| 1. |
- Akay, Altug, et al.
(författare)
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A Novel Data-Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin
- 2015
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Ingår i: IEEE journal of biomedical and health informatics. - 2168-2194 .- 2168-2208. ; 19:1, s. 389-396
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Tidskriftsartikel (refereegranskat)abstract
- A novel data mining method was developed to gauge the experience of the drug Sitagliptin (trade name Januvia) by patients with diabetes mellitus type 2. To this goal, we devised a two-step analysis framework. Initial exploratory analysis using self-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user's clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis that can enable improved outcomes and solutions for public health and important feedback for the manufacturer.
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| 2. |
- Akay, Altug, et al.
(författare)
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Assessing Antidepressants Using Intelligent Data Monitoring and Mining of Online Fora
- 2016
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 20:4, s. 977-986
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Tidskriftsartikel (refereegranskat)abstract
- Depression is a global health concern. Social networks allow the affected population to share their experiences. These experiences, when mined, extracted, and analyzed, can be converted into either warnings to recall drugs (dangerous side effects), or service improvement (interventions, treatment options) based on observations derived from user behavior in depression-related social networks. Our aim was to develop a weighted network model to represent user activity on social health networks. This enabled us to accurately represent user interactions by relying on the data's semantic content. Our three-step method uses the weighted network model to represent user's activity, and network clustering and module analysis to characterize user interactions and extract further knowledge from user's posts. The network's topological properties reflect user activity such as posts' general topic as well as timing, while weighted edges reflect the posts semantic content and similarities among posts. The result, a synthesis from word data frequency, statistical analysis of module content, and the modeled health network's properties, has allowed us to gain insight into consumer sentiment of antidepressants. This approach will allow all parties to participate in improving future health solutions of patients suffering from depression.
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| 3. |
- Akay, Altug, et al.
(författare)
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Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care
- 2015
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Ingår i: IEEE journal of biomedical and health informatics. - 2168-2194 .- 2168-2208. ; 19:1, s. 210-218
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Tidskriftsartikel (refereegranskat)abstract
- Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.
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| 4. |
- Banaee, Hadi, 1986-, et al.
(författare)
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Data-driven rule mining and representation of temporal patterns in physiological sensor data
- 2015
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Ingår i: IEEE journal of biomedical and health informatics. - 2168-2194 .- 2168-2208. ; 19:5, s. 1557-1566
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Tidskriftsartikel (refereegranskat)abstract
- Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, ...) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.
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| 5. |
- Barua, Shaibal, et al.
(författare)
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Automated EEG Artifact Handling with Application in Driver Monitoring
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:5, s. 1350-1361
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Tidskriftsartikel (refereegranskat)abstract
- Automated analyses of electroencephalographic (EEG) signals acquired in naturalistic environments is becoming increasingly important in areas such as brain computer interfaces and behaviour science. However, the recorded EEG in such environments is often heavily contaminated by motion artifacts and eye movements. This poses new requirements on artifact handling. The objective of this paper is to present an automated EEG artifacts handling algorithm which will be used as a pre-processing step in a driver monitoring application. The algorithm, named ARTE (Automated aRTifacts handling in EEG), is based on wavelets, independent component analysis and hierarchical clustering. The algorithm is tested on a dataset obtained from a driver sleepiness study including 30 drivers and 540 30-minute 30-channel EEG recordings. The algorithm is evaluated by a clinical neurophysiologist, by quantitative criteria (signal quality index, mean square error, relative error and mean absolute error), and by demonstrating its usefulness as a pre-processing step in driver monitoring, here exemplified with driver sleepiness classification. All results are compared with a state of the art algorithm called FORCe. The quantitative and expert evaluation results show that the two algorithms are comparable and that both algorithms significantly reduce the impact of artifacts in recorded EEG signals. When artifact handling is used as a pre-processing step in driver sleepiness classification, the classification accuracy increased by 5% when using ARTE and by 2% when using FORCe. The advantage with ARTE is that it is data driven and does not rely on additional reference signals or manually defined thresholds, making it well suited for use in dynamic settings where unforeseen and rare artifacts are commonly encountered.
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| 6. |
- Ferreira, Javier, 1982-, et al.
(författare)
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A handheld and textile-enabled bioimpedance system for ubiquitous body composition analysis. : An initial functional validation
- 2016
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 21:5
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, many efforts have been made to promote a healthcare paradigm shift from the traditional reactive hospital-centered healthcare approach towards a proactive, patient-oriented and self-managed approach that could improve service quality and help reduce costs while contributing to sustainability. Managing and caring for patients with chronic diseases accounts over 75% of healthcare costs in developed countries. One of the most resource demanding diseases is chronic kidney disease (CKD), which often leads to a gradual and irreparable loss of renal function, with up to 12% of the population showing signs of different stages of this disease. Peritoneal dialysis and home haemodialysis are life-saving home-based renal replacement treatments that, compared to conventional in-center hemodialysis, provide similar long-term patient survival, less restrictions of life-style, such as a more flexible diet, and better flexibility in terms of treatment options and locations. Bioimpedance has been largely used clinically for decades in nutrition for assessing body fluid distributions. Moreover, bioimpedance methods are used to assess the overhydratation state of CKD patients, allowing clinicians to estimate the amount of fluid that should be removed by ultrafiltration. In this work, the initial validation of a handheld bioimpedance system for the assessment of body fluid status that could be used to assist the patient in home-based CKD treatments is presented. The body fluid monitoring system comprises a custom-made handheld tetrapolar bioimpedance spectrometer and a textile-based electrode garment for total body fluid assessment. The system performance was evaluated against the same measurements acquired using a commercial bioimpedance spectrometer for medical use on several voluntary subjects. The analysis of the measurement results and the comparison of the fluid estimations indicated that both devices are equivalent from a measurement performance perspective, allowing for its use on ubiquitous e-healthcare dialysis solutions.
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| 7. |
- Hafid, Abdelakram, et al.
(författare)
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Full Impedance Cardiography Measurement Device Using Raspberry PI3 and System-on-Chip Biomedical Instrumentation Solutions
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2168-2194 .- 2168-2208. ; 22:6, s. 1883-1894
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-I t-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPl.com, for open access under a Non Commercial-Share A like 4.0 International License.
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| 8. |
- Hafid, Abdelakram, et al.
(författare)
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Full impedance cardiography measurement device using raspberry PI3 and system-on-chip biomedical instrumentation solutions
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 22:6, s. 1883-1894
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-It-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPI.com, for open access under a Non Commercial-Share A like 4.0 International License.
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| 9. |
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| 10. |
- Memedi, Mevludin, et al.
(författare)
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Validity and responsiveness of at-home touch-screen assessments in advanced Parkinson's disease
- 2015
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 19:6, s. 1829-1834
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression in patients with fluctuations. Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales. In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-non-naïve patients, there were no significant changes in mean OTS until month 36. The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59). Responsiveness measured as effect size was 0.696 and 0.536 for OTS and UPDRS respectively. The trends of the test scores were similar to the trends of clinical rating scores but dropout rate was high. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of well-established scales. The responsiveness and reproducibility were better for OTS than for total UPDRS.
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