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Artículos de revistas sobre el tema "Patient monitoring"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 28 de julio de 2024

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1

Palkhede, Nakul S., Sachin D. Mali y Prof Manisha S. Shelar. "IoT Based Patient Monitoring". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 de junio de 2018): 971–75. http://dx.doi.org/10.31142/ijtsrd14216.

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Kumaran M, Senthil, Chittoria R.K, Bibilash B.S., Friji M.T, Mohapatra D.P. y Dineshkumar S. "Tele-Monitoring of Patient in ICU". New Indian Journal of Surgery 8, n.º 3 (2017): 445–47. http://dx.doi.org/10.21088/nijs.0976.4747.8317.28.

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3

Morton, Anthony. "PATIENT MONITORING". Australian and New Zealand Journal of Surgery 46, n.º 4 (21 de enero de 2008): 304–9. http://dx.doi.org/10.1111/j.1445-2197.1976.tb03238.x.

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4

Emmett, C. y P. Hutton. "Patient monitoring". British Medical Bulletin 44, n.º 2 (1988): 302–21. http://dx.doi.org/10.1093/oxfordjournals.bmb.a072253.

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5

Lakshmi, Ms M., Sai Sri Gaddala, Sandhya Rani Konda, Sahithi Gaja y Spoorthi Begari. "Remote Monitoring of Heart Patient with Auto Defibrillator". International Journal of Research Publication and Reviews 5, n.º 5 (2 de mayo de 2024): 1010–19. http://dx.doi.org/10.55248/gengpi.5.0524.1120.

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6

Sukuvaara, T., A. Heikelä, EMJ Koskp, H. Nieminen y M. Sydänmaa. "Computerized patient monitoring". Acta Anaesthesiologica Scandinavica 37 (diciembre de 1993): 185–89. http://dx.doi.org/10.1111/j.1399-6576.1993.tb03672.x.

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7

Doss, Marilyn C. y John W. Waterbor. "Monitoring Patient Satisfaction". Oncology Issues 10, n.º 5 (septiembre de 1995): 16–18. http://dx.doi.org/10.1080/10463356.1995.11904559.

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Malone, Mary P. y Elizabeth L. Pollock. "Monitoring Patient Satisfaction". Oncology Issues 10, n.º 5 (septiembre de 1995): 19–21. http://dx.doi.org/10.1080/10463356.1995.11904560.

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9

Jenner, C. A. y J. A. Wilson. "Continuous patient monitoring". Anaesthesia 56, n.º 6 (junio de 2001): 591–92. http://dx.doi.org/10.1111/j.1365-2044.2001.2094-12.x.

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10

Chishti, Ahmed y Iain A. Walker. "Patient monitoring techniques". Surgery (Oxford) 37, n.º 8 (agosto de 2019): 450–59. http://dx.doi.org/10.1016/j.mpsur.2019.05.002.

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11

&NA;. "patient monitoring: products". Nursing Management (Springhouse) 34, n.º 12 (diciembre de 2003): 32. http://dx.doi.org/10.1097/00006247-200312000-00012.

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12

Lepper, Joe. "Remote patient monitoring". Primary Health Care 19, n.º 6 (9 de julio de 2009): 37–38. http://dx.doi.org/10.7748/phc.19.6.37.s27.

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13

Hudson, F. R. "TLD patient monitoring". British Journal of Radiology 60, n.º 710 (febrero de 1987): 205. http://dx.doi.org/10.1259/0007-1285-60-710-205-a.

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14

Blanchet, Kevin D. "Remote Patient Monitoring". Telemedicine and e-Health 14, n.º 2 (marzo de 2008): 127–30. http://dx.doi.org/10.1089/tmj.2008.9989.

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15

Berenholtz, Sean M. y Peter J. Pronovost. "Monitoring Patient Safety". Critical Care Clinics 23, n.º 3 (julio de 2007): 659–73. http://dx.doi.org/10.1016/j.ccc.2007.05.003.

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16

Gupta, Anannya. "Remote patient monitoring". Journal of Applied Sciences and Clinical Practice 4, n.º 2 (2023): 64. http://dx.doi.org/10.4103/jascp.jascp_42_22.

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17

Hunt, Marion. "Monitoring the Critically III Patient Monitoring the Critically III Patient". Nursing Standard 16, n.º 45 (24 de julio de 2002): 29. http://dx.doi.org/10.7748/ns2002.07.16.45.29.b193.

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18

Gokhale, Prerna, Vighnesh Rasal, Sahil Amberkar y Sanika Sonawane. "Patient Monitoring using Geofencing". International Journal for Research in Applied Science and Engineering Technology 10, n.º 11 (30 de noviembre de 2022): 427–30. http://dx.doi.org/10.22214/ijraset.2022.47339.

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Abstract: In these modern times, technologies are used to efficiently monitor the location of people. In this project,geofencing technology is used to locate and monitor theLocation of hospital patients. With Geofence the movement of patients can be virtually concentrated within the Hospital premises without hindering real activities by placing boundaries near the patient's area. By using Global Positioning System (GPS) tech, signals of a patient's device can be traced and monitored. If the patient goes beyond the geofenced area, the administrator will receive a SMS from the device and could handle the situation so that the patients don't get lost. Patient's location can be tracked by the device by using the GPS fitted on the patient's device, and the patient is unaware of it. To satisfy these conditions, essential processes must be done, such as Analysis of Geofenced area and Sending SMS or notifications on the desired Mobile Phone that is with the administrator. All of this will create an environment that can supervise patients that could be of any programmed distance, In this project for demonstration we have taken geofence radius as 30 metres. This device will receive a message automatically when the patient is out of the Geofenced area. The shortest distance between the patient and the administrator is calculated by using Haversine formula.

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19

Kanchikere, Jayanna, A. K. Ghosh y Kalyankumar Kalyankumar. "Embedded Patient Monitoring System". International Journal of Power Electronics and Drive Systems (IJPEDS) 10, n.º 1 (1 de marzo de 2019): 388. http://dx.doi.org/10.11591/ijpeds.v10.i1.pp388-397.

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<p>ICU remains for Intensive Care Unit, a place in the recuperating office where wiped out patients are checked eagerly. Commonly, the patient-staff extent is low and the LIFE-SAVING EQUIPMENT used is outstandingly bleeding edge Generally ICU is a healing facility for course of action of genuine nursing and remedial consideration of essentially wiped out patients, depicted by high bore and measure of unending nursing and restorative supervision and by use of cutting edge checking and resuscitative equipment The patients in the ICU require a predictable seeing of their Temperature and pulse circulatory strain. This undertaking is a working model, which wires sensors to evaluate imperative parameters specifically the Temperature, Respiratory temperature and Heart Beat. The sensors are interfaced to PC, with the objective that the condition of a patient can be explored by masters in any bit of the center wherever they are. At whatever point there is a variety from the standard felt by the patient, the particular patient will give an alert movement, by which the pro can race to the patient. Despite when the patient is in a careless condition, each one of the parameters will be identified and pro will be admonished, thusly it diminishes master's remaining task at hand and besides gives more correct results .Our endeavor is a working model which wires sensors to measure each one of these parameters like body temperature, Respiratory Temp and Heart Beat rate and trade it to the PC, with the objective that the patient condition can be examined to by authorities in any bit of the recuperating focus wherever they are. In this way it decreases experts work stack and besides gives more exact results, wherever there is a variety from the standard felt by the patient, we have in like manner combined saline watching system which gives an alert when the saline container going to cleanse.</p>

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20

M, Dhinakaran, Sowndarya S, Shobana R, Sivasankari R y Sindhu R. "Wireless Power Transfer to Biomedical Implants for Patient Monitoring". SIJ Transactions on Computer Networks & Communication Engineering 05, n.º 05 (18 de octubre de 2017): 10–12. http://dx.doi.org/10.9756/sijcnce/v5i5/05010160101.

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21

Patel, Priyanka. "Innovations in Cardiovascular Nursing: Telemedicine and Remote Patient Monitoring". International Journal of Research Publication and Reviews 4, n.º 10 (16 de octubre de 2023): 2296–99. http://dx.doi.org/10.55248/gengpi.4.1023.102713.

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22

Patel, Priyanka. "Innovations in Cardiovascular Nursing: Telemedicine and Remote Patient Monitoring". International Journal of Research Publication and Reviews 4, n.º 10 (16 de octubre de 2023): 2296–99. http://dx.doi.org/10.55248/gengpi.4.1023.102813.

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23

Kuck, Kai, Lars Lofgren y Carter Lybbert. "Anesthesia Patient Monitoring 2050". Anesthesia & Analgesia 138, n.º 2 (12 de enero de 2024): 273–83. http://dx.doi.org/10.1213/ane.0000000000006660.

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The monitoring of vital signs in patients undergoing anesthesia began with the very first case of anesthesia and has evolved alongside the development of anesthesiology ever since. Patient monitoring started out as a manually performed, intermittent, and qualitative assessment of the patient’s general well-being in the operating room. In its evolution, patient monitoring development has responded to the clinical need, for example, when critical incident studies in the 1980s found that many anesthesia adverse events could be prevented by improved monitoring, especially respiratory monitoring. It also facilitated and perhaps even enabled increasingly complex surgeries in increasingly higher-risk patients. For example, it would be very challenging to perform and provide anesthesia care during some of the very complex cardiovascular surgeries that are almost routine today without being able to simultaneously and reliably monitor multiple pressures in a variety of places in the circulatory system. Of course, anesthesia patient monitoring itself is enabled by technological developments in the world outside of the operating room. Throughout its history, anesthesia patient monitoring has taken advantage of advancements in material science (when nonthrombogenic polymers allowed the design of intravascular catheters, for example), in electronics and transducers, in computers, in displays, in information technology, and so forth. Slower product life cycles in medical devices mean that by carefully observing technologies such as consumer electronics, including user interfaces, it is possible to peek ahead and estimate with confidence the foundational technologies that will be used by patient monitors in the near future. Just as the discipline of anesthesiology has, the patient monitoring that accompanies it has come a long way from its beginnings in the mid-19th century. Extrapolating from careful observations of the prevailing trends that have shaped anesthesia patient monitoring historically, patient monitoring in the future will use noncontact technologies, will predict the trajectory of a patient’s vital signs, will add regional vital signs to the current systemic ones, and will facilitate directed and supervised anesthesia care over the broader scope that anesthesia will be responsible for.

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24

Sebastian, Sherin. "Remote Patient Monitoring System". International Journal of Distributed and Parallel systems 3, n.º 5 (30 de septiembre de 2012): 99–110. http://dx.doi.org/10.5121/ijdps.2012.3509.

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25

Ramya, V. "Embedded Patient Monitoring System". International Journal of Embedded Systems and Applications 1, n.º 2 (31 de diciembre de 2011): 51–63. http://dx.doi.org/10.5121/ijesa.2011.1205.

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26

P, Sabeena, Uma Sheeri y Divya Mourya. "Smart Patient Monitoring System". European Journal of Information Technologies and Computer Science 2, n.º 2 (29 de abril de 2022): 13–17. http://dx.doi.org/10.24018/compute.2022.2.2.53.

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During the current COVID-19 epidemic, IoT-based health monitoring devices could be incredibly useful for COVID-19 sufferers. This study develops an IoT-based system for real-time health monitoring that incorporates the patient's measured body temperature, pulse rate, and oxygen saturation, which are the most important critical care indicators. A liquid crystal display (LCD) shows the current temperature, heart rate, and oxygen saturation level, which can be easily linked with a mobile application for quick access. Using an Arduino Uno-based device, the recommended IoT-based technique was tested and verified on five human test subjects. The system's results were promising: the data collected by the system was saved fast. The system's results were found to be accurate when compared to other commercially available devices. IoT-based gadgets could be immensely valuable in saving people's lives during the COVID-19 epidemic.

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27

Crisafulli, Jacob. "Patient Monitoring Replacement Forecast". Journal of Clinical Engineering 47, n.º 1 (enero de 2022): 39–43. http://dx.doi.org/10.1097/jce.0000000000000507.

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28

Fudim, Marat, Michael Mirro y Hao-Ming Cheng. "Audicor Remote Patient Monitoring". JACC: Basic to Translational Science 7, n.º 3 (marzo de 2022): 313–15. http://dx.doi.org/10.1016/j.jacbts.2022.02.007.

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29

Hulme, Jonathan. "Monitoring the injured patient". Trauma 8, n.º 2 (abril de 2006): 85–93. http://dx.doi.org/10.1177/1460408606072761.

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30

Dres, Martin, Nuttapol Rittayamai y Laurent Brochard. "Monitoring patient–ventilator asynchrony". Current Opinion in Critical Care 22, n.º 3 (junio de 2016): 246–53. http://dx.doi.org/10.1097/mcc.0000000000000307.

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31

&NA;. "Enjoy “smarter” patient monitoring". Nursing 36, n.º 8 (agosto de 2006): 27. http://dx.doi.org/10.1097/00152193-200608000-00020.

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32

Horan, Brian. "Monitoring and Patient Safety". Anaesthesia and Intensive Care 16, n.º 1 (febrero de 1988): 5–6. http://dx.doi.org/10.1177/0310057x8801600102.

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33

Clarke, F. L. y P. A. Gaines. "Patient monitoring in radiology". Clinical Radiology 47, n.º 2 (febrero de 1993): 143. http://dx.doi.org/10.1016/s0009-9260(05)81194-8.

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34

Berman, Irwin R. "Monitoring and patient safety". Gastrointestinal Endoscopy 36, n.º 2 (marzo de 1990): 160. http://dx.doi.org/10.1016/s0016-5107(90)70979-0.

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35

Vorlickova, H., D. Navratilova y L. Pankracova. "Patient fall monitoring project". European Journal of Oncology Nursing 10, n.º 3 (julio de 2006): 221–22. http://dx.doi.org/10.1016/j.ejon.2006.04.018.

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36

Rosenthal, Kelli. "Enjoy ???smarter??? patient monitoring". Nursing Management (Springhouse) 37, n.º 5 (mayo de 2006): 52. http://dx.doi.org/10.1097/00006247-200605000-00013.

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37

Metcalfe, L. "Monitoring skills: diabetes". Nurse Prescriber 1, n.º 2 (febrero de 2004): 1–6. http://dx.doi.org/10.1017/s1467115804000173.

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SummaryThe extended independent and supplementary nurse prescribing programme implemented in 2003 is anticipated to have the potential to improve patient care by enabling faster access to medicine for people with chronic conditions such as Diabetes. Diabetes mellitus is a progressive, life–long, chronic condition which affects an estimated 1.4 million people in the UK and inevitably requires medication to control symptoms and prevent complications associated with the disease.The supplementary prescribing option of nurse prescribing is an opportunity for Diabetes Specialist Nurses (DSN) to provide timely and appropriate clinical management for patients with diabetes within an agreed management plan. The clinical management plan (CMP) is a patient specific document and can only be implemented to enable supplementary nurse prescribing upon agreement by the individual patient, the independent prescriber (doctor or dentist and supplementary prescriber (nurse or pharmacist). The CMP allows the DSN to manage the treatment of the patient by modifying diabetes therapies tailored to the needs of the individual to achieve treatment goals. The use of a patient specific CMP compliments the National service Framework for diabetes requirement that all patients have a care plan. Involving the patient by seeking agreement to implement the CMP may improve concordance and compliance with therapies and treatment decisions.Monitoring diabetes control is essential from diagnosis as the information received provides valuable feedback as to the effectiveness of therapies. Comprehensive diabetes education should be tailored to empower the patient with knowledge and skills to manage this chronic condition regardless of treatment regimes.Consideration should always be given to the type of diabetes, the potential effects on lifestyle and treatment options available today.

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38

Bathe, Manasi. "IOT Based Patient Health Monitoring System". International Journal for Research in Applied Science and Engineering Technology 10, n.º 7 (31 de julio de 2022): 1989–92. http://dx.doi.org/10.22214/ijraset.2022.45633.

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Abstract: Patients are facing a problematic situation of unforeseen demise due to the specific reason of heart problems and attack which is because of non existence of good medical maintenance to patients at the needed time. This is for specially monitoring old age patients and informing doctors and loved ones. So, we are proposing an innovative project to dodge such sudden death rates by using Patient Health Monitoring that uses sensor technology and uses the internet to communicate to the doctor in case of problems. This system uses temperature, heartbeat,ECG, Saline-level sensor for tracking patient’s health. These sensors are connected to the Arduino-uno. The patient health is tracked with the controller which is in turn interfaced to an LCD display, bluetooth and WI-FI connection to send the data to the web-server (Thinkspeak).In case of any abrupt changes in patient heart-rate or body temperature alert is sent about the patient using IoT. This system also shows patients temperature ,saline level and heartbeat tracked live data with timestamps over the internetwork. Thus, a Patient health monitoring system based on IoT uses the internet to effectively monitor patient health and helps the user monitor themselves.

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39

Farias, Frederico A. C., Vincenzo F. Falavigna, Maria Eduarda L. Viapiana y Asdrubal Falavigna. "Remote patient monitoring in spine surgery". Journal of Musculoskeletal Surgery and Research 6 (21 de mayo de 2022): 160–66. http://dx.doi.org/10.25259/jmsr_37_2022.

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Objectives: Remote monitoring or telemonitoring is an innovative strategy to improve patient care. This study aimed to present the authors’ experience of a remote monitoring platform for post-operative care of patients who have undergone spine surgery. Methods: A telemonitoring system for patients undergoing spine surgery, centered on a smartphone application named Wippe Track, (Brazilian Telemedicine Company BR HomMed, São Paulo, Brazil) compatible with both Android and iOS smartphones. All patients had two Bluetooth devices connected to the smartphone application: Digital scale and wristband activity tracker. The outcomes evaluated were based on patient-related outcome measures (PROMs) using specific disease-oriented questionnaires. Results: A preliminary study to refine the system based on patient/provider input was performed with 30 patients. The adherence and interaction with the platform were 94%. Patients have provided all the PROMs data inputs and have consistently interacted with monitoring center professionals and the surgeon through the platform. Weight loss was observed in 76% of overweight patients using remote nutritionist support and specific exercises. Conclusion: The patients were satisfied and felt secure. The remote monitoring could detect and help manage events and warnings related to post-operative issues and reduce the need to travel for non-scheduled care.

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40

S.I., Parihar. "Wireless Patient Health Monitoring System". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 05 (10 de mayo de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem33659.

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Wireless Patient Health Monitoring Systems (WPHMS) have emerged as transformative solutions in modern healthcare, facilitating continuous and remote monitoring of patients' vital signs in real-time. This paper presents a comprehensive exploration of a Wireless Patient Health Monitoring System leveraging the Blynk app, a versatile IoT platform. The system is designed to enable seamless data collection, transmission, and visualization of critical health parameters such as pulse rate, temperature, and blood pressure. By integrating cutting-edge hardware components with intuitive software interfaces, the proposed system offers unprecedented flexibility and accessibility in patient monitoring, transcending the limitations of traditional wired monitoring systems. The research begins with a thorough review of existing literature on wireless healthcare technologies, highlighting the evolution of patient monitoring systems and the role of IoT platforms in revolutionizing healthcare delivery. Building upon this foundation, the paper proceeds to outline the hardware architecture of the WPHMS, detailing the selection and integration of microcontroller boards, sensor modules.

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41

Daly, Robert Michael, Jennifer R. Cracchiolo, Jessie C. Holland, AnnMarie Mazzella Ebstein, Katherine Panageas y Jessica Flynn. "Remote monitoring after hospital discharge." JCO Oncology Practice 19, n.º 11_suppl (noviembre de 2023): 561. http://dx.doi.org/10.1200/op.2023.19.11_suppl.561.

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561 Background: Strategies to improve transitions from the hospital to home for patients with cancer are considered an important component of quality, patient-centered care in oncology. CMS evaluates cancer hospital performance based on the 30-day unplanned hospital readmission rate, and this measure has been endorsed by the National Quality Forum. Nationally, the 30-day readmission rate for oncology patients ranges from 19%-27%. These readmissions come at high psychosocial, physical, and financial costs for patients and caregivers. A remote monitoring intervention that includes frequent contacts with the patient is likely to be effective in improving this transition. Methods: We evaluated the feasibility, acceptability, and perceived value of a mobile health intervention to monitor and manage symptoms of adult medical oncology patients discharged from an NCI-designated cancer center to home. Patients were monitored for 10 days, which is the median time to readmission for an oncology patient. The technology supporting the program included: 1) a patient portal enabling daily electronic patient-reported outcomes assessments; 2) alerts for concerning symptoms; 3) an application to allow staff to review and trend symptom data; 4) a secure platform to support communications and televisits between staff and patients; 5) an advanced feedback report to provide just-in-time patient symptom education. Feasibility and acceptability were evaluated through engagement (goal: >50% response rate) and symptom alerts and perceived value was measured through a patient engagement survey that included a net promoter score (how likely the patient is to recommend the program to similar patients; goal >0.70). Results: Between January 1, 2021 to December 31, 2022, the program enrolled 2,258 medical oncology patients (median age: 63 years, 55% female). Patients with gastrointestinal (25%) and thoracic (19%) malignancies were the most prevalent diagnoses. Of those enrolled, 66% of patients participated in home remote monitoring by self-reporting symptom data and 49% of patients generated a severe symptom alert. The most prevalent severe symptom alerts were pain (24%), constipation (7%), and fatigue (7%). Patients expressed a great deal of satisfaction with the intervention with a net promoter score of 0.86. In preliminary analysis, we found that 30-day readmission decreased by 3% (from 33% to 30%) representing savings of $2.3 - $5.8 MM annually. Conclusions: A remote monitoring program after discharge was feasible, acceptable, and perceived to be of value by oncology patients discharged from a cancer center. Future work will evaluate the value of a remote symptom monitoring platform in decreasing readmissions.

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42

Padmasree, M. "IOT based Patient Monitoring System". International Journal for Research in Applied Science and Engineering Technology 9, n.º VI (10 de junio de 2021): 285–92. http://dx.doi.org/10.22214/ijraset.2021.34966.

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Health monitoring is the fundamental issue in this day and age. Because of absence of amazing health monitoring, patient go through from genuine medical problems. There are great deals of IoT gadgets now days to uncover the health of impacted individual over web. Wellbeing specialists are moreover exploiting these splendid contraptions to look out for their patients. With heaps of new medical care innovation new companies, IoT is suddenly changing the medical services industries. Actually in this undertaking, we will make an IoT based patient checking System which reports the patient coronary heart beat rate and assemble temperature and besides transport an email/SMS alert each time these readings go past basic characteristics. Heartbeat charge and inward warmth level readings are recorded over ThingSpeak and Google sheets so that affected individual health can be seen from wherever in the world over through on the web. A furor will in like manner be related so that affected individual can crush it on emergency to send email/SMS to their relatives.

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43

Sparks, Ross Stewart y Chris Okugami. "Tele-Health Monitoring of Patient Wellness". Journal of Intelligent Systems 25, n.º 4 (1 de octubre de 2016): 515–28. http://dx.doi.org/10.1515/jisys-2014-0175.

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AbstractThe vital signs of chronically ill patients are monitored daily. The record flags when a specific vital sign is stable or when it trends into dangerous territory. Patients also self-assess their current state of well-being, i.e. whether they are feeling worse than usual, neither unwell nor very well compared to usual, or are feeling better than usual. This paper examines whether past vital sign data can be used to forecast how well a patient is going to feel the next day. Reliable forecasting of a chronically sick patient’s likely state of health would be useful in regulating the care provided by a community nurse, scheduling care when the patient needs it most. The hypothesis is that the vital signs indicate a trend before a person feels unwell and, therefore, are lead indicators of a patient going to feel unwell. Time series and classification or regression tree methods are used to simplify the process of observing multiple measurements such as body temperature, heart rate, etc., by selecting the vital sign measures, which best forecast well-being. We use machine learning techniques to automatically find the best combination of these vital sign measurements and their rules that forecast the wellness of individual patients. The machine learning models provide rules that can be used to monitor the future wellness of a patient and regulate their care plans.

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44

Shende, Prof Deepali K., Mr Sidheshwar Madrewar y Mr Shivam Bhongade Mr Shivam Dugade. "Dementia Patient Activity Monitoring and Fall Detection using IoT for Elderly". International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30 de junio de 2019): 363–67. http://dx.doi.org/10.31142/ijtsrd23656.

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45

Singh, Malkit y Anand Mittal. "Internet of Things: Challenges in Web Based Remote Patient Monitoring (RPM)". Scholars Journal of Applied Medical Sciences 4, n.º 7 (julio de 2016): 2706–9. http://dx.doi.org/10.21276/sjams.2016.4.7.84.

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46

King, J. R., J. D. Phillips, R. Judge, A. D. Armond, J. A. Corbett y N. J. Birch. "Instant lithium monitoring". Psychiatric Bulletin 15, n.º 3 (marzo de 1991): 138–39. http://dx.doi.org/10.1192/pb.15.3.138.

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Lithium therapy for affective disorders has been estimated to result in savings of hospital beds in the National Health Service of £23 million each year. In order to maintain and justify this saving we must invest to provide careful monitoring of serum lithium concentrations in patients living in the community. In recent years the advent of lithium clinics has done much to improve the compliance and safety of lithium therapy (Masterton et al, 1988), but we believe that there may be scope for further improvement both in the service to the patient and in encouragement of the patient to comply.

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Rajendran, Manivasagam. "Intensive Patient Monitoring Using LabVIEW". Review of Computer Engineering Research 6, n.º 2 (2019): 92–96. http://dx.doi.org/10.18488/journal.76.2019.62.92.96.

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Shelar, Manisha, Jaykaran Singh y Mukesh Tiwari. "Wireless Patient Health Monitoring System". International Journal of Computer Applications 62, n.º 6 (18 de enero de 2013): 1–5. http://dx.doi.org/10.5120/10081-4697.

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49

Barro, Senen, Jesus Presedo, Paulo F??lix, Daniel Castro y Jose Antonio Vila. "New Trends in Patient Monitoring". Disease Management and Health Outcomes 10, n.º 5 (2002): 291–306. http://dx.doi.org/10.2165/00115677-200210050-00003.

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50

Suraj, Ashish, Amarnath S. Kaushik y Kannika Bai. "Patient Monitoring Using Emotion Recognition". International Journal for Research in Applied Science and Engineering Technology 10, n.º 8 (31 de agosto de 2022): 1197–201. http://dx.doi.org/10.22214/ijraset.2022.46387.

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Resumen

Abstract: Due to technology improvements, recognising the patient's emotions using deep learning algorithms has gotten a lot of interest recently. Automatically detecting emotions can aid in the development of smart healthcare centers that can detect pain and tiredness in patients so that medication can be started sooner. One of the most fascinating themes is the use of advanced technology to discern emotions, as it defines the human-machine interaction. Various strategies were used to teach machines how to predict emotions. Recent research in the field of employing neural networks to recognise emotions will be used in our system. Our system focuses on recognising emotions from facial expressions and demonstrating several approaches for implementing these algorithms in the real world. Techniques for recognising emotions can be utilized as a surveillance system in healthcare canters to monitor patients.

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