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ระบบใบสั่งยาอัจฉริยะสำหรับผู้ป่วยสูงอายุที่ป่วยด้วยโรคเบาหวาน ความดันโลหิตสูงและโรคหัวใจ ณ โรงพยาบาลสงขลานครินทร์
No full textMaster of Science (Data Science), 2023Over time, large amounts of clinical data have accumulated in electronic health records (EHRs), making it difficult for healthcare professionals to navigate and make patient-centered decisions. This underscores the need for healthcare recommendation systems that help medical professionals make faster and more accurate decisions. This study addresses drug recommendation systems that generate an appropriate list of drugs that match patients’ diagnoses. Currently, recommendations are manually prepared by physicians, but this is difficult for patients with multiple comorbidities. We explored approaches to drug recommendations based on elderly patients with diabetes, hypertension, and cardiovascular disease who visited primary care clinics and often had multiple conditions. We examined both collaborative filtering approaches and traditional machine learning classifiers. The hybrid model between the two yielded a recall at 5 of 76.61%, a precision at 5 of 46.20%, a macro-averaged area under the curve of 74.52%, and an average physician agreement of 47.50%. Although collaborative filtering is widely used in recommendation systems, our results showed that it consistently underperformed traditional classification. Collaborative filtering was
sensitive to class imbalances and favored the more popular classes. This study has highlighted challenges that need to be addressed when developing recommendation systems in EHRs.เมื่อเวลาผ่านไป ข้อมูลการรักษาผู้ป่วยที่มาพบแพทย์ที่คลินิกจำนวนมาก ได้ถูกสะสมอยู่ในบันทึกสุขภาพอิเล็กทรอนิกส์ (EHRs) เมื่อข้อมูลสะสมมากขึ้น ทำให้ยากสำหรับบุคลากรทางการแพทย์ในการนำข้อมูลมาใช้และทำการตัดสินใจที่เน้นใช้ข้อมูลผู้ป่วยเป็นศูนย์กลาง สิ่งนี้เน้นย้ำถึงความจำเป็นของระบบคำแนะนำด้านการดูแลสุขภาพที่ช่วยให้แพทย์สามารถตัดสินใจได้รวดเร็วและแม่นยำยิ่งขึ้น การศึกษานี้กล่าวถึงระบบการแนะนำยาที่สร้างรายการยาที่เหมาะสม ซึ่งตรงกับการวินิจฉัยของผู้ป่วยในปัจจุบัน คำแนะนำต่างๆ จัดทำขึ้นโดยแพทย์เอง แต่นี่เป็นเรื่องยากสำหรับผู้ป่วยที่มีโรคร่วมหลายโรค เราสำรวจแนวทางคำแนะนำการใช้ยาโดยพิจารณาจากผู้ป่วยสูงอายุที่เป็นโรคเบาหวาน โรคความดันโลหิตสูง และโรคหัวใจ ที่เข้ารับบริการที่คลินิกปฐมภูมิและมักมีภาวะร่วมหลายอย่าง เราได้ตรวจสอบทั้งวิธีการกรองร่วมกัน (collaborative filtering) และวิธีการแยกกลุ่มประเภท (machine learning classifiers) รวมถึงการใช้ไฮบริดโมเดลระหว่างทั้งสองประเภท แม้ว่าการกรองร่วมกัน (collaborative filtering) จะใช้กันอย่างแพร่หลายในระบบคำแนะนำ (recommendation systems) แต่ผลลัพธ์ของเราแสดงให้เห็นว่าการกรองประเภทนี้มีประสิทธิภาพต่ำกว่าการจัดประเภทแบบแยกกลุ่ม (machine learning classifiers) การกรองร่วมกันนั้นมีผลต่อความไม่สมดุลของคลาสและสนับสนุนคลาสที่ได้รับความนิยมมากกว่า การศึกษานี้ได้เน้นถึงความท้าทายที่ต้องแก้ไขเมื่อพัฒนาระบบคำแนะนำในบันทึกสุขภาพอิเล็กทรอนิกส์ (EHRs
Non-contact vital sign monitoring of pre-term infants
Get PDFNon-contact vital sign monitoring using video cameras enables the measurement of heart rate and respiratory rate to be performed without sensors attached to the skin of the patient. This provides advantages in terms of patient comfort and the management of skin irritation and infection. However, the use of non-contact technologies in the clinic presents several challenges such as variations in human skin colour, changes in lighting conditions, the detection of the presence of a patient in the video frame and the selection of suitable regions of interests (ROIs) from which vital signs can be estimated.
This thesis proposes a framework for the accurate and continuous measurement of heart rate and respiratory rate in pre-term infants in a real-world hospital environment. The framework has been developed and validated on the data obtained from a clinical study of pre-term infants hospitalised in the high-dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford. The study involved the recording of videos and reference vital signs for 90 sessions during daytime from 30 pre-term infants, comprising a total recording time of 426.6 hours. The dataset provided a wide range of vital sign values for developing and validating the framework.
Multi-task deep learning algorithms were developed so that vital-sign estimation could be performed only when the infant was present in front of the video camera and no clinical interventions were undertaken. The algorithms are able to deal with different skin tones, lighting condition changes and variable body postures.
Heart rate is estimated from the photoplethysmographic imaging (PPGi) signal derived from subtle changes in the colour of the skin areas. Respiratory rate is estimated using data fusion from the PPGi signals along with the shape and morphological properties of the skin areas. Signal quality assessment algorithms are developed for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. In a test set, the mean absolute error between the reference and camera-derived heart rates is 2.3 beats/min for over 76% of the time for which the reference and camera data are valid. The mean absolute error between the reference and camera-derived respiratory rate is 3.6 breaths/min for over 78% of the time. Most of the time periods during which the video camera cannot provide estimates of heart rate and respiratory rate are lower than 30 seconds.
This thesis further investigated the feasibility of using non-contact algorithms to estimate heart rate and respiratory rate during a painful clinical procedure, a heel prick for withdrawing blood for blood gas analysis. Adaptation of the algorithms enables physiological indicators of pain to be derived from the analysis of the video camera data.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients
No full textCritical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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