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The Role of IoT in Healthcare Services
Published in Ankan Bhattacharya, Bappadittya Roy, Samarendra Nath Sur, Saurav Mallik, Subhasis Dasgupta, Internet of Things and Data Mining for Modern Engineering and Healthcare Applications, 2023
Devendra Singh, Himanshu Pandey, Virendra Singh, Devendra Pandey
It is concluded that the rapidly progressing knowledge and developing IoT techniques have paved the way for the greater possibility for emerging medical care information models. However, difficulties still exist in attaining safe and efficient telemedical care services. Some defined sectors for future progress are documented in following ways: (1) Self-education and self-development. Facing enormous data and a high level of difficulty, IoT itself cannot contribute to the rehabilitation care system or to creating medical sources. Proper and efficient treatments must be available depending upon major factors like immediate identification of the patient's disease and providing proper rehabilitation treatments on the basis of disease identification. Even with identical symptoms, the state of the sufferer varies from one to another. All the elements have to be considered in order to provide efficient medical treatment. A computer-based system that is merely supported by data acquired with the help of sensors helps to keep previous records of former cases, whereas self-learning technology can actively and intelligently identify the disease and help in providing proper treatments. Some self-learning algorithms, for example, Genetic Algorithm (GA), Ant-Colony Optimization (ACO), and Simulated Annealing (SA), can be utilized to investigate data and mining information.
Fundamentals of Receptor Assessment
Published in Jack Daugherty, Assessment of Chemical Exposures, 2020
Root, Katzin and Schnare devised an interesting table from reviewing studies of exposure to polyhalogenated biphenyls (PHBs, specifically PBBs and PCBs). They divided the studies into six exposure cohorts if that term may be used in such a way: ambient, low-level, occupational, extended occupational, lifelong ambient and massive. They found that the accumulated PHB in blood and body fat increased respectively as I have listed the exposure cohorts. The ambient exposure cohort had no observed health indications and biological functions were normal with ppb level PHB accumulated in the blood and fat for less than 35 years. The low-level exposure cohort, in the less than 1 ppm BHP accumulation range for more than 35 years, presented subtle symptoms such as fatigue, weakness, nervousness, pain in the joints and headaches among others. There is a wide overlap of these symptoms with other syndromes and diseases. Symptoms noted with lipophilic chemicals such as PHBs are not different from those reported for hydrophilic chemicals indicating they may relate to psychiatric or psychological conditions. Root, Katzin and Schnare are convinced however that differences between exposed groups and control groups cannot be explained without considering exposure to toxic substances in the etiology.
Health and Safety at Work
Published in Stephen Pheasant, Christine M. Haslegrave, Bodyspace, 2018
Stephen Pheasant, Christine M. Haslegrave
More recently it has become increasingly clear, however, that many people with RSI/WRULD cannot easily be allocated to any of the traditionally recognised clinical categories. These people — who are described as suffering from Type II RSI — typically report symptoms of pain and dysfunction at multiple sites in the upper limb (or limbs), shoulder region and neck. These symptoms are often described as ‘diffuse’. This is an unfortunate choice of word, in that it tends to imply that the symptoms are vague and insubstantial. They are not — at least, not always. In some cases they are crippling. A better description is ‘disseminated’. It is likewise often said that these people have no objective clinical signs. (In medical parlance, a symptom is something reported by the patient; a sign is something that the physician observes.) This distinction is only partly true at best, in that the principal signs that may be observed are ones in which the patient reports pain, either on the palpation of tender structures (mainly muscles) or on the performance of certain diagnostic manoeuvres (the details of which need not concern us here).
The role of feedback in the robotic-assisted upper limb rehabilitation in people with multiple sclerosis: a systematic review
Published in Expert Review of Medical Devices, 2023
Marialuisa Gandolfi, Stefano Mazzoleni, Giovanni Morone, Marco Iosa, Filippo Galletti, Nicola Smania
The type and severity of symptoms and the disease course vary from one person to another. However, around 66% of people with MS (PwMS) reported upper limb dysfunctions and 76% dexterity impairment leading to a reduced Quality of Life and active participation [3,4]. In particular, fine and gross manual dexterity is altered compared to normative data in most persons with MS, with abnormalities in gross manual dexterity more prevalent earlier in the disease course [5]. Moreover, since the early stage of the disease (no/mild disability), 80–90% of PwMs complained of hypoesthesia, and approximately 10% impaired body functions such as grip strength and endurance and coordination disturbances [6]. Therefore, developing therapeutic strategies to promote upper limb functional recovery is fundamental.
Exponential and non-Exponential Based Generalized Similarity Measures for Complex Hesitant Fuzzy Sets with Applications
Published in Fuzzy Information and Engineering, 2020
Tahir Mahmood, Ubaid ur Rehman, Zeeshan Ali
The symptoms of different diseases are different. The medical diagnosis depends on the victim’s symptoms which show what type of disease a victim has. The multiple symptoms of a victim represent a symptom set and a set of diseases can represent by different diseases.
Double-quantitative decision rough set over two universes and application to African swine fever decision-making
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2021
Xiaoyuan Hu, Bingzhen Sun, Ting Wang, Chao Jiang
As is well known, uncertainty is an inseparable aspect of medical diagnosis, because a symptom is an uncertainty index of whether or not a disease is occurring. Symptoms and diseases belong to two different universes, although they are interrelated with each other. Thus, uncertainty arises when describing the interrelations between symptoms and diseases in clinical settings. In a specific group of patients, each patient may show many symptoms, just as each disease could have many symptoms. This makes it very difficult for a doctor to decide which disease the patient has. Until now, there are various data-based medical diagnosis methods that have been developed, including expert systems and fuzzy approaches (Liu & Qin, 2017). Taking advantages of human expertise and experiential knowledge, the expert system has been successfully utilised in medical diagnosis. However, the limitations of acquiring expert knowledge and maintaining the medical database are revealed in expert system-based medical diagnosis (Dheeba et al., 2017). An additional potential weakness of this approach is that the medical database may include conflicting expert knowledge that may vary from case to case. This variation may preclude a general mathematical formulation that can be utilised for medical diagnosis in different fields. Rough set theory is a new mathematical tool for studying intelligent decision systems characterised by insufficient and incomplete information, and it is an effective tool to acquire knowledge with its core concepts of lower and upper approximations (Huang et al., 2017; Prerna et al., 2014; Sun & Ma, 2015). So far rough set theory has become one of the important and effective methods for decision-making problem with incomplete and inaccurate available information (C. Wang, Qian, et al., 2018). Also, it has established an effective qualitative method for dealing with uncertainty in a wide variety of applications related to knowledge discovery and pattern recognition (Sun & Ma, 2018; B. Sun et al., 2019). In the framework of two universes, this paper explores a new application field of decision-making under uncertainty by using rough set theory.