| 1. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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A Hybrid Case-Based System in Stress Diagnosis and Treatment
- 2012
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Computer-aided decision support systems play anincreasingly important role in clinical diagnosis and treatment.However, they are difficult to build for domains where thedomain theory is weak and where different experts differ indiagnosis. Stress diagnosis and treatment is an example of such adomain. This paper explores several artificial intelligencemethods and techniques and in particular case-based reasoning,textual information retrieval, rule-based reasoning, and fuzzylogic to enable a more reliable diagnosis and treatment of stress.The proposed hybrid case-based approach has been validated byimplementing a prototype in close collaboration with leadingexperts in stress diagnosis. The obtained sensitivity, specificityand overall accuracy compared to an expert are 92%, 86% and88% respectively.
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| 2. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Multi-Modal Case-Based System for Clinical Diagnosis and Treatment in Stress Management
- 2009
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Konferensbidrag (refereegranskat)abstract
- A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often base their diagnosis and decision on manual inspection of signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with the manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. A computer system, classifying the sensor signals is one valuable property assisting a clinician. This paper presents a case-based system that assist a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning, and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with the experts. The experimental results suggest that such a system is valuable both for the less experienced clinicians and for experts where the system may be seen as a second option.
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| 3. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Multi-Module Case Based Biofeedback System for Stress Treatment
- 2011
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 51:2, s. 107-115
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Tidskriftsartikel (refereegranskat)abstract
- Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.
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| 4. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning
- 2008
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Ingår i: Proc. 9th European Conference on Case-based Reasoning. ; , s. 57-68
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Konferensbidrag (refereegranskat)abstract
- Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician's competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinicians, acting as second opinion for experienced clinicians. The three phase CBR framework is deployed to classify a patient, estimate initial parameters and to make recommendations for biofeedback training by retrieving and comparing with previous similar cases in terms of features extracted. The three phases work independently from each other. Moreover, fuzzy techniques are incorporated into our CBR system to better accommodate uncertainty in clinicians reasoning as well as decision analysis. All parts in the proposed framework have been implemented and primarily validated in a prototypical system. The initial result shows how the three phases functioned with CBR technique to assist biofeedback training. Eventually the system enables the clinicians to allow a patient to train himself/herself unsupervised.
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| 5. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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A vision-based indoor navigation system for individuals with visual impairment
- 2019
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Ingår i: International Journal of Artificial Intelligence. - : CESER Publications. - 0974-0635. ; 17:2, s. 188-201
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Tidskriftsartikel (refereegranskat)abstract
- Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.
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| 6. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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An Overview of three Medical Applications Using Hybrid Case-Based Reasoning
- 2012
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Konferensbidrag (refereegranskat)abstract
- Today more and more patient journals are stored electronically but they are rarely used for more than statistical purpose. In this paper we present an approach where clinical patient journals are used for improved clinical decision making on an individual level. The underlying assumption is that medical staff benefit from comparing a specific patient with similar patient. By comparing symptoms, diagnosis, medication and outcome in an individual level they are able to make more informed decisions at the point of care. This paper presents some parts of our more than ten years research efforts in the area and some of the projects and their underlying hybrid approaches. As a foundation for all our projects we use case-based reasoning (CBR) research in combination with techniques from artificial intelligence, data mining, statistics and search techniques. Three systems are presented in two medical domains 1) decision support for stress diagnosis 2) decision support for stress treatment and 3) decision support for post-operative pain treatment and discuss results and lessons learned.
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| 7. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—an overview
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol. 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 171-192
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Bokkapitel (refereegranskat)abstract
- As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.
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| 8. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—case studies
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 261-291
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Bokkapitel (refereegranskat)abstract
- To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning (MR) in health informatics are often challenging as they comprise with multivariate information coming from heterogeneous sources e.g. sensor signals, text, etc. This book chapter presents the research development of AI, ML and MR as applications in health informatics. Five case studies on health informatics have been discussed and presented as (1) advanced Parkinson’s disease, (2) stress management, (3) postoperative pain treatment, (4) driver monitoring, and (5) remote health monitoring. Here, the challenges, solutions, models, results, limitations are discussed with future wishes.
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| 9. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Bibliometric Profiling of a Group: A Discussion on Different Indicators
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Now-a-days in some advanced countries bibliometric profiling plays a vital role when making decision on promotion, fund allocation and award prizes. Accurate identification of this is important since it is becoming important to assess scientific output for a researcher or a group of researcher. This paper presents and discusses several most common indicators of bibliometric profiling together with h- and g-indexes. A case study has been conducted on 101 scientific articles with three most well known search engines. The study results using several indicators are presented in this report.
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| 10. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Big Data Analytics in Health Monitoring at Home
- 2017
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Ingår i: Medicinteknikdagarna 2017 MTD 2017.
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Konferensbidrag (refereegranskat)abstract
- This paper proposed a big data analytics approach applied in the projects ESS-H and E-care@home in the context of biomedical and health informatics with the advancement of information fusion, data abstraction, data mining, knowledge discovery, learning, and reasoning [1][2]. Data are collected through the projects, considering both the health parameters, e.g. temperature, bio-impedance, skin conductance, heart sound, blood pressure, pulse, respiration, weight, BMI, BFP, movement, activity, oxygen saturation, blood glucose, heart rate, medication compliance, ECG, EMG, and EEG, and the environmental parameters e.g. force/pressure, infrared (IR), light/luminosity, photoelectric, room-temperature, room-humidity, electrical usage, water usage, RFID localization and accelerometers. They are collected as semi-structured/unstructured, continuous/periodic, digital/paper record, single/multiple patients, once/several-times, etc. and stored in a central could server [5]. Thus, with the help of embedded system, digital technologies, wireless communication, Internet of Things (IoT) and smart sensors, massive quantities of data (so called ‘Big Data’) with value, volume, velocity, variety, veracity and variability are achieved [2]. The data analysis work in the following three steps. In Step1, pre-processing, future extraction and selection are performed based on a combination of statistical, machine learning and signal processing techniques. A novel strategy to fuse the data at feature level and as well as at data level considers a defined fusion mechanism [3]. In Step2, a combination of potential sequences in the learning and search procedure is investigated. Data mining and knowledge discovery, using the refined data from the above for rule extraction and knowledge mining, with support for anomaly detection, pattern recognition and regression are also explored here [4]. In Step3, adaptation of knowledge representation approaches is achieved by combining different artificial intelligence methods [3] [4]. To provide decision support a hybrid approach is applied utilizing different machine learning algorithms, e.g. case-based reasoning, and clustering [4]. The approach offers several data analytics tasks, e.g. information fusion, anomaly detection, rules and knowledge extraction, clustering, pattern identification, correlation analysis, linear regression, logic regression, decision trees, etc. Thus, the approach assist in decision support, early detection of symptoms, context awareness and patient’s health status in a personal environment.
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