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Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less...
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... Table 6, classification parameters (precision, recall, and f-measure) are presented for each individual. For the context aware validation, we have generated 2000 behavior anomalies for each elderly person and experimented with CAAWD for the proposed behavior analysis (BA) module validation. ...Similar publications
Internet of Things (IoT) devices are operating in various domains like healthcare environment, smart cities, smart homes, transportation, and smart grid system. These devices transmit a bulk of data through various sensors, actuators, transceivers, or other wearable devices. Data in the IoT environment is susceptible to many threats, attacks, and r...
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... Knowledge discovery techniques have proved their impact on the medical and healthcare field [16]. In healthcare and prevention, predictive data mining is a relatively novel area of research, which blends sophisticated computing and representational techniques with expert's insight to develop tools for improving the healthcare domain especially in early prediction of the disease. ...
Since the beginning of the AIDS epidemic, the HIV virus has infected millions of people, and the count is on the rise with every passing day. This epidemic has affected not only the children and adults but also infants born to HIV positive mothers. Unfortunately, there is no cure for HIV/AIDS; however, early and accurate prediction methods are required for early treatment and would be helpful to decrease the spread of the disease. Previous studies have investigated the early prediction of HIV infection, mainly utilizing HIV positive patients' medical records. However, besides medical records, other characteristics, such as drug injection, sexual behavior, and other behavioral and biological factors, are also essential to consider while predicting the future acquisition of HIV. In this study, we developed and validated a prediction model for the probability of future HIV acquisition in high-risk groups. We developed prediction models using electronic medical records of patients from Nai Zindagi Trust (NZ), Pakistan. We analyzed the data for socio-demographic, behavioral (drug injection, sexual behavior), and biological features to predict the patients who are at high risk of acquiring HIV. State of the art data mining and machine learning techniques have been applied along with feature selection techniques for HIV risk prediction. The proposed model predicts HIV status with 82% accuracy having an improvement of 10–15% over dominant classifiers, i.e., SVM, Neural Network, J48, and PART. The new risk scores of HIV acquisition could assist health providers in counseling high-risk groups and in targeting intensified testing, treatment, and prevention to people at the greatest risk for HIV infection.
... Healthcare overtime has become an important domain in the field of information technology due to the conjunction of technology and its use in different applications. Use of software technology seem becoming a need for management and processing of healthcare data [1,2] and clinical documents [3]. Quality of healthcare is directly proportional to the quality of data stored in healthcare related software like EHRs, Billing Management Systems, Patient Health Records, Integrated Healthcare Systems [4][5][6]. ...
... Ho (Null Hypothesis, µR-µC =0 or µR=µC (1) H1 (Alternate Hypothesis, µR-µC ≠0 or µR≠µC (2) Where µR denotes the average implementation time of system checks (healthcare edits) through rule-based programming and µC denotes the average implementation time of system checks (healthcare edits) through conventional programming. Implementation time depicts total time spent on each check from analysis to coding and making it operational. ...
Automated medical claims processing and billing is a popular application domain of information technology. Managing medical related data is a tedious job for healthcare professionals, which distracts them from their main job of healthcare. The technology used in data management has a sound impact on the quality of healthcare data. Most of Information Technology (IT) organizations use conventional software development technology for the implementation of healthcare systems. The objective of this experimental study is to devise a mechanism for use of rule-based expert systems in medical related edits and compare it with the conventional software development technology. A sample of 100 medical edits is selected as a dataset to be tested for implementation using both technologies. Besides empirical analysis, paired t-test is also used to validate the statistical significance of the difference between the two techniques. The conventional software development technology took 254.5 working hours, while rule-based technology took 81 hours to process these edits. Rule-based technology outperformed the conventional systems by increasing the confidence value to 95% and reliability measure to 0.462 (which is < 0.5) which is three times more efficient than conventional software development technology.
... AC methods have previously been used in various fields of healthcare, including the prediction of outcomes and adverse events (13)(14)(15)(16), the prediction of diseases and wellness (17)(18)(19)(20), as well as biochemistry and genetics (21)(22)(23)(24)(25). AC methods have also been used in the field of in-hospital mortality risk estimation using the results of 12 lab tests (26). This shows the feasibility of AC methods in in-hospital mortality risk estimation. ...
We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.
... Rule generation at minimum-support values to ensure that, even with fewer occurrences, training data is not ignored for useful rules; the extracted rules are stored in a database for efficient retrieval when needed. Rule pruning is not done in proposed system [25]. In the test instance arrives at the classifiers for the classification, projections of classification rules are made on the basis of the attributes on test instances from the already derived association rules. ...
... The proposed system, composed of several smart objects to be incorporated into everyday life, is tested on both final users, i.e., self-sufficient and non-self-sufficient seniors, and caregivers, and the assessment is reasonably satisfactory. A smart solution, based on wirelessly interconnected sensors, for proper wellness ascertainment of older adults, living alone in smart homes, is proposed in [14]. This system strives to afford healthcare monitoring of older people, along with the main aim of higher wellness measurement classification accuracy and precision for better healthcare. ...
Smart homes represent one of the principal points in the new ecosystem of the Internet of Things (IoT), both for the centrality of the home in the life of individuals and the significant potential concerning the diffusion of smart objects and innovative services. While IoT-oriented smart homes can revise how inhabitants interact with the domestic environment, each well-defined piece of technology necessitates precise network performance and distinct levels of security based on the sensitivity of the controlled system and the information it handles. This editorial presents a review of the papers accepted in the special issue. The issue has focused at obtaining high-quality papers aimed at solving well-known technical problems and challenges typical of IoT-oriented smart homes.
Artificial intelligence's (AI) learning, reasoning, problem-solving, and perception features have given a new horizon to the modern healthcare infrastructure. AI is demonstrating best practices in different domains of healthcare, such as efficient drug discovery processes, accuracy in disease diagnosis, assisted surgeries, efficient utilization of human resources, and many more. This chapter provides a comprehensive overview of AI in healthcare, its applications, and recent research studies with respect to disease prediction and information-processing healthcare applications. Associated technologies of AI healthcare applications have been discussed as the realization of concepts is not possible without considering these technologies. Furthermore, it is essential to present the ethical aspect of AI in healthcare, and discuss the ethical boundaries the ethical boundaries of the applications. AI is becoming inevitable for modern and future healthcare; however, certain challenges are associated with the realization of smart and intelligent healthcare infrastructure.
Buildings, viewed as cyber-physical systems, become smart by deploying Building Management Systems (BMS). They should be aware about the state and environment of the building. This is achieved by developing a sensing system that senses different interesting factors of the building, called as “facets of sensing.” Depending on the application, different facets need to be sensed at various locations. Existing approaches for sensing these facets consist of deploying sensors at all the places so they can be sensed directly. But installing numerous sensors often aggravate the issues of user inconvenience, cost of installation and maintenance, and generation of e-waste. This article proposes how intelligently using the existing information can help to estimate the facets in cyber-physical systems like buildings, thereby reducing the sensors to be deployed. In this article, an optimization framework has been developed, which optimally deploys sensors in a building such that it satisfies BMS requirements with minimum number of sensors. The proposed solution is applied to real-world scenarios with cyber-physical systems. The results indicate that the proposed optimization framework is able to reduce the number of sensors by 59% and 49% when compared to the baseline and heuristic approach, respectively.
