An industrial IoT approach for pharmaceutical industry growth: volume 2
Saved in:
| Other Authors: | , , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom
Academic Press
[2020]
|
| Online Access: | DE-91 |
| Physical Description: | 1 Online-Ressource (xxvii, 353 Seiten) Illustrationen |
| ISBN: | 9780128213278 |
Staff View
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| 245 | 1 | 0 | |a An industrial IoT approach for pharmaceutical industry growth |b volume 2 |c edited by Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar |
| 264 | 1 | |a London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom |b Academic Press |c [2020] | |
| 264 | 4 | |c © 2020 | |
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| 505 | 8 | |a Front Cover -- An Industrial IoT Approach for Pharmaceutical Industry Growth -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- About the book -- 1 Medical big data mining and processing in e-health care -- 1.1 Introduction -- 1.1.1 Types of big data -- 1.1.1.1 Structured -- 1.1.1.2 Unstructured -- 1.1.1.3 Semistructured -- 1.1.2 Characteristics of big data -- 1.1.2.1 Variety -- 1.1.2.2 Velocity -- 1.1.2.3 Volume -- 1.1.3 Integration of big data with medical imaging -- 1.1.4 Advantages of health-care data management -- 1.1.5 Challenges of health-care data management -- 1.1.6 Health care as a big data database -- 1.1.7 Benefits of medical big data -- 1.2 Architecture of big data in health care -- 1.2.1 Batch processing layer -- 1.2.2 Data acquisition -- 1.2.3 Electronic health-care records -- 1.2.4 Biomedical images -- 1.2.5 Social network analysis -- 1.2.6 Sensing data -- 1.2.7 Cell phones -- 1.2.8 Semantic module -- 1.3 Preparation of data -- 1.3.1 Data filtering -- 1.3.2 Data cleaning -- 1.3.3 Noise treatment -- 1.4 Feature extraction and feature selection -- 1.5 Predictive model design -- 1.6 Data storage -- 1.7 Stream processing layer -- 1.7.1 Data synchronization -- 1.7.2 Adaptive learning -- 1.7.3 Adaptive preprocessor -- 1.7.4 Adaptive predictor -- 1.8 Query processor -- 1.9 Visualization layer -- 1.10 Use of big data in biomedical research -- 1.11 Companies using big data in health care -- 1.11.1 Dignity health: analytics helps prevent deadly infections -- 1.11.2 Express scripts: better decisions, healthier outcomes with big data -- 1.11.3 United health care: monitoring fraud and waste, improving clinical outcomes -- 1.12 Other opportunities for big data in health care -- 1.12.1 Episode analytics -- 1.13 Use of health care in big data analytics -- 1.14 Unique features of big data in health care | |
| 505 | 8 | |a 1.14.1 Heterogeneity -- 1.14.2 Incompleteness -- 1.14.3 Data privacy -- 1.14.4 Ownership -- 1.15 Big data applications in health care -- 1.16 Internet of Things-based medical image processing -- 1.16.1 Patient information management -- 1.16.2 Medical emergency management -- 1.16.3 Medical waste information management -- 1.16.4 Drug storage -- 1.16.5 Combating pharmaceutical errors -- 1.16.6 Medical equipment and drug tracking -- 1.16.7 Connected information sharing -- 1.16.8 Newborn antikidnapping system -- 1.16.9 Alarm system -- 1.17 Internet of Things -- 1.18 Use of the Internet of Things in health care -- 1.18.1 Remote patient monitoring -- 1.18.2 Wearables -- 1.18.3 Better drug management -- 1.18.4 Hospital management -- 1.19 Health care in various countries -- 1.19.1 Simultaneous reporting and monitoring -- 1.19.2 End-to-end connectivity and affordability -- 1.19.3 Data assortment and analysis -- 1.19.4 Research -- 1.19.5 Data security and privacy -- 1.20 Disadvantages of Internet of Things in health care -- 1.21 Medical Internet of Things and cyber-physical systems -- 1.22 Social network data -- 1.23 History of the Internet of Things in health care -- 1.24 Challenges of the Internet of Things in health care -- 1.25 Future of the Internet of Things in health care -- 1.26 Internet of Things with ThingSpeak -- 1.27 Introduction to the cloud -- 1.27.1 Cloud computing -- 1.27.2 Cloud storage -- 1.28 ThingSpeak channels -- 1.28.1 Channel setting of the cloud -- 1.28.2 Using the channel -- 1.29 Application working -- 1.29.1 Doctors -- 1.29.2 Patients -- 1.29.3 Login credentials -- 1.29.4 Registration credentials -- 1.30 Summary -- References -- 2 Brain-computer interfaces and their applications -- 2.1 Introduction -- 2.1.1 Neuroimaging approaches in brain-computer interfaces -- 2.1.2 Electroencephalography -- 2.1.3 Magnetoencephalography | |
| 505 | 8 | |a 2.1.4 Electrocorticography -- 2.1.5 Intracortical neuron recording -- 2.1.6 Functional magnetic resonance imaging -- 2.1.7 Near-infrared spectroscopy -- 2.2 Control signal types in brain-computer interfaces -- 2.2.1 Visual-evoked potentials -- 2.2.2 Slow cortical potentials -- 2.2.3 P300-evoked potentials -- 2.2.4 Sensorimotor rhythms -- 2.3 Types of brain-computer interface -- 2.4 Features extraction and selection -- 2.4.1 Principal component analysis -- 2.4.2 Independent component analysis -- 2.4.3 Autoregressive components -- 2.4.4 Matched filtering -- 2.4.5 Wavelet transformation -- 2.5 Artifacts in brain-computer interfaces -- 2.6 Classification algorithms -- 2.6.1 k-Nearest neighbor classifier -- 2.6.2 Linear discriminant analysis -- 2.6.3 Support vector machine -- 2.6.4 Artificial neural network -- 2.7 Brain-computer interface applications -- 2.7.1 Communication -- 2.7.2 Motor restoration -- 2.7.3 Environmental control -- 2.7.4 Locomotion -- 2.7.5 Entertainment -- 2.7.6 Other brain-computer interface applications -- 2.8 Conclusion -- References -- 3 Transforming pharma logistics with the Internet of things -- 3.1 Introduction -- 3.1.1 What is the Internet of things? -- 3.1.2 Internet of things in logistics and the supply chain -- 3.2 Growth of pharmaceutical industries -- 3.2.1 Rising demand -- 3.2.2 Advent of Internet of things -- 3.2.3 Internet of things-based pharma architecture -- 3.2.3.1 Perception layer -- 3.2.3.2 Network transmission layer -- 3.2.3.3 Support/middleware layer -- 3.2.3.4 Application layer -- 3.3 Applications of Internet of things in pharmaceutical logistics -- 3.3.1 Manufacturing of drugs and equipment -- 3.3.1.1 Unique identification number -- 3.3.1.2 Real-time location system -- 3.3.1.3 Sensors -- 3.3.1.4 Cloud computing -- 3.3.1.5 Communication technologies -- 3.3.2 Warehouse management | |
| 505 | 8 | |a 3.3.2.1 Design goals for a pharmaceutical warehouse management system -- 3.3.2.2 Architecture of a pharmaceutical warehouse management system -- 3.3.3 Shipment and tracking -- 3.3.4 Quality control of drugs -- 3.4 Challenges in pharma logistics -- 3.4.1 Supply chain visibility -- 3.4.2 Security issues -- 3.4.3 Temperature control -- 3.4.4 Data security -- 3.5 Conquering pharma logistics with Internet of things -- 3.5.1 Smart warehousing -- 3.5.1.1 Business benefits of smart warehousing -- 3.5.1.1.1 Increasing operational efficiency in the warehouse -- 3.5.1.1.2 Maintain desired storage conditions -- 3.5.1.1.3 Orient production and demand -- 3.5.1.1.4 Increase manufacturing efficiency -- 3.5.1.2 Basic workflow of a smart warehouse management system -- 3.5.2 Installing radio frequency identification tags -- 3.5.3 Real-time shipment tracking -- 3.5.3.1 Ensuring product integrity and traceability -- 3.5.3.2 Reduce inventory costs -- 3.5.4 Environmental sensor installation -- References -- Further reading -- 4 Drug identification and interaction checking using the Internet of Things -- 4.1 Introduction -- 4.2 Internet of Things based smart devices used in pharma and health care -- 4.2.1 Organ on a chip -- 4.2.2 Chip in a pill -- 4.2.3 Google glass -- 4.2.4 iBeacons -- 4.2.5 Sensors in drug-delivery devices -- 4.2.6 Smart wheelchairs -- 4.2.7 Wristbands -- 4.3 Role of the Internet of Things across various stages in the healthcare value chain -- 4.3.1 Stage 1: Research and development -- 4.3.2 Stage 2: Supply chain -- 4.3.3 Stage 3: Marketing and sales -- 4.3.4 Stage 4: End users -- 4.4 Key benefits of the Internet of Things to the life sciences industry -- 4.5 Rule-based system -- 4.5.1 Allergies -- 4.5.2 Active ingredient interactions -- 4.5.3 Drug loop -- 4.5.4 Renal impairment -- 4.5.5 Pregnancy -- 4.5.6 Optimization of absorption | |
| 505 | 8 | |a 4.6 Social and cultural factors associated with drug abuse in adolescents -- 4.6.1 Parental influence -- 4.6.2 Family structure -- 4.6.3 Peer influence -- 4.6.4 Role models -- 4.6.5 Advertising and promotion -- 4.6.6 Socioeconomic factors -- 4.6.7 Availability -- 4.6.8 Knowledge, attitudes, and beliefs -- 4.6.9 Street children and drug abuse -- 4.7 What happens to your brain when you take drugs? -- 4.7.1 Challenges for drug treatment and care -- 4.8 What causes drug abuse? -- 4.8.1 Commonly abused drugs -- 4.8.1.1 Alcohol -- 4.8.1.2 Anabolic steroids -- 4.8.1.3 Club drugs -- 4.8.1.4 Cocaine -- 4.8.1.5 Heroin -- 4.8.1.6 Inhalants -- 4.8.1.7 Marijuana -- 4.8.1.8 Methamphetamines -- 4.8.2 Prescription drugs -- 4.8.3 Stages of drug abuse -- 4.8.4 Treating drug abuse -- 4.8.5 Detoxification -- 4.8.6 Preventing drug abuse -- 4.8.7 Effects of drug abuse and addiction -- 4.9 The effects of drug abuse on health -- 4.9.1 Drug effects on behavior -- 4.9.2 Effects of drug abuse on unborn babies -- 4.10 Types of drugs -- 4.10.1 Stimulants -- 4.10.1.1 Risks of stimulant abuse -- 4.10.2 Depressants -- 4.10.3 Alcohol as a depressant -- 4.10.4 Tobacco as a depressant -- 4.10.4.1 Risks of depressant abuse -- 4.10.5 Hallucinogens -- 4.10.5.1 Risks of hallucinogen abuse -- 4.10.6 Dissociatives -- 4.10.6.1 Risks of dissociative abuse -- 4.10.7 Opioids -- 4.10.7.1 Risks of opioid abuse -- 4.10.8 Inhalants -- 4.10.8.1 Risks of inhalant abuse -- 4.10.9 Cannabis -- 4.10.9.1 Risks of cannabis abuse -- 4.11 Conclusions -- References -- 5 Accelerating data acquisition process in the pharmaceutical industry using Internet of Things -- 5.1 Introduction -- 5.1.1 Architectural framework of the Internet of Things -- 5.1.1.1 Sensing layer -- 5.1.1.2 Network layer -- 5.1.1.3 Service layer -- 5.1.1.4 Interface layer -- 5.1.2 Overview of the pharmaceutical industry | |
| 505 | 8 | |a 5.1.3 The Internet of Things revolution in the pharmaceutical industry | |
| 700 | 1 | |a Balas, Valentina E. |d 1956- |0 (DE-588)1202958311 |4 edt | |
| 700 | 1 | |a Solanki, Vijender Kumar |d 1980- |0 (DE-588)1139801074 |4 edt | |
| 700 | 1 | |a Kumar, Raghvendra |d 1987- |0 (DE-588)1156490286 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |a Balas, Valentina Emilia |t An Industrial IoT Approach for Pharmaceutical Industry Growth |d San Diego : Elsevier Science & Technology,c2020 |n Druck-Ausgabe |z 978-0-12-821326-1 |
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| contents | Front Cover -- An Industrial IoT Approach for Pharmaceutical Industry Growth -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- About the book -- 1 Medical big data mining and processing in e-health care -- 1.1 Introduction -- 1.1.1 Types of big data -- 1.1.1.1 Structured -- 1.1.1.2 Unstructured -- 1.1.1.3 Semistructured -- 1.1.2 Characteristics of big data -- 1.1.2.1 Variety -- 1.1.2.2 Velocity -- 1.1.2.3 Volume -- 1.1.3 Integration of big data with medical imaging -- 1.1.4 Advantages of health-care data management -- 1.1.5 Challenges of health-care data management -- 1.1.6 Health care as a big data database -- 1.1.7 Benefits of medical big data -- 1.2 Architecture of big data in health care -- 1.2.1 Batch processing layer -- 1.2.2 Data acquisition -- 1.2.3 Electronic health-care records -- 1.2.4 Biomedical images -- 1.2.5 Social network analysis -- 1.2.6 Sensing data -- 1.2.7 Cell phones -- 1.2.8 Semantic module -- 1.3 Preparation of data -- 1.3.1 Data filtering -- 1.3.2 Data cleaning -- 1.3.3 Noise treatment -- 1.4 Feature extraction and feature selection -- 1.5 Predictive model design -- 1.6 Data storage -- 1.7 Stream processing layer -- 1.7.1 Data synchronization -- 1.7.2 Adaptive learning -- 1.7.3 Adaptive preprocessor -- 1.7.4 Adaptive predictor -- 1.8 Query processor -- 1.9 Visualization layer -- 1.10 Use of big data in biomedical research -- 1.11 Companies using big data in health care -- 1.11.1 Dignity health: analytics helps prevent deadly infections -- 1.11.2 Express scripts: better decisions, healthier outcomes with big data -- 1.11.3 United health care: monitoring fraud and waste, improving clinical outcomes -- 1.12 Other opportunities for big data in health care -- 1.12.1 Episode analytics -- 1.13 Use of health care in big data analytics -- 1.14 Unique features of big data in health care 1.14.1 Heterogeneity -- 1.14.2 Incompleteness -- 1.14.3 Data privacy -- 1.14.4 Ownership -- 1.15 Big data applications in health care -- 1.16 Internet of Things-based medical image processing -- 1.16.1 Patient information management -- 1.16.2 Medical emergency management -- 1.16.3 Medical waste information management -- 1.16.4 Drug storage -- 1.16.5 Combating pharmaceutical errors -- 1.16.6 Medical equipment and drug tracking -- 1.16.7 Connected information sharing -- 1.16.8 Newborn antikidnapping system -- 1.16.9 Alarm system -- 1.17 Internet of Things -- 1.18 Use of the Internet of Things in health care -- 1.18.1 Remote patient monitoring -- 1.18.2 Wearables -- 1.18.3 Better drug management -- 1.18.4 Hospital management -- 1.19 Health care in various countries -- 1.19.1 Simultaneous reporting and monitoring -- 1.19.2 End-to-end connectivity and affordability -- 1.19.3 Data assortment and analysis -- 1.19.4 Research -- 1.19.5 Data security and privacy -- 1.20 Disadvantages of Internet of Things in health care -- 1.21 Medical Internet of Things and cyber-physical systems -- 1.22 Social network data -- 1.23 History of the Internet of Things in health care -- 1.24 Challenges of the Internet of Things in health care -- 1.25 Future of the Internet of Things in health care -- 1.26 Internet of Things with ThingSpeak -- 1.27 Introduction to the cloud -- 1.27.1 Cloud computing -- 1.27.2 Cloud storage -- 1.28 ThingSpeak channels -- 1.28.1 Channel setting of the cloud -- 1.28.2 Using the channel -- 1.29 Application working -- 1.29.1 Doctors -- 1.29.2 Patients -- 1.29.3 Login credentials -- 1.29.4 Registration credentials -- 1.30 Summary -- References -- 2 Brain-computer interfaces and their applications -- 2.1 Introduction -- 2.1.1 Neuroimaging approaches in brain-computer interfaces -- 2.1.2 Electroencephalography -- 2.1.3 Magnetoencephalography 2.1.4 Electrocorticography -- 2.1.5 Intracortical neuron recording -- 2.1.6 Functional magnetic resonance imaging -- 2.1.7 Near-infrared spectroscopy -- 2.2 Control signal types in brain-computer interfaces -- 2.2.1 Visual-evoked potentials -- 2.2.2 Slow cortical potentials -- 2.2.3 P300-evoked potentials -- 2.2.4 Sensorimotor rhythms -- 2.3 Types of brain-computer interface -- 2.4 Features extraction and selection -- 2.4.1 Principal component analysis -- 2.4.2 Independent component analysis -- 2.4.3 Autoregressive components -- 2.4.4 Matched filtering -- 2.4.5 Wavelet transformation -- 2.5 Artifacts in brain-computer interfaces -- 2.6 Classification algorithms -- 2.6.1 k-Nearest neighbor classifier -- 2.6.2 Linear discriminant analysis -- 2.6.3 Support vector machine -- 2.6.4 Artificial neural network -- 2.7 Brain-computer interface applications -- 2.7.1 Communication -- 2.7.2 Motor restoration -- 2.7.3 Environmental control -- 2.7.4 Locomotion -- 2.7.5 Entertainment -- 2.7.6 Other brain-computer interface applications -- 2.8 Conclusion -- References -- 3 Transforming pharma logistics with the Internet of things -- 3.1 Introduction -- 3.1.1 What is the Internet of things? -- 3.1.2 Internet of things in logistics and the supply chain -- 3.2 Growth of pharmaceutical industries -- 3.2.1 Rising demand -- 3.2.2 Advent of Internet of things -- 3.2.3 Internet of things-based pharma architecture -- 3.2.3.1 Perception layer -- 3.2.3.2 Network transmission layer -- 3.2.3.3 Support/middleware layer -- 3.2.3.4 Application layer -- 3.3 Applications of Internet of things in pharmaceutical logistics -- 3.3.1 Manufacturing of drugs and equipment -- 3.3.1.1 Unique identification number -- 3.3.1.2 Real-time location system -- 3.3.1.3 Sensors -- 3.3.1.4 Cloud computing -- 3.3.1.5 Communication technologies -- 3.3.2 Warehouse management 3.3.2.1 Design goals for a pharmaceutical warehouse management system -- 3.3.2.2 Architecture of a pharmaceutical warehouse management system -- 3.3.3 Shipment and tracking -- 3.3.4 Quality control of drugs -- 3.4 Challenges in pharma logistics -- 3.4.1 Supply chain visibility -- 3.4.2 Security issues -- 3.4.3 Temperature control -- 3.4.4 Data security -- 3.5 Conquering pharma logistics with Internet of things -- 3.5.1 Smart warehousing -- 3.5.1.1 Business benefits of smart warehousing -- 3.5.1.1.1 Increasing operational efficiency in the warehouse -- 3.5.1.1.2 Maintain desired storage conditions -- 3.5.1.1.3 Orient production and demand -- 3.5.1.1.4 Increase manufacturing efficiency -- 3.5.1.2 Basic workflow of a smart warehouse management system -- 3.5.2 Installing radio frequency identification tags -- 3.5.3 Real-time shipment tracking -- 3.5.3.1 Ensuring product integrity and traceability -- 3.5.3.2 Reduce inventory costs -- 3.5.4 Environmental sensor installation -- References -- Further reading -- 4 Drug identification and interaction checking using the Internet of Things -- 4.1 Introduction -- 4.2 Internet of Things based smart devices used in pharma and health care -- 4.2.1 Organ on a chip -- 4.2.2 Chip in a pill -- 4.2.3 Google glass -- 4.2.4 iBeacons -- 4.2.5 Sensors in drug-delivery devices -- 4.2.6 Smart wheelchairs -- 4.2.7 Wristbands -- 4.3 Role of the Internet of Things across various stages in the healthcare value chain -- 4.3.1 Stage 1: Research and development -- 4.3.2 Stage 2: Supply chain -- 4.3.3 Stage 3: Marketing and sales -- 4.3.4 Stage 4: End users -- 4.4 Key benefits of the Internet of Things to the life sciences industry -- 4.5 Rule-based system -- 4.5.1 Allergies -- 4.5.2 Active ingredient interactions -- 4.5.3 Drug loop -- 4.5.4 Renal impairment -- 4.5.5 Pregnancy -- 4.5.6 Optimization of absorption 4.6 Social and cultural factors associated with drug abuse in adolescents -- 4.6.1 Parental influence -- 4.6.2 Family structure -- 4.6.3 Peer influence -- 4.6.4 Role models -- 4.6.5 Advertising and promotion -- 4.6.6 Socioeconomic factors -- 4.6.7 Availability -- 4.6.8 Knowledge, attitudes, and beliefs -- 4.6.9 Street children and drug abuse -- 4.7 What happens to your brain when you take drugs? -- 4.7.1 Challenges for drug treatment and care -- 4.8 What causes drug abuse? -- 4.8.1 Commonly abused drugs -- 4.8.1.1 Alcohol -- 4.8.1.2 Anabolic steroids -- 4.8.1.3 Club drugs -- 4.8.1.4 Cocaine -- 4.8.1.5 Heroin -- 4.8.1.6 Inhalants -- 4.8.1.7 Marijuana -- 4.8.1.8 Methamphetamines -- 4.8.2 Prescription drugs -- 4.8.3 Stages of drug abuse -- 4.8.4 Treating drug abuse -- 4.8.5 Detoxification -- 4.8.6 Preventing drug abuse -- 4.8.7 Effects of drug abuse and addiction -- 4.9 The effects of drug abuse on health -- 4.9.1 Drug effects on behavior -- 4.9.2 Effects of drug abuse on unborn babies -- 4.10 Types of drugs -- 4.10.1 Stimulants -- 4.10.1.1 Risks of stimulant abuse -- 4.10.2 Depressants -- 4.10.3 Alcohol as a depressant -- 4.10.4 Tobacco as a depressant -- 4.10.4.1 Risks of depressant abuse -- 4.10.5 Hallucinogens -- 4.10.5.1 Risks of hallucinogen abuse -- 4.10.6 Dissociatives -- 4.10.6.1 Risks of dissociative abuse -- 4.10.7 Opioids -- 4.10.7.1 Risks of opioid abuse -- 4.10.8 Inhalants -- 4.10.8.1 Risks of inhalant abuse -- 4.10.9 Cannabis -- 4.10.9.1 Risks of cannabis abuse -- 4.11 Conclusions -- References -- 5 Accelerating data acquisition process in the pharmaceutical industry using Internet of Things -- 5.1 Introduction -- 5.1.1 Architectural framework of the Internet of Things -- 5.1.1.1 Sensing layer -- 5.1.1.2 Network layer -- 5.1.1.3 Service layer -- 5.1.1.4 Interface layer -- 5.1.2 Overview of the pharmaceutical industry 5.1.3 The Internet of Things revolution in the pharmaceutical industry |
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Press</subfield><subfield code="c">[2020]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxvii, 353 Seiten)</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Front Cover -- An Industrial IoT Approach for Pharmaceutical Industry Growth -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- About the book -- 1 Medical big data mining and processing in e-health care -- 1.1 Introduction -- 1.1.1 Types of big data -- 1.1.1.1 Structured -- 1.1.1.2 Unstructured -- 1.1.1.3 Semistructured -- 1.1.2 Characteristics of big data -- 1.1.2.1 Variety -- 1.1.2.2 Velocity -- 1.1.2.3 Volume -- 1.1.3 Integration of big data with medical imaging -- 1.1.4 Advantages of health-care data management -- 1.1.5 Challenges of health-care data management -- 1.1.6 Health care as a big data database -- 1.1.7 Benefits of medical big data -- 1.2 Architecture of big data in health care -- 1.2.1 Batch processing layer -- 1.2.2 Data acquisition -- 1.2.3 Electronic health-care records -- 1.2.4 Biomedical images -- 1.2.5 Social network analysis -- 1.2.6 Sensing data -- 1.2.7 Cell phones -- 1.2.8 Semantic module -- 1.3 Preparation of data -- 1.3.1 Data filtering -- 1.3.2 Data cleaning -- 1.3.3 Noise treatment -- 1.4 Feature extraction and feature selection -- 1.5 Predictive model design -- 1.6 Data storage -- 1.7 Stream processing layer -- 1.7.1 Data synchronization -- 1.7.2 Adaptive learning -- 1.7.3 Adaptive preprocessor -- 1.7.4 Adaptive predictor -- 1.8 Query processor -- 1.9 Visualization layer -- 1.10 Use of big data in biomedical research -- 1.11 Companies using big data in health care -- 1.11.1 Dignity health: analytics helps prevent deadly infections -- 1.11.2 Express scripts: better decisions, healthier outcomes with big data -- 1.11.3 United health care: monitoring fraud and waste, improving clinical outcomes -- 1.12 Other opportunities for big data in health care -- 1.12.1 Episode analytics -- 1.13 Use of health care in big data analytics -- 1.14 Unique features of big data in health care</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.14.1 Heterogeneity -- 1.14.2 Incompleteness -- 1.14.3 Data privacy -- 1.14.4 Ownership -- 1.15 Big data applications in health care -- 1.16 Internet of Things-based medical image processing -- 1.16.1 Patient information management -- 1.16.2 Medical emergency management -- 1.16.3 Medical waste information management -- 1.16.4 Drug storage -- 1.16.5 Combating pharmaceutical errors -- 1.16.6 Medical equipment and drug tracking -- 1.16.7 Connected information sharing -- 1.16.8 Newborn antikidnapping system -- 1.16.9 Alarm system -- 1.17 Internet of Things -- 1.18 Use of the Internet of Things in health care -- 1.18.1 Remote patient monitoring -- 1.18.2 Wearables -- 1.18.3 Better drug management -- 1.18.4 Hospital management -- 1.19 Health care in various countries -- 1.19.1 Simultaneous reporting and monitoring -- 1.19.2 End-to-end connectivity and affordability -- 1.19.3 Data assortment and analysis -- 1.19.4 Research -- 1.19.5 Data security and privacy -- 1.20 Disadvantages of Internet of Things in health care -- 1.21 Medical Internet of Things and cyber-physical systems -- 1.22 Social network data -- 1.23 History of the Internet of Things in health care -- 1.24 Challenges of the Internet of Things in health care -- 1.25 Future of the Internet of Things in health care -- 1.26 Internet of Things with ThingSpeak -- 1.27 Introduction to the cloud -- 1.27.1 Cloud computing -- 1.27.2 Cloud storage -- 1.28 ThingSpeak channels -- 1.28.1 Channel setting of the cloud -- 1.28.2 Using the channel -- 1.29 Application working -- 1.29.1 Doctors -- 1.29.2 Patients -- 1.29.3 Login credentials -- 1.29.4 Registration credentials -- 1.30 Summary -- References -- 2 Brain-computer interfaces and their applications -- 2.1 Introduction -- 2.1.1 Neuroimaging approaches in brain-computer interfaces -- 2.1.2 Electroencephalography -- 2.1.3 Magnetoencephalography</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.1.4 Electrocorticography -- 2.1.5 Intracortical neuron recording -- 2.1.6 Functional magnetic resonance imaging -- 2.1.7 Near-infrared spectroscopy -- 2.2 Control signal types in brain-computer interfaces -- 2.2.1 Visual-evoked potentials -- 2.2.2 Slow cortical potentials -- 2.2.3 P300-evoked potentials -- 2.2.4 Sensorimotor rhythms -- 2.3 Types of brain-computer interface -- 2.4 Features extraction and selection -- 2.4.1 Principal component analysis -- 2.4.2 Independent component analysis -- 2.4.3 Autoregressive components -- 2.4.4 Matched filtering -- 2.4.5 Wavelet transformation -- 2.5 Artifacts in brain-computer interfaces -- 2.6 Classification algorithms -- 2.6.1 k-Nearest neighbor classifier -- 2.6.2 Linear discriminant analysis -- 2.6.3 Support vector machine -- 2.6.4 Artificial neural network -- 2.7 Brain-computer interface applications -- 2.7.1 Communication -- 2.7.2 Motor restoration -- 2.7.3 Environmental control -- 2.7.4 Locomotion -- 2.7.5 Entertainment -- 2.7.6 Other brain-computer interface applications -- 2.8 Conclusion -- References -- 3 Transforming pharma logistics with the Internet of things -- 3.1 Introduction -- 3.1.1 What is the Internet of things? -- 3.1.2 Internet of things in logistics and the supply chain -- 3.2 Growth of pharmaceutical industries -- 3.2.1 Rising demand -- 3.2.2 Advent of Internet of things -- 3.2.3 Internet of things-based pharma architecture -- 3.2.3.1 Perception layer -- 3.2.3.2 Network transmission layer -- 3.2.3.3 Support/middleware layer -- 3.2.3.4 Application layer -- 3.3 Applications of Internet of things in pharmaceutical logistics -- 3.3.1 Manufacturing of drugs and equipment -- 3.3.1.1 Unique identification number -- 3.3.1.2 Real-time location system -- 3.3.1.3 Sensors -- 3.3.1.4 Cloud computing -- 3.3.1.5 Communication technologies -- 3.3.2 Warehouse management</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3.2.1 Design goals for a pharmaceutical warehouse management system -- 3.3.2.2 Architecture of a pharmaceutical warehouse management system -- 3.3.3 Shipment and tracking -- 3.3.4 Quality control of drugs -- 3.4 Challenges in pharma logistics -- 3.4.1 Supply chain visibility -- 3.4.2 Security issues -- 3.4.3 Temperature control -- 3.4.4 Data security -- 3.5 Conquering pharma logistics with Internet of things -- 3.5.1 Smart warehousing -- 3.5.1.1 Business benefits of smart warehousing -- 3.5.1.1.1 Increasing operational efficiency in the warehouse -- 3.5.1.1.2 Maintain desired storage conditions -- 3.5.1.1.3 Orient production and demand -- 3.5.1.1.4 Increase manufacturing efficiency -- 3.5.1.2 Basic workflow of a smart warehouse management system -- 3.5.2 Installing radio frequency identification tags -- 3.5.3 Real-time shipment tracking -- 3.5.3.1 Ensuring product integrity and traceability -- 3.5.3.2 Reduce inventory costs -- 3.5.4 Environmental sensor installation -- References -- Further reading -- 4 Drug identification and interaction checking using the Internet of Things -- 4.1 Introduction -- 4.2 Internet of Things based smart devices used in pharma and health care -- 4.2.1 Organ on a chip -- 4.2.2 Chip in a pill -- 4.2.3 Google glass -- 4.2.4 iBeacons -- 4.2.5 Sensors in drug-delivery devices -- 4.2.6 Smart wheelchairs -- 4.2.7 Wristbands -- 4.3 Role of the Internet of Things across various stages in the healthcare value chain -- 4.3.1 Stage 1: Research and development -- 4.3.2 Stage 2: Supply chain -- 4.3.3 Stage 3: Marketing and sales -- 4.3.4 Stage 4: End users -- 4.4 Key benefits of the Internet of Things to the life sciences industry -- 4.5 Rule-based system -- 4.5.1 Allergies -- 4.5.2 Active ingredient interactions -- 4.5.3 Drug loop -- 4.5.4 Renal impairment -- 4.5.5 Pregnancy -- 4.5.6 Optimization of absorption</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.6 Social and cultural factors associated with drug abuse in adolescents -- 4.6.1 Parental influence -- 4.6.2 Family structure -- 4.6.3 Peer influence -- 4.6.4 Role models -- 4.6.5 Advertising and promotion -- 4.6.6 Socioeconomic factors -- 4.6.7 Availability -- 4.6.8 Knowledge, attitudes, and beliefs -- 4.6.9 Street children and drug abuse -- 4.7 What happens to your brain when you take drugs? -- 4.7.1 Challenges for drug treatment and care -- 4.8 What causes drug abuse? -- 4.8.1 Commonly abused drugs -- 4.8.1.1 Alcohol -- 4.8.1.2 Anabolic steroids -- 4.8.1.3 Club drugs -- 4.8.1.4 Cocaine -- 4.8.1.5 Heroin -- 4.8.1.6 Inhalants -- 4.8.1.7 Marijuana -- 4.8.1.8 Methamphetamines -- 4.8.2 Prescription drugs -- 4.8.3 Stages of drug abuse -- 4.8.4 Treating drug abuse -- 4.8.5 Detoxification -- 4.8.6 Preventing drug abuse -- 4.8.7 Effects of drug abuse and addiction -- 4.9 The effects of drug abuse on health -- 4.9.1 Drug effects on behavior -- 4.9.2 Effects of drug abuse on unborn babies -- 4.10 Types of drugs -- 4.10.1 Stimulants -- 4.10.1.1 Risks of stimulant abuse -- 4.10.2 Depressants -- 4.10.3 Alcohol as a depressant -- 4.10.4 Tobacco as a depressant -- 4.10.4.1 Risks of depressant abuse -- 4.10.5 Hallucinogens -- 4.10.5.1 Risks of hallucinogen abuse -- 4.10.6 Dissociatives -- 4.10.6.1 Risks of dissociative abuse -- 4.10.7 Opioids -- 4.10.7.1 Risks of opioid abuse -- 4.10.8 Inhalants -- 4.10.8.1 Risks of inhalant abuse -- 4.10.9 Cannabis -- 4.10.9.1 Risks of cannabis abuse -- 4.11 Conclusions -- References -- 5 Accelerating data acquisition process in the pharmaceutical industry using Internet of Things -- 5.1 Introduction -- 5.1.1 Architectural framework of the Internet of Things -- 5.1.1.1 Sensing layer -- 5.1.1.2 Network layer -- 5.1.1.3 Service layer -- 5.1.1.4 Interface layer -- 5.1.2 Overview of the pharmaceutical industry</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">5.1.3 The Internet of Things revolution in the pharmaceutical industry</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Balas, Valentina E.</subfield><subfield code="d">1956-</subfield><subfield code="0">(DE-588)1202958311</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Solanki, Vijender Kumar</subfield><subfield code="d">1980-</subfield><subfield code="0">(DE-588)1139801074</subfield><subfield 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| id | DE-604.BV047441666 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:01:23Z |
| indexdate | 2025-04-07T16:00:47Z |
| institution | BVB |
| isbn | 9780128213278 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032843818 |
| oclc_num | 1155322128 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xxvii, 353 Seiten) Illustrationen |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Academic Press |
| record_format | marc |
| spelling | An industrial IoT approach for pharmaceutical industry growth volume 2 edited by Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar London, United Kingdom ; San Diego, CA, United States ; Cambridge, MA, United States ; Kidlington, Oxford, United Kingdom Academic Press [2020] © 2020 1 Online-Ressource (xxvii, 353 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Front Cover -- An Industrial IoT Approach for Pharmaceutical Industry Growth -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- About the book -- 1 Medical big data mining and processing in e-health care -- 1.1 Introduction -- 1.1.1 Types of big data -- 1.1.1.1 Structured -- 1.1.1.2 Unstructured -- 1.1.1.3 Semistructured -- 1.1.2 Characteristics of big data -- 1.1.2.1 Variety -- 1.1.2.2 Velocity -- 1.1.2.3 Volume -- 1.1.3 Integration of big data with medical imaging -- 1.1.4 Advantages of health-care data management -- 1.1.5 Challenges of health-care data management -- 1.1.6 Health care as a big data database -- 1.1.7 Benefits of medical big data -- 1.2 Architecture of big data in health care -- 1.2.1 Batch processing layer -- 1.2.2 Data acquisition -- 1.2.3 Electronic health-care records -- 1.2.4 Biomedical images -- 1.2.5 Social network analysis -- 1.2.6 Sensing data -- 1.2.7 Cell phones -- 1.2.8 Semantic module -- 1.3 Preparation of data -- 1.3.1 Data filtering -- 1.3.2 Data cleaning -- 1.3.3 Noise treatment -- 1.4 Feature extraction and feature selection -- 1.5 Predictive model design -- 1.6 Data storage -- 1.7 Stream processing layer -- 1.7.1 Data synchronization -- 1.7.2 Adaptive learning -- 1.7.3 Adaptive preprocessor -- 1.7.4 Adaptive predictor -- 1.8 Query processor -- 1.9 Visualization layer -- 1.10 Use of big data in biomedical research -- 1.11 Companies using big data in health care -- 1.11.1 Dignity health: analytics helps prevent deadly infections -- 1.11.2 Express scripts: better decisions, healthier outcomes with big data -- 1.11.3 United health care: monitoring fraud and waste, improving clinical outcomes -- 1.12 Other opportunities for big data in health care -- 1.12.1 Episode analytics -- 1.13 Use of health care in big data analytics -- 1.14 Unique features of big data in health care 1.14.1 Heterogeneity -- 1.14.2 Incompleteness -- 1.14.3 Data privacy -- 1.14.4 Ownership -- 1.15 Big data applications in health care -- 1.16 Internet of Things-based medical image processing -- 1.16.1 Patient information management -- 1.16.2 Medical emergency management -- 1.16.3 Medical waste information management -- 1.16.4 Drug storage -- 1.16.5 Combating pharmaceutical errors -- 1.16.6 Medical equipment and drug tracking -- 1.16.7 Connected information sharing -- 1.16.8 Newborn antikidnapping system -- 1.16.9 Alarm system -- 1.17 Internet of Things -- 1.18 Use of the Internet of Things in health care -- 1.18.1 Remote patient monitoring -- 1.18.2 Wearables -- 1.18.3 Better drug management -- 1.18.4 Hospital management -- 1.19 Health care in various countries -- 1.19.1 Simultaneous reporting and monitoring -- 1.19.2 End-to-end connectivity and affordability -- 1.19.3 Data assortment and analysis -- 1.19.4 Research -- 1.19.5 Data security and privacy -- 1.20 Disadvantages of Internet of Things in health care -- 1.21 Medical Internet of Things and cyber-physical systems -- 1.22 Social network data -- 1.23 History of the Internet of Things in health care -- 1.24 Challenges of the Internet of Things in health care -- 1.25 Future of the Internet of Things in health care -- 1.26 Internet of Things with ThingSpeak -- 1.27 Introduction to the cloud -- 1.27.1 Cloud computing -- 1.27.2 Cloud storage -- 1.28 ThingSpeak channels -- 1.28.1 Channel setting of the cloud -- 1.28.2 Using the channel -- 1.29 Application working -- 1.29.1 Doctors -- 1.29.2 Patients -- 1.29.3 Login credentials -- 1.29.4 Registration credentials -- 1.30 Summary -- References -- 2 Brain-computer interfaces and their applications -- 2.1 Introduction -- 2.1.1 Neuroimaging approaches in brain-computer interfaces -- 2.1.2 Electroencephalography -- 2.1.3 Magnetoencephalography 2.1.4 Electrocorticography -- 2.1.5 Intracortical neuron recording -- 2.1.6 Functional magnetic resonance imaging -- 2.1.7 Near-infrared spectroscopy -- 2.2 Control signal types in brain-computer interfaces -- 2.2.1 Visual-evoked potentials -- 2.2.2 Slow cortical potentials -- 2.2.3 P300-evoked potentials -- 2.2.4 Sensorimotor rhythms -- 2.3 Types of brain-computer interface -- 2.4 Features extraction and selection -- 2.4.1 Principal component analysis -- 2.4.2 Independent component analysis -- 2.4.3 Autoregressive components -- 2.4.4 Matched filtering -- 2.4.5 Wavelet transformation -- 2.5 Artifacts in brain-computer interfaces -- 2.6 Classification algorithms -- 2.6.1 k-Nearest neighbor classifier -- 2.6.2 Linear discriminant analysis -- 2.6.3 Support vector machine -- 2.6.4 Artificial neural network -- 2.7 Brain-computer interface applications -- 2.7.1 Communication -- 2.7.2 Motor restoration -- 2.7.3 Environmental control -- 2.7.4 Locomotion -- 2.7.5 Entertainment -- 2.7.6 Other brain-computer interface applications -- 2.8 Conclusion -- References -- 3 Transforming pharma logistics with the Internet of things -- 3.1 Introduction -- 3.1.1 What is the Internet of things? -- 3.1.2 Internet of things in logistics and the supply chain -- 3.2 Growth of pharmaceutical industries -- 3.2.1 Rising demand -- 3.2.2 Advent of Internet of things -- 3.2.3 Internet of things-based pharma architecture -- 3.2.3.1 Perception layer -- 3.2.3.2 Network transmission layer -- 3.2.3.3 Support/middleware layer -- 3.2.3.4 Application layer -- 3.3 Applications of Internet of things in pharmaceutical logistics -- 3.3.1 Manufacturing of drugs and equipment -- 3.3.1.1 Unique identification number -- 3.3.1.2 Real-time location system -- 3.3.1.3 Sensors -- 3.3.1.4 Cloud computing -- 3.3.1.5 Communication technologies -- 3.3.2 Warehouse management 3.3.2.1 Design goals for a pharmaceutical warehouse management system -- 3.3.2.2 Architecture of a pharmaceutical warehouse management system -- 3.3.3 Shipment and tracking -- 3.3.4 Quality control of drugs -- 3.4 Challenges in pharma logistics -- 3.4.1 Supply chain visibility -- 3.4.2 Security issues -- 3.4.3 Temperature control -- 3.4.4 Data security -- 3.5 Conquering pharma logistics with Internet of things -- 3.5.1 Smart warehousing -- 3.5.1.1 Business benefits of smart warehousing -- 3.5.1.1.1 Increasing operational efficiency in the warehouse -- 3.5.1.1.2 Maintain desired storage conditions -- 3.5.1.1.3 Orient production and demand -- 3.5.1.1.4 Increase manufacturing efficiency -- 3.5.1.2 Basic workflow of a smart warehouse management system -- 3.5.2 Installing radio frequency identification tags -- 3.5.3 Real-time shipment tracking -- 3.5.3.1 Ensuring product integrity and traceability -- 3.5.3.2 Reduce inventory costs -- 3.5.4 Environmental sensor installation -- References -- Further reading -- 4 Drug identification and interaction checking using the Internet of Things -- 4.1 Introduction -- 4.2 Internet of Things based smart devices used in pharma and health care -- 4.2.1 Organ on a chip -- 4.2.2 Chip in a pill -- 4.2.3 Google glass -- 4.2.4 iBeacons -- 4.2.5 Sensors in drug-delivery devices -- 4.2.6 Smart wheelchairs -- 4.2.7 Wristbands -- 4.3 Role of the Internet of Things across various stages in the healthcare value chain -- 4.3.1 Stage 1: Research and development -- 4.3.2 Stage 2: Supply chain -- 4.3.3 Stage 3: Marketing and sales -- 4.3.4 Stage 4: End users -- 4.4 Key benefits of the Internet of Things to the life sciences industry -- 4.5 Rule-based system -- 4.5.1 Allergies -- 4.5.2 Active ingredient interactions -- 4.5.3 Drug loop -- 4.5.4 Renal impairment -- 4.5.5 Pregnancy -- 4.5.6 Optimization of absorption 4.6 Social and cultural factors associated with drug abuse in adolescents -- 4.6.1 Parental influence -- 4.6.2 Family structure -- 4.6.3 Peer influence -- 4.6.4 Role models -- 4.6.5 Advertising and promotion -- 4.6.6 Socioeconomic factors -- 4.6.7 Availability -- 4.6.8 Knowledge, attitudes, and beliefs -- 4.6.9 Street children and drug abuse -- 4.7 What happens to your brain when you take drugs? -- 4.7.1 Challenges for drug treatment and care -- 4.8 What causes drug abuse? -- 4.8.1 Commonly abused drugs -- 4.8.1.1 Alcohol -- 4.8.1.2 Anabolic steroids -- 4.8.1.3 Club drugs -- 4.8.1.4 Cocaine -- 4.8.1.5 Heroin -- 4.8.1.6 Inhalants -- 4.8.1.7 Marijuana -- 4.8.1.8 Methamphetamines -- 4.8.2 Prescription drugs -- 4.8.3 Stages of drug abuse -- 4.8.4 Treating drug abuse -- 4.8.5 Detoxification -- 4.8.6 Preventing drug abuse -- 4.8.7 Effects of drug abuse and addiction -- 4.9 The effects of drug abuse on health -- 4.9.1 Drug effects on behavior -- 4.9.2 Effects of drug abuse on unborn babies -- 4.10 Types of drugs -- 4.10.1 Stimulants -- 4.10.1.1 Risks of stimulant abuse -- 4.10.2 Depressants -- 4.10.3 Alcohol as a depressant -- 4.10.4 Tobacco as a depressant -- 4.10.4.1 Risks of depressant abuse -- 4.10.5 Hallucinogens -- 4.10.5.1 Risks of hallucinogen abuse -- 4.10.6 Dissociatives -- 4.10.6.1 Risks of dissociative abuse -- 4.10.7 Opioids -- 4.10.7.1 Risks of opioid abuse -- 4.10.8 Inhalants -- 4.10.8.1 Risks of inhalant abuse -- 4.10.9 Cannabis -- 4.10.9.1 Risks of cannabis abuse -- 4.11 Conclusions -- References -- 5 Accelerating data acquisition process in the pharmaceutical industry using Internet of Things -- 5.1 Introduction -- 5.1.1 Architectural framework of the Internet of Things -- 5.1.1.1 Sensing layer -- 5.1.1.2 Network layer -- 5.1.1.3 Service layer -- 5.1.1.4 Interface layer -- 5.1.2 Overview of the pharmaceutical industry 5.1.3 The Internet of Things revolution in the pharmaceutical industry Balas, Valentina E. 1956- (DE-588)1202958311 edt Solanki, Vijender Kumar 1980- (DE-588)1139801074 edt Kumar, Raghvendra 1987- (DE-588)1156490286 edt Erscheint auch als Balas, Valentina Emilia An Industrial IoT Approach for Pharmaceutical Industry Growth San Diego : Elsevier Science & Technology,c2020 Druck-Ausgabe 978-0-12-821326-1 |
| spellingShingle | An industrial IoT approach for pharmaceutical industry growth volume 2 Front Cover -- An Industrial IoT Approach for Pharmaceutical Industry Growth -- Copyright Page -- Contents -- List of contributors -- About the editors -- Preface -- About the book -- 1 Medical big data mining and processing in e-health care -- 1.1 Introduction -- 1.1.1 Types of big data -- 1.1.1.1 Structured -- 1.1.1.2 Unstructured -- 1.1.1.3 Semistructured -- 1.1.2 Characteristics of big data -- 1.1.2.1 Variety -- 1.1.2.2 Velocity -- 1.1.2.3 Volume -- 1.1.3 Integration of big data with medical imaging -- 1.1.4 Advantages of health-care data management -- 1.1.5 Challenges of health-care data management -- 1.1.6 Health care as a big data database -- 1.1.7 Benefits of medical big data -- 1.2 Architecture of big data in health care -- 1.2.1 Batch processing layer -- 1.2.2 Data acquisition -- 1.2.3 Electronic health-care records -- 1.2.4 Biomedical images -- 1.2.5 Social network analysis -- 1.2.6 Sensing data -- 1.2.7 Cell phones -- 1.2.8 Semantic module -- 1.3 Preparation of data -- 1.3.1 Data filtering -- 1.3.2 Data cleaning -- 1.3.3 Noise treatment -- 1.4 Feature extraction and feature selection -- 1.5 Predictive model design -- 1.6 Data storage -- 1.7 Stream processing layer -- 1.7.1 Data synchronization -- 1.7.2 Adaptive learning -- 1.7.3 Adaptive preprocessor -- 1.7.4 Adaptive predictor -- 1.8 Query processor -- 1.9 Visualization layer -- 1.10 Use of big data in biomedical research -- 1.11 Companies using big data in health care -- 1.11.1 Dignity health: analytics helps prevent deadly infections -- 1.11.2 Express scripts: better decisions, healthier outcomes with big data -- 1.11.3 United health care: monitoring fraud and waste, improving clinical outcomes -- 1.12 Other opportunities for big data in health care -- 1.12.1 Episode analytics -- 1.13 Use of health care in big data analytics -- 1.14 Unique features of big data in health care 1.14.1 Heterogeneity -- 1.14.2 Incompleteness -- 1.14.3 Data privacy -- 1.14.4 Ownership -- 1.15 Big data applications in health care -- 1.16 Internet of Things-based medical image processing -- 1.16.1 Patient information management -- 1.16.2 Medical emergency management -- 1.16.3 Medical waste information management -- 1.16.4 Drug storage -- 1.16.5 Combating pharmaceutical errors -- 1.16.6 Medical equipment and drug tracking -- 1.16.7 Connected information sharing -- 1.16.8 Newborn antikidnapping system -- 1.16.9 Alarm system -- 1.17 Internet of Things -- 1.18 Use of the Internet of Things in health care -- 1.18.1 Remote patient monitoring -- 1.18.2 Wearables -- 1.18.3 Better drug management -- 1.18.4 Hospital management -- 1.19 Health care in various countries -- 1.19.1 Simultaneous reporting and monitoring -- 1.19.2 End-to-end connectivity and affordability -- 1.19.3 Data assortment and analysis -- 1.19.4 Research -- 1.19.5 Data security and privacy -- 1.20 Disadvantages of Internet of Things in health care -- 1.21 Medical Internet of Things and cyber-physical systems -- 1.22 Social network data -- 1.23 History of the Internet of Things in health care -- 1.24 Challenges of the Internet of Things in health care -- 1.25 Future of the Internet of Things in health care -- 1.26 Internet of Things with ThingSpeak -- 1.27 Introduction to the cloud -- 1.27.1 Cloud computing -- 1.27.2 Cloud storage -- 1.28 ThingSpeak channels -- 1.28.1 Channel setting of the cloud -- 1.28.2 Using the channel -- 1.29 Application working -- 1.29.1 Doctors -- 1.29.2 Patients -- 1.29.3 Login credentials -- 1.29.4 Registration credentials -- 1.30 Summary -- References -- 2 Brain-computer interfaces and their applications -- 2.1 Introduction -- 2.1.1 Neuroimaging approaches in brain-computer interfaces -- 2.1.2 Electroencephalography -- 2.1.3 Magnetoencephalography 2.1.4 Electrocorticography -- 2.1.5 Intracortical neuron recording -- 2.1.6 Functional magnetic resonance imaging -- 2.1.7 Near-infrared spectroscopy -- 2.2 Control signal types in brain-computer interfaces -- 2.2.1 Visual-evoked potentials -- 2.2.2 Slow cortical potentials -- 2.2.3 P300-evoked potentials -- 2.2.4 Sensorimotor rhythms -- 2.3 Types of brain-computer interface -- 2.4 Features extraction and selection -- 2.4.1 Principal component analysis -- 2.4.2 Independent component analysis -- 2.4.3 Autoregressive components -- 2.4.4 Matched filtering -- 2.4.5 Wavelet transformation -- 2.5 Artifacts in brain-computer interfaces -- 2.6 Classification algorithms -- 2.6.1 k-Nearest neighbor classifier -- 2.6.2 Linear discriminant analysis -- 2.6.3 Support vector machine -- 2.6.4 Artificial neural network -- 2.7 Brain-computer interface applications -- 2.7.1 Communication -- 2.7.2 Motor restoration -- 2.7.3 Environmental control -- 2.7.4 Locomotion -- 2.7.5 Entertainment -- 2.7.6 Other brain-computer interface applications -- 2.8 Conclusion -- References -- 3 Transforming pharma logistics with the Internet of things -- 3.1 Introduction -- 3.1.1 What is the Internet of things? -- 3.1.2 Internet of things in logistics and the supply chain -- 3.2 Growth of pharmaceutical industries -- 3.2.1 Rising demand -- 3.2.2 Advent of Internet of things -- 3.2.3 Internet of things-based pharma architecture -- 3.2.3.1 Perception layer -- 3.2.3.2 Network transmission layer -- 3.2.3.3 Support/middleware layer -- 3.2.3.4 Application layer -- 3.3 Applications of Internet of things in pharmaceutical logistics -- 3.3.1 Manufacturing of drugs and equipment -- 3.3.1.1 Unique identification number -- 3.3.1.2 Real-time location system -- 3.3.1.3 Sensors -- 3.3.1.4 Cloud computing -- 3.3.1.5 Communication technologies -- 3.3.2 Warehouse management 3.3.2.1 Design goals for a pharmaceutical warehouse management system -- 3.3.2.2 Architecture of a pharmaceutical warehouse management system -- 3.3.3 Shipment and tracking -- 3.3.4 Quality control of drugs -- 3.4 Challenges in pharma logistics -- 3.4.1 Supply chain visibility -- 3.4.2 Security issues -- 3.4.3 Temperature control -- 3.4.4 Data security -- 3.5 Conquering pharma logistics with Internet of things -- 3.5.1 Smart warehousing -- 3.5.1.1 Business benefits of smart warehousing -- 3.5.1.1.1 Increasing operational efficiency in the warehouse -- 3.5.1.1.2 Maintain desired storage conditions -- 3.5.1.1.3 Orient production and demand -- 3.5.1.1.4 Increase manufacturing efficiency -- 3.5.1.2 Basic workflow of a smart warehouse management system -- 3.5.2 Installing radio frequency identification tags -- 3.5.3 Real-time shipment tracking -- 3.5.3.1 Ensuring product integrity and traceability -- 3.5.3.2 Reduce inventory costs -- 3.5.4 Environmental sensor installation -- References -- Further reading -- 4 Drug identification and interaction checking using the Internet of Things -- 4.1 Introduction -- 4.2 Internet of Things based smart devices used in pharma and health care -- 4.2.1 Organ on a chip -- 4.2.2 Chip in a pill -- 4.2.3 Google glass -- 4.2.4 iBeacons -- 4.2.5 Sensors in drug-delivery devices -- 4.2.6 Smart wheelchairs -- 4.2.7 Wristbands -- 4.3 Role of the Internet of Things across various stages in the healthcare value chain -- 4.3.1 Stage 1: Research and development -- 4.3.2 Stage 2: Supply chain -- 4.3.3 Stage 3: Marketing and sales -- 4.3.4 Stage 4: End users -- 4.4 Key benefits of the Internet of Things to the life sciences industry -- 4.5 Rule-based system -- 4.5.1 Allergies -- 4.5.2 Active ingredient interactions -- 4.5.3 Drug loop -- 4.5.4 Renal impairment -- 4.5.5 Pregnancy -- 4.5.6 Optimization of absorption 4.6 Social and cultural factors associated with drug abuse in adolescents -- 4.6.1 Parental influence -- 4.6.2 Family structure -- 4.6.3 Peer influence -- 4.6.4 Role models -- 4.6.5 Advertising and promotion -- 4.6.6 Socioeconomic factors -- 4.6.7 Availability -- 4.6.8 Knowledge, attitudes, and beliefs -- 4.6.9 Street children and drug abuse -- 4.7 What happens to your brain when you take drugs? -- 4.7.1 Challenges for drug treatment and care -- 4.8 What causes drug abuse? -- 4.8.1 Commonly abused drugs -- 4.8.1.1 Alcohol -- 4.8.1.2 Anabolic steroids -- 4.8.1.3 Club drugs -- 4.8.1.4 Cocaine -- 4.8.1.5 Heroin -- 4.8.1.6 Inhalants -- 4.8.1.7 Marijuana -- 4.8.1.8 Methamphetamines -- 4.8.2 Prescription drugs -- 4.8.3 Stages of drug abuse -- 4.8.4 Treating drug abuse -- 4.8.5 Detoxification -- 4.8.6 Preventing drug abuse -- 4.8.7 Effects of drug abuse and addiction -- 4.9 The effects of drug abuse on health -- 4.9.1 Drug effects on behavior -- 4.9.2 Effects of drug abuse on unborn babies -- 4.10 Types of drugs -- 4.10.1 Stimulants -- 4.10.1.1 Risks of stimulant abuse -- 4.10.2 Depressants -- 4.10.3 Alcohol as a depressant -- 4.10.4 Tobacco as a depressant -- 4.10.4.1 Risks of depressant abuse -- 4.10.5 Hallucinogens -- 4.10.5.1 Risks of hallucinogen abuse -- 4.10.6 Dissociatives -- 4.10.6.1 Risks of dissociative abuse -- 4.10.7 Opioids -- 4.10.7.1 Risks of opioid abuse -- 4.10.8 Inhalants -- 4.10.8.1 Risks of inhalant abuse -- 4.10.9 Cannabis -- 4.10.9.1 Risks of cannabis abuse -- 4.11 Conclusions -- References -- 5 Accelerating data acquisition process in the pharmaceutical industry using Internet of Things -- 5.1 Introduction -- 5.1.1 Architectural framework of the Internet of Things -- 5.1.1.1 Sensing layer -- 5.1.1.2 Network layer -- 5.1.1.3 Service layer -- 5.1.1.4 Interface layer -- 5.1.2 Overview of the pharmaceutical industry 5.1.3 The Internet of Things revolution in the pharmaceutical industry |
| title | An industrial IoT approach for pharmaceutical industry growth volume 2 |
| title_auth | An industrial IoT approach for pharmaceutical industry growth volume 2 |
| title_exact_search | An industrial IoT approach for pharmaceutical industry growth volume 2 |
| title_exact_search_txtP | An industrial IoT approach for pharmaceutical industry growth volume 2 |
| title_full | An industrial IoT approach for pharmaceutical industry growth volume 2 edited by Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar |
| title_fullStr | An industrial IoT approach for pharmaceutical industry growth volume 2 edited by Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar |
| title_full_unstemmed | An industrial IoT approach for pharmaceutical industry growth volume 2 edited by Valentina Emilia Balas, Vijender Kumar Solanki, Raghvendra Kumar |
| title_short | An industrial IoT approach for pharmaceutical industry growth |
| title_sort | an industrial iot approach for pharmaceutical industry growth volume 2 |
| title_sub | volume 2 |
| work_keys_str_mv | AT balasvalentinae anindustrialiotapproachforpharmaceuticalindustrygrowthvolume2 AT solankivijenderkumar anindustrialiotapproachforpharmaceuticalindustrygrowthvolume2 AT kumarraghvendra anindustrialiotapproachforpharmaceuticalindustrygrowthvolume2 |