 What is organic chemistry?…”
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Predicting Heart Failure Invasive, Non-Invasive, Machine Learning No Artificial Intelligence Based Methods

Table of Contents: “…Preface vii Abbreviations ix Acknowledgment xvii 1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1Hidayet Takci 2 Conventional Clinical Methods for Predicting Heart Disease 23Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni 3 Types of Biosensors and their Importance in Cardiovascular Applications 47S Irem Kaya, Leyla Karadurmus, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan 4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81Mohamed Zied Chaari and Somaya Al-Maadeed 5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin 6 Artificial Intelligence Techniques in Cardiology: An Overview 139Ikram-Ul Haq and Bo Xu 7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155Ahmad Mousa Altamimi and Mohammad Azzeh 8 Applications of Machine Learning for Predicting Heart Failure 171Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin 9 Machine Learning Techniques for Predicting and Managing Heart Failure 189Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis 10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem 11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane 12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas 13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam Index 321…”
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PREDICTING HEART FAILURE INVASIVE, NON-INVASIVE, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE BASED METHODS

Table of Contents: “…Preface vii Abbreviations ix Acknowledgment xvii 1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure 1Hidayet Takci 2 Conventional Clinical Methods for Predicting Heart Disease 23Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni 3 Types of Biosensors and their Importance in Cardiovascular Applications 47S Irem Kaya, Leyla Karadurmus, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan 4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors 81Mohamed Zied Chaari and Somaya Al-Maadeed 5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview 109Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin 6 Artificial Intelligence Techniques in Cardiology: An Overview 139Ikram-Ul Haq and Bo Xu 7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases 155Ahmad Mousa Altamimi and Mohammad Azzeh 8 Applications of Machine Learning for Predicting Heart Failure 171Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin 9 Machine Learning Techniques for Predicting and Managing Heart Failure 189Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis 10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers 227Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem 11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review 243Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane 12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction 269Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas 13 Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices 295Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam Index 321…”
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Prescriber's Guide Stahl's Essential Psychopharmacology by Stahl, Stephen M.

Table of Contents: “…Chlorpromazine - 26. citalopram - 27. clomipramine - 28. clonazepam - 29. clonidine - 30. clorazepate - 31. clozapine - 32. cyamemazine - 33. desipramine - 34. desvenlafaxine - 35. deutetrabenazine - 36. dextromethorphan - 37. diazepam - 38. diphenhydramine - 39. disulfiram - 40. donepezil - 41. dothiepin - 42. doxepin - 43. duloxetine - 44. escitalopram - 45. esketamine - 46. estazolam - 47. eszopiclone - 48. flibanserin - 49. flumazenil - 50. flunitrazepam - 51. fluoxetine - 52. flupenthixol - 53. fluphenazine - 54. flurazepam - 55. fluvoxamine - 56. gabapentin - 57. galantamine - 58. guanfacine - 59. haloperidol - 60. hydroxyzine - 61. iloperidone - 62. imipramine - 63. isocarboxazid - 64. ketamine - 65. lamotrigine; 66. levetiracetam; 67. lemborexant - 68. levomilnacipran - 69. lisdexamfetamine - 70. lithium - 71. lofexidine - 72. lofepramine - 73. loflazeptate - 74. lorazepam - 75. loxapine - 76. lumateperone - 77. lurasidone - 78. maprotiline - 79. memantine - 80.methylfolate (l) - 81. methylphenidate (d) - 82. methylphenidate (d,l) - 83. mianserin - 84. midazolam - 85. milnacipran - 86. mirtazapine - 87. moclobemide - 88. modafinil - 89. molindone - 90. nalmefene - 91. naltrexone; 92. naltrexone-bupropion - 93. nefazodone - 94. nortriptyline - 95. olanzapine - 96. oxazepam - 97. oxcarbazepine - 98. paliperidone - 99. paroxetine - 100. perospirone - 101. perphenazine - 102. phenelzine - 103. phentermine-topiramate - 104. pimavanserin - 105. pimozide - 106. pipothiazine - 107. pitolisant - 108. prazosin - 109. pregabalin - 110. propranolol - 111. protriptyline - 112. quazepam - 113. quetiapine - 114. ramelteon - 115. reboxetine - 116. risperidone - 117. rivastigmine - 118. selegiline - 119. sertindole - 120. sertraline - 121. sildenafil - 122. sodium oxybate - 123. solriamfetol - 124. sulpiride - 125.suvorexant - 126. tasimelteon - 127. temazepam - 128. thioridazine - 129. thiothixene - 130. tiagabine - 131. tianeptine - 132. topiramate - 133. tranylcypromine - 134. trazodone - 135. triazolam - 136. trifluoperazine - 137. trihexyphenidyl - 138. triiodothyronine - 139. trimipramine - 140. valbenazine - 141. valproate - 142. varenicline - 143. venlafaxine - 144. vilazodone - 145. vortioxetine - 146. zaleplon - 147. ziprasidone - 148. zolpidem - 149. zonisamide - 150. zopiclone - 151. zotepine - 152. zuclopenthixol - Index by drug name - Index by use - Index by class - Abbreviations.…”
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Core topics in cardiac anesthesia

Table of Contents: “…Machine generated contents note: Preface; Foreword; Abbreviations; Part I. Anatomy and Physiology: 1. Cardiac embryology and anatomy; 2. …”
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McGraw-Hill's Nurse's Dictionary

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Calculations and pharmaceutics in practice

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Statistics for management and economics by Keller, Gerald

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Murder in the cathedral by Eliot, T. S. 1888-1965 Thomas Stearns

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Statistics for management and economics by Keller, Gerald

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The Essentials of STATISTICS A Tool for Social Research by Healey, Joseph F. 1945-

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Essentials of understanding psychology by Feldman, Robert S 1947- Robert Stephen

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The Times atlas of the world

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A concise history of the American Republic by Morison, Samuel Eliot

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A.B.C. Japanese-English dictionary an entirely new method of classification of Chines-Japanese characters by Vaccari, Oreste

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Mathematics illustrated dictionary facts, figures, and people including the new mathematics and a computer section by Bendick, Jeanne

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