Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring patients’ vital signs in ambulatory settings. A major challenge is to design such ultra-low energy devices for long-term patient monitoring. In this paper, we present a real-time event-driven classification technique based on the random forest classification scheme, which uses a confidence-related decision-making process. The main goal of this technique is to maintain a high classification accuracy while reducing the complexity of the classification algorithm. We validate our approach on a well-established and complete MI database (Physiobank, PTB Diagnostic ECG database [1]). Our experimental evaluation demonstrates that our real-time classification scheme outperforms the existing approaches in terms of energy consumption and battery lifetime by a factor of 2.60, with no classification quality loss

Towards Adaptive Technology in Routine Mental Healthcare

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients’ needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.publishedVersio

Privacy-Preserving Machine Learning and Data Sharing in Healthcare Applications

Artificial intelligence (AI) and automated decision-making have the potential to improve accuracy and efficiency in healthcare applications. In particular, AI is proved to outperform human experts in certain domains. However, the application of AI and machine learning for automated decision-making in healthcare comes with challenges, such as security and privacy preservation. Such issues are among the primary concerns that must be addressed as they may negatively affect individuals. For instance, a patient’s privacy is violated if sharing his/her medical data with a third-party data recipient reveals that he/she had a medical condition. Furthermore, particular guidelines, e.g., General Data Protection Regulation (GDPR), are proposed to legally protect the privacy of patients that has to be observed while employing AI and machine learning in this domain. In order to address such privacy concerns, in this thesis, we consider two principal directions for the analysis of data and concentrate our research on them. In one primary direction, the analysis is performed on the published/shared data. Therefore, the data holder needs to consider particular measures to protect the privacy of data subjects, for instance, by perturbing the data before publishing. In this thesis, along this direction, we propose an anonymization framework, formulated as an optimization problem, for datasets with both categorical and numerical attributes. The proposed framework is based on clustering the data samples by considering the diversity issue in anonymization to reduce the risks of identity and attribute linkage attacks. Our method achieves anonymity by formulating and solving this problem as a constrained optimization problem, by jointly considering the k-anonymity, l-diversity, and t-closeness privacy models. We evaluate our framework on popular publicly available structured healthcare data. The other primary direction is to perform analysis without publishing the data. In such settings, we consider multiple parties, each of which holds a different part of the data. The objective is to analyze the data held on these parties without direct access to the data record values. In this thesis, along this direction, we present a scalable privacypreserving distributed learning framework based on the Extremely Randomized Trees (ERT) algorithm and Secure Multiparty Computation (SMC) techniques. We build a machine learning model based on the entire dataset by analyzing the data locally at each party and combining the results of this analysis. We evaluate the distributed implementation of our technique based on healthcare datasets collected in the INTROMAT project and demonstrate its prediction performance. In summary, the research in this thesis contributes to the possibility of exploiting health data in the healthcare setting for analysis and automatic decision-making without privacy violation. This has a long-term potential for better decision-making in the healthcare context, diagnosis, and treatment, at an affordable cost

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures, as the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%

A Practical Methodology for Anonymization of Structured Health Data

Hospitals, as data custodians, have the need to share a version of the data in hand with external research institutes for analysis purposes. For preserving the privacy of the patients, anonymization methods are employed to produce a modified version of data for publishing; these methodologies shall not reveal the patient’s information while maintaining the utility of data. In this article, we propose a practical methodology for anonymization of structured health data based on cryptographic algorithms, which preserves the privacy by construction. Our initial experimental results indicate that the methodology might outperform the existing solutions by retaining the utility of data

Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Artificial intelligence and machine learning have recently attracted considerable attention in the healthcare domain. The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, for instance, hospitals or patients’ personal devices. One main difficulty lies in analyzing such data without compromising patients’ privacy and personal data, which is a primary concern in healthcare applications. Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about the data subjects. In this paper, we propose a distributed extremely randomized trees algorithm for learning from distributed data with privacy preservation. We present the implementation of our technique (which we refer to as k -PPD-ERT) on a cloud platform and demonstrate its performance based on medical data, including Heart Disease, Breast Cancer, and mental health datasets (Depresjon and Psykose datasets) associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project

Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Artificial intelligence and machine learning have recently attracted considerable attention in the healthcare domain. The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, for instance, hospitals or patients’ personal devices. One main difficulty lies in analyzing such data without compromising patients’ privacy and personal data, which is a primary concern in healthcare applications. Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about the data subjects. In this paper, we propose a distributed extremely randomized trees algorithm for learning from distributed data with privacy preservation. We present the implementation of our technique (which we refer to as k -PPD-ERT) on a cloud platform and demonstrate its performance based on medical data, including Heart Disease, Breast Cancer, and mental health datasets (Depresjon and Psykose datasets) associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project

Extremely Randomized Trees With Privacy Preservation for Distributed Structured Health Data

Artificial intelligence and machine learning have recently attracted considerable attention in the healthcare domain. The data used by machine learning algorithms in healthcare applications is often distributed over multiple sources, for instance, hospitals or patients’ personal devices. One main difficulty lies in analyzing such data without compromising patients’ privacy and personal data, which is a primary concern in healthcare applications. Therefore, in these applications, we are interested in running machine learning algorithms over distributed data without disclosing sensitive information about the data subjects. In this paper, we propose a distributed extremely randomized trees algorithm for learning from distributed data with privacy preservation. We present the implementation of our technique (which we refer to as k -PPD-ERT) on a cloud platform and demonstrate its performance based on medical data, including Heart Disease, Breast Cancer, and mental health datasets (Depresjon and Psykose datasets) associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) project