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Knowledge Mining from Medical Images
Published in Wahiba Ben Abdessalem Karaa, Nilanjan Dey, Mining Multimedia Documents, 2017
Amira S. Ashour, Nilanjan Dey, Suresh Chandra Satapathy
Navigation through information-rich databases becomes an innovative knowledge discovery challenge that requires intelligent agents. Health informatics is a quickly developing domain that is focused on relating information technology and computer science for health/medical data extraction and interpretation. It is the automation of health information in order to support the clinical care, training, administration of health services, and medical research to enhance health information processing by collecting, storing, effectively retrieving, and analyzing medical data for clinicians, administrators, and researchers [9–11]. Nevertheless, there is a deficiency in the efficient analysis methods for discovering the hidden knowledge from the gigantic healthcare databases.
Clinical Notes Mining for Post Discharge Mortality Prediction
Published in IETE Technical Review, 2022
Vineet Kumar, Rohit Bajpai, Ram Babu Roy
Wider adoption of Electronic Health Records (EHRs) in the hospital setting has given rise to a plethora of clinical data. EHR records patient demographic details, past medical history, periodic clinical measurements like patient vitals, test reports, medical interventions, and detailed clinical/nursing notes. Structured clinical (tabular) data contain a rich but incomplete picture of the patient. Clinical notes are recorded by attending nurses in free form textual format. Murdoch et al. [1] found out that EHR contains almost 80% of unstructured data. They contain rich information relevant to the patient’s response to treatment and illness trajectory as well. In order to identify high-risk patients, health systems must leverage text analytics to derive insights from free form clinical texts. Natural Language Processing (NLP) helps in interpretation of textual data. It can aid in information extraction, conversion of unstructured to structured data, document categorization, etc. However, due to their high free form nature utilizing these unstructured clinical descriptions (UCDs) in building clinical decision support systems is not much explored. Predicting post-discharge mortality is one of the major research areas in health informatics [2].
Rehabilitation robotics after stroke: a bibliometric literature review
Published in Expert Review of Medical Devices, 2022
Giacomo Zuccon, Basilio Lenzo, Matteo Bottin, Giulio Rosati
A similar analysis can also be conducted for conference publications (Figure 2(b)). The top five conferences in order of publication number are International Conference on Rehabilitation Robotics (ICORR) with 442 papers, followed by Annual International Conference of the IEEE Engineering in Medicine and Biology with 229 papers, International Conference on Biomedical Robotics and Biomechatronics with 136 papers, International Conference on Intelligent Robots and Systems with 47 papers, and International Conference on Robotics and Automation with 46 papers (the acronyms used for the conferences are given in Table 2). Clearly, ICORR dominates the research output on rehabilitation robotics published in conferences, giving around a third of all conference papers on the field of rehab robotics. Over 60% of the conference publications are sponsored by IEEE (1329 papers), which indicates the emphasis of these applications of rehabilitation robotics concerning engineering, technology, and computing. The content of these five conferences is split over control, systems and electronic engineering, biomedical engineering, computer science, and medicine (health informatics and rehabilitation).
Cognitive Effects of Visualization Techniques for Inconsistency Metrics on Monitoring Data-Intensive Processes
Published in Information Systems Management, 2021
Sabine Nagel, Carl Corea, Patrick Delfmann
A total of 48 undergraduate and graduate students from the school of Computer Science at the University of Koblenz-Landau participated in our experiment. Please observe that this school does not only include traditional computer science but also includes institutes focusing on business informatics or business administration. Course programs ranged from subjects such as traditional computer science, business informatics, information management and data science. Thus, the participants represented diverse fields. In total, 8 female and 40 male students participated in the experiment. Although the introductory slides covered all relevant concepts, all participants had a general knowledge of business process management based on their study programs, i.e., all students had previous courses on the basics of business process modeling. Previous knowledge on the DMN standard and decision modeling was not assumed and thus, covered in the introductory slides. The assignment of the 48 students into the two groups was performed at random. Furthermore, no incentive was offered, so participation was voluntary.