Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference最新文献_第7页

Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep. 以可穿戴设备为导向，支持解释行为对睡眠的影响。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781768

Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva

{"title":"Wearable-oriented Support for Interpretation of Behavioural Effects on Sleep.","authors":"Clauirton A Siebra, Jonysberg Quintino, Andre L M Santos, Fabio Q B Da Silva","doi":"10.1109/EMBC53108.2024.10781768","DOIUrl":"10.1109/EMBC53108.2024.10781768","url":null,"abstract":"Daily behaviour directly impacts health in the short and long term. Thus, embracing and maintaining healthy behaviours work like a preventive action, avoiding or delaying the emergence of chronic diseases. The process of changing daily routines toward healthy behaviours starts by understanding the current problems. Wearable and deep learning (DL) technologies represent important resources for supporting such an understanding. This paper discusses a strategy to interpret multifeatured longitudinal wearable data to analyse possible causes of health issues. We use the sleep domain as a case example where the aim is to clarify the reasons for poor sleep quality. A dataset with wearable data of 1874 days was used to create an explainable DL model, which indicates the main day-before-night sleep behaviours that may cause poor sleep quality. We use a comparative analysis with a hormone-based framework for sleep control as the form of validation. The results show that the explanations corroborate the results of the literature. However, other datasets with more features should be explored to verify the combination of these features and their effects on the health aspect under study.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning. 基于深度学习的连续手抓动作脑电图重构。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781850

Yuting Tang, Neethu Robinson, Xi Fu, Kavitha P Thomas, Aung Aung Phyo Wai, Cuntai Guan

{"title":"Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.","authors":"Yuting Tang, Neethu Robinson, Xi Fu, Kavitha P Thomas, Aung Aung Phyo Wai, Cuntai Guan","doi":"10.1109/EMBC53108.2024.10781850","DOIUrl":"10.1109/EMBC53108.2024.10781850","url":null,"abstract":"Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic exoskeletons. A new yet under-explored BCI control paradigm is Motion Trajectory Prediction (MTP). While MTP provides continuous control signals suitable for high-precision tasks, its feasibility and applications are challenged by the low signal-to-noise ratio, especially in noninvasive settings. Previous research has predominantly focused on kinematic reconstruction of upper (e.g., arm reaching) and lower limbs (e.g., gait). However, finger movements have received much less attention, despite their crucial role in daily activities. To address this gap, our study explores the potential of noninvasive Electroencephalography (EEG) for reconstructing finger movements, specifically during hand grasping actions. A new experimental paradigm to collect multichannel EEG data from 20 healthy subjects, while performing full, natural hand opening and closing movements, was designed. Employing state-of-the-art deep learning algorithms, continuous decoding models were constructed for eight key finger joints. The Convolutional Neural Network with Attention approach achieved an average decoding performance of r=0.63. Furthermore, a post-hoc metric was proposed for hand grasp cycle detection, and 83.5% of hand grasps were successfully detected from the reconstructed motion signals, which can potentially serve as a new BCI command. Explainable AI algorithm was also applied to analyze the topographical relevance of trained features. Our findings demonstrate the feasibility of using EEG to reconstruct hand joint movements and highlight the potential of MTP-BCI in control and rehabilitation applications.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fast Patch-Based Hankel Low-Rank Method for Magnetic Resonance Spectroscopy Reconstruction. 基于快速贴片的Hankel低秩磁共振谱重建方法。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782347

Hengfa Lu, Xinlin Zhang

引用次数: 0

Identifying Prediabetes in Canadian Populations Using Machine Learning. 使用机器学习识别加拿大人群中的前驱糖尿病。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782174

Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri

{"title":"Identifying Prediabetes in Canadian Populations Using Machine Learning.","authors":"Katherine Lu, Paijani Sheth, Zhi Lin Zhou, Kamyar Kazari, Aziz Guergachi, Karim Keshavjee, Mohammad Noaeen, Zahra Shakeri","doi":"10.1109/EMBC53108.2024.10782174","DOIUrl":"10.1109/EMBC53108.2024.10782174","url":null,"abstract":"Prediabetes is a critical health condition characterized by elevated blood glucose levels that fall below the threshold for Type 2 diabetes (T2D) diagnosis. Accurate identification of prediabetes is essential to forestall the progression to T2D among at-risk individuals. This study aims to pinpoint the most effective machine learning (ML) model for prediabetes prediction and to elucidate the key biological variables critical for distinguishing individuals with prediabetes. Utilizing data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), our analysis included 6,414 participants identified as either nondiabetic or prediabetic. A rigorous selection process led to the identification of ten variables for the study, informed by literature review, data completeness, and the evaluation of collinearity. Our comparative analysis of seven ML models revealed that the Deep Neural Network (DNN), enhanced with early stop regularization, outshined others by achieving a recall rate of 60%. This model's performance underscores its potential in effectively identifying prediabetic individuals, showcasing the strategic integration of ML in healthcare. While the model reflects a significant advancement in prediabetes prediction, it also opens avenues for further research to refine prediction accuracy, possibly by integrating novel biological markers or exploring alternative modeling techniques. The results of our work represent a pivotal step forward in the early detection of prediabetes, contributing significantly to preventive healthcare measures and the broader fight against the global epidemic of Type 2 diabetes.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Magnetic Resonance Elastography for Mechanical Modeling of the Human Lumbar Intervertebral Disc. 磁共振弹性成像用于人体腰椎间盘的力学建模。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782890

Emily Triolo, Waiman Meinhold, Efe Ozkaya, Jun Ueda, Mehmet Kurt

{"title":"Magnetic Resonance Elastography for Mechanical Modeling of the Human Lumbar Intervertebral Disc.","authors":"Emily Triolo, Waiman Meinhold, Efe Ozkaya, Jun Ueda, Mehmet Kurt","doi":"10.1109/EMBC53108.2024.10782890","DOIUrl":"10.1109/EMBC53108.2024.10782890","url":null,"abstract":"Magnetic Resonance Elastography (MRE) is a phase-contrast imaging technique that allows for determination of mechanical properties of tissue in-vivo. Due to physiological and morphological changes leading to changes in tissue mechanical properties, MRE may be a promising imaging tool for detection of intervertebral disc degeneration. We therefore performed a preliminary study to determine the frequency dependent mechanical properties of the lumbar intervertebral discs. Six healthy volunteers underwent multifrequency MRE (50, 80, and 100 Hz) to measure the mechanical properties of the intervertebral discs between the L3 and L4, and L4 and L5 vertebrae. Frequency-independent disc mechanical properties and best-fit mechanical model were determined from the frequency-dependent disc data by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). A seventh individual with a history of a discectomy on the disc between the L4 and L5 vertebrae was also scanned to provide a preliminary analysis about how degeneration impacts disc mechanical properties. Our findings show that the Zener model may best represent the disc's frequency-dependent mechanical response. Additionally, we observed a significantly lower complex shear modulus in the degenerated disc than the healthy discs at each frequency, demonstrating the potential for MRE to detect early signs of degeneration and pinpoint the cause of chronic back pain.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robotic Assistance for Precise Spinal Injections: Development and Clinical Verification. 精确脊柱注射的机器人辅助：发展和临床验证。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781757

Aswathaman G, Keerthivasan S, Shyam A, Manojkumar Lakshmanan, Mohanashankar Sivaprakasam

{"title":"Robotic Assistance for Precise Spinal Injections: Development and Clinical Verification.","authors":"Aswathaman G, Keerthivasan S, Shyam A, Manojkumar Lakshmanan, Mohanashankar Sivaprakasam","doi":"10.1109/EMBC53108.2024.10781757","DOIUrl":"10.1109/EMBC53108.2024.10781757","url":null,"abstract":"Robot-assisted surgical systems have shown promising results and better patient outcomes in pedicle screw instrumentation and percutaneous needle interventions. Many commercial robotic assistance systems are available for the aforementioned procedures. However, there is only limited literature on robotic spinal injection and needle delivery. Moreover, there is no robotic system that is commercially available for assisting surgeons in spinal injections and needle placement. To address this gap, we developed a robotic system that can provide stereotactic assistance to the surgeon for administering spinal injections and needles. The system utilizes a commercially available collaborative manipulator and a stereoscopic navigation system. A robot motion planner was developed to impart collision avoidance capabilities and make the manipulator adept for the surgical setting. A clinical phantom study was conducted to validate the overall system performance and accuracy. 60 different needle plans were targeted on the lumbar region by expert surgeons and executed through the proposed system. A mean target point error of 1.02 mm with a standard deviation of 0.5 mm was achieved. The observations and results obtained through the study show that the proposed robotic guidance system can be of potential aid in accomplishing accurate spinal needle and injection delivery.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Early Deterioration in Lower Acuity Telehealth Patients Using Gradient Boosting. 使用梯度增强预测低度远程医疗患者的早期恶化。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782253

Ricardo Ricci Lopes, Holly Chavez, Louis Atallah

{"title":"Predicting Early Deterioration in Lower Acuity Telehealth Patients Using Gradient Boosting.","authors":"Ricardo Ricci Lopes, Holly Chavez, Louis Atallah","doi":"10.1109/EMBC53108.2024.10782253","DOIUrl":"10.1109/EMBC53108.2024.10782253","url":null,"abstract":"Timely recognition of physiological abnormalities is vital for early intervention, potentially preventing adverse outcomes and minimizing the need for transfer to a higher level of care. This is a primary focus of telehealth monitoring in which remote clinicians utilize population management to identify and prioritize patients of concern or instability. This work proposes an Early Warning Score model based on gradient boosting, emphasizing prompt deterioration detection, especially tailored to patients in lower acuity units (e.g. - medical/surgical) who are also receiving telehealth monitoring. Data included 36,963 patient encounters from the eICU Research Institute database. The model utilizes 35 features extracted from demographics, vital signs, and laboratory data. It showed enhanced performance in comparison to a version of the Modified Early Warning Score (MEWS*) that considers age and oxygen saturation instead of the level of consciousness. The model achieved an AUROC of 0.79 and AUPRC of 0.28, 24 hours before deterioration, surpassing MEWS* with values of 0.67 and 0.07, respectively. Within an hour before deterioration happens, the proposed model achieved an AUROC of 0.86 and AUPRC of 0.42 while MEWS* achieved 0.74 and 0.21, respectively. Future investigations will focus on exploring the impact of missing data, continuous performance for individual patients, and integration into clinical workflows.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Effect of Physical Structural Properties on Electrochemical Properties of Ruthenium Oxide for Neural Stimulating and Recording Electrodes. 用于神经刺激和记录电极的氧化钌的物理结构特性对其电化学特性的影响。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781914

Yupeng Wu, Miguel Figueroa Hernandez, Tian Lei, Siddarth Jayakumar, Rohan R Lalapet, Alexandra Joshi-Imre, Mark E Orazem, Kevin J Otto, Stuart F Cogan

{"title":"The Effect of Physical Structural Properties on Electrochemical Properties of Ruthenium Oxide for Neural Stimulating and Recording Electrodes.","authors":"Yupeng Wu, Miguel Figueroa Hernandez, Tian Lei, Siddarth Jayakumar, Rohan R Lalapet, Alexandra Joshi-Imre, Mark E Orazem, Kevin J Otto, Stuart F Cogan","doi":"10.1109/EMBC53108.2024.10781914","DOIUrl":"10.1109/EMBC53108.2024.10781914","url":null,"abstract":"Recently, there has been a growing interest in ruthenium oxide (RuOx) as an alternative mixed-conductor oxide to SIROF as an electrode coating. RuOx is recognized as a faradic charge-injection coating with high CSCc, long-term pulsing stability, and low impedance. We examined how the structural properties of sputter-deposited RuOx influence its electrochemical performance as an electrode coating for neural stimulation and recording. Thin film RuOx was deposited under various pressures: 5 mTorr, 15 mTorr, 30 mTorr, and 60 mTorr on wafer-based planar test structures. Electrochemical characterizations, including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and voltage transient (VT), were employed. The structure of RuOx films was characterized by scanning electron microscope (SEM). Our findings revealed that the sputtering pressure significantly influences the growth of the RuOx film, subsequently affecting its electrochemical performance. The results indicate that the electrochemical performance of RuOx can be optimized by adjusting the deposition conditions to achieve a favorable balance between electronic and ionic conductivity.Clinical Relevance- This research underscores the potential for optimizing the structural properties of RuOx to enhance its electrochemical capabilities for neural stimulation and recording.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Muscle activation of lower limb during walking in elderly individuals with sarcopenia: A pilot study. 老年肌肉减少症患者行走时下肢肌肉的激活：一项初步研究。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10782274

Yongjin Li, Binbin Wang, Xin Jiao, Mirabel Ewura Esi Acquah, Yunxia Zhu, Jun Jin, Xiaoyan Zhang, Dongyun Gu

{"title":"Muscle activation of lower limb during walking in elderly individuals with sarcopenia: A pilot study.","authors":"Yongjin Li, Binbin Wang, Xin Jiao, Mirabel Ewura Esi Acquah, Yunxia Zhu, Jun Jin, Xiaoyan Zhang, Dongyun Gu","doi":"10.1109/EMBC53108.2024.10782274","DOIUrl":"10.1109/EMBC53108.2024.10782274","url":null,"abstract":"Sarcopenia is a muscle disease that can lead to a decrease in muscle mass and strength. Patients with sarcopenia usually have gait disorders and a higher risk of falls. At present, muscle activation of lower limb during walking in patients with sarcopenia is not clear, making it difficult to find effective rehabilitation training. In this study, we aim to investigate muscle activation of lower limb during walking in elderly individuals with sarcopenia. We collected surface EMG signals from the tibialis anterior (TA), lateral gastrocnemius (GL), rectus femoris (RF), and biceps femoris (BF) muscles during walking. Results showed that differences of muscle activation between sarcopenia patients and healthy elderly during walking are mainly reflected in the shank. Specially, RMS of TA was statistically significantly higher in sarcopenia patients during swing phase (p=0.005). Modulation index of GL was significantly higher in sarcopenia patients during pre-swing phase (p=0.01). Coactivation index of TA-GL was significantly lower in sarcopenia patients during single stance phase. A significant strong correlation was also observed between RMS of GL and step length (r=0.863, p=0.012) in sarcopenia patients. These results indicated that differences of muscle activation in shank may contribute to gait disorders in sarcopenia patients. It is recommended that exercise intervention strategies for sarcopenia patients should focus on shank muscles.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Medication Recommendation with Hierarchical Network and Patient Visit Histories. 基于分层网络和患者就诊记录的药物推荐。

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Pub Date : 2024-07-01 DOI: 10.1109/EMBC53108.2024.10781496

Sawrawit Chairat, Apichat Sae-Ang, Kerdkiat Suvirat, Thammasin Ingviya, Sitthichok Chaichulee

{"title":"Enhancing Medication Recommendation with Hierarchical Network and Patient Visit Histories.","authors":"Sawrawit Chairat, Apichat Sae-Ang, Kerdkiat Suvirat, Thammasin Ingviya, Sitthichok Chaichulee","doi":"10.1109/EMBC53108.2024.10781496","DOIUrl":"10.1109/EMBC53108.2024.10781496","url":null,"abstract":"Prescribing medications is an essential part of patient care and requires precision and personalization in selection. Our study introduces a hierarchical medication recommendation system that aims to improve the prescribing process. We use FastText to embed medical contexts and employ a hierarchical attention-based model to manage the hierarchical structure of medication codes. The system takes input data from the current visit and the three previous visits to make recommendations. We trained and evaluated our model on 99,417 anonymized primary care outpatient visits. Our model achieved a mean average precision (mean AP) of 0.8724, 0.7419, 0.6805, and 0.6184 at the first, second, third, and fourth levels of the ATC system, respectively. We demonstrate that incorporating patient visit histories can improve predictions. Our results provide a solution to improve medication prescribing and suggest possible extensions for more comprehensive recommendations.","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":"2024 ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143559368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0