Assessment of medication self-administration using artificial intelligence

Zhao, M.; Hoti, Kreshnik; Wang, Hao; Raghu, Aniruddh; Katabi, Dina

doi:10.1038/s41591-021-01273-1

Cited by 41 publications

(22 citation statements)

References 42 publications

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“…The same radio device was used to detect errors in medication self-administration ( 71 ) and monitor patients with Alzheimer’s disease ( 72 ), endometriosis ( 73 ), COVID-19 ( 74 ), and facioscapulohumeral muscular dystrophy (FSHD) ( 75 ). Also, we have explored the use of the device with individuals with PD in a small pilot study focused on sleep and daily activities ( 76 ).…”

Section: Methodsmentioning

confidence: 99%

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

et al. 2022

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Parkinson’s disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people’s bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients’ daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

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Section: Methodsmentioning

confidence: 99%

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

et al. 2022

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“…Inpatient medication predictions may have clinical decision support applications, despite the fact that they are outcome-agnostic. Recent studies have also used AI to ensure medication safety, particularly device-based administration, with minimal potential overhead for patients and healthcare professionals [26]. With so much diversity in modern medical research, we believe our system could have significant clinical implications.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-enabled rapid and symptom-based medication recommendation system (COV-MED) for the COVID-19 patients

et al. 2022

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In a general COVID-19 population in Cox’s Bazar, Bangladesh, we developed a medication recommendation system based on clinical information from the electronic medical record (EMR). Our goal was also to enable deep learning (DL) strategies to quickly assist physicians and COVID-19 patients by recommending necessary medications. The general demographic data, clinical symptoms, basic clinical tests, and drug information of 8953 patients were used to create a dataset. The learning model in this COVID-MED model was created using Keras (an open-source artificial neural network library) to solve regression problems. In this study, a sequential model was adopted. In order to improve the prediction capability and achieve global minima quickly and smoothly, the COVID-MED model incorporates an adaptive optimizer dubbed Adam. The model calculated a mean absolute error of 0.0037, a mean squared error of 0.000035, and a root mean squared error of 0.0059. The model predicts the output medications, such as injections or other oral medications, with around 99% accuracy. These findings show that medication can be predicted using information from the EMR. Similar models allow for patient-specific decision support to help prevent medication errors in diseases other than COVID-19.

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“…Given that a personalized approach to the selection of an appropriate device for any given patient enhances persistence with device adherence (11), studies like this one, that probe into patients' experience and expectations, will ultimately help to increase treatment compliance. In the future, artificial intelligence-based solutions for contactless at-home assessment of patient's inhaler handling will help to monitor and improve treatment adherence through reminders and instant feedback on use, with healthcare professionals having access to such records for an objective measure of treatment adherence (30).…”

Section: Discussionmentioning

confidence: 99%

Exploring quality of life and satisfaction with treatment in asthmatic patients receiving dry powder inhalers: a multinational survey

et al. 2021

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Introduction: The quality of life (QoL) and device needs have not been characterized in asthmatic patients treated via dry powder inhalers (DPIs). The aim of this study was to assess the impact of asthma on health-related QoL, device satisfaction, and preference in adult asthmatic patients using DPI devices, and to identify any DPI-associated unmet needs. Methods: An online survey was conducted between November and December 2019 on eligible patients from the Cint consumer panel across Europe. Newly designed, as well as validated questionnaires were used to collect data on QoL and inhaler satisfaction. Results: A total of 1063 asthmatic patient took part in the survey; 66% of the patients reported medium or high impact of asthma on the overall QoL. The majority of patients (61%) reported high level of satisfaction with their current device. The patients with medium-to-high impact of asthma on QoL were significantly less likely to be satisfied with their current device (55%) than those who reported low-to-medium impact of asthma on QoL (67%; p-value < 0.001). "Higher number of available doses, " "usability, " "clear dose counter, " and "feedback on correct inhalation" were the attributes mostly requested from a new device. The demand for user-friendly devices that provide feedback on correct drug administration was identified as an unmet need. Conclusions and clinical relevance:In asthmatic patients with medium to high impact of asthma on the overall QoL, the satisfaction with the device is highly affected.

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Assessment of medication self-administration using artificial intelligence

Cited by 41 publications

References 42 publications

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

Monitoring gait at home with radio waves in Parkinson’s disease: A marker of severity, progression, and medication response

Artificial intelligence-enabled rapid and symptom-based medication recommendation system (COV-MED) for the COVID-19 patients

Exploring quality of life and satisfaction with treatment in asthmatic patients receiving dry powder inhalers: a multinational survey

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