| 1. |
- Agriesti, Serio, et al.
(author)
-
A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models
- 2023
-
In: IEEE Open Journal of Intelligent Transportation Systems. - 2687-7813. ; 4, s. 740-754
-
Journal article (peer-reviewed)abstract
- Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors' knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.
|
|
| 2. |
- Alam, Mahbub Ul, 1988-
(author)
-
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things : Enhancing COVID-19 & Early Sepsis Detection
- 2024
-
Doctoral thesis (other academic/artistic)abstract
- This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalized and participatory care—a transition that could be facilitated by these emerging fields. The thesis accentuates how IoMT could serve as a robust platform for data aggregation, analysis, and transmission, which could empower healthcare providers to deliver more effective care. The COVID-19 pandemic has particularly stressed the importance of such patient-centric systems for remote patient monitoring and disease management.The integration of ML-driven CDSSs with IoMT is viewed as an extremely important step in healthcare systems that could offer real-time decision-making support and enhance patient health outcomes. The thesis investigates ML's capability to analyze complex medical datasets, identify patterns and correlations, and adapt to changing conditions, thereby enhancing its predictive capabilities. It specifically focuses on the development of IoMT-based CDSSs for COVID-19 and early sepsis detection, using advanced ML methods and medical data.Key issues addressed cover data annotation scarcity, data sparsity, and data heterogeneity, along with the aspects of security, privacy, and accessibility. The thesis also intends to enhance the interpretability of ML prediction model-based CDSSs. Ethical considerations are prioritized to ensure adherence to the highest standards.The thesis demonstrates the potential and efficacy of combining ML with IoMT to enhance CDSSs by emphasizing the importance of model interpretability, system compatibility, and the integration of multimodal medical data for an effective CDSS.Overall, this thesis makes a significant contribution to the fields of ML and IoMT in healthcare, featuring their combined potential to enhance CDSSs, particularly in the areas of COVID-19 and early sepsis detection.The thesis hopes to enhance understanding among medical stakeholders and acknowledges the need for continuous development in this sector.
|
|
| 3. |
- Alam, Mahbub Ul, et al.
(author)
-
COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning
- 2023
-
In: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). ; , s. 646-653
-
Conference paper (peer-reviewed)abstract
- COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.
|
|
| 4. |
- Alam, Mahbub Ul, 1988-, et al.
(author)
-
SHAMSUL : Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction
- 2023
-
In: Nordic Machine Intelligence. - 2703-9196. ; 3:1, s. 27-47
-
Journal article (peer-reviewed)abstract
- The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To address this matter, our study delves into the application of four well-established interpretability methods: Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), Gradient-weighted Class Activation Mapping (Grad-CAM), and Layer-wise Relevance Propagation (LRP). Leveraging the approach of transfer learning with a multi-label-multi-class chest radiography dataset, we aim to interpret predictions pertaining to specific pathology classes. Our analysis encompasses both single-label and multi-label predictions, providing a comprehensive and unbiased assessment through quantitative and qualitative investigations, which are compared against human expert annotation. Notably, Grad-CAM demonstrates the most favorable performance in quantitative evaluation, while the LIME heatmap score segmentation visualization exhibits the highest level of medical significance. Our research underscores both the outcomes and the challenges faced in the holistic approach adopted for assessing these interpretability methods and suggests that a multimodal-based approach, incorporating diverse sources of information beyond chest radiography images, could offer additional insights for enhancing interpretability in the medical domain.
|
|
| 5. |
- Bampa, Maria, et al.
(author)
-
A clustering framework for patient phenotyping with application to adverse drug events
- 2020
-
In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). - : IEEE. - 9781728194295 - 9781728194301 ; , s. 177-182
-
Conference paper (peer-reviewed)abstract
- We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.
|
|
| 6. |
- Bampa, Maria, 1992-
(author)
-
Data-Driven AI for Patient and Public Health : On the Use of Multisource and Multimodal Data in Machine Learning to Improve Healthcare
- 2024
-
Doctoral thesis (other academic/artistic)abstract
- The integration of artificial intelligence in healthcare has created a new era of advancements, reshaping patient care and revolutionizing public health interventions. Through artificial intelligence, healthcare providers and public health authorities can optimize interventions, leading to more precise and efficient responses that enhance patient outcomes and address public health challenges effectively. The past decade has witnessed a rapid digital transformation across industries, and healthcare is no exception. This evolution is evident in the widespread adoption of electronic health records and healthcare information systems and the integration of diverse technologies, including handheld, wearable, and smart devices.A central challenge in this digital shift lies in representing data from multiple sources and modalities for downstream machine learning tasks. This complexity stems from the varied longitudinal or contextual events in patients' historical records, encompassing lab tests, vital signs, diagnoses, and drug administration. Additionally, the challenge extends to predictive modeling and constructing robust models that accurately classify future health events, taking into consideration heterogeneous health-related data. Electronic phenotyping, crucial for identifying fine-grained disease/patient clusters, is also a central problem when utilizing multisource and multimodal information effectively to create meaningful patient profiles. In the context of public health interventions, exemplified by crises like the COVID-19 pandemic, decision-making requires a delicate balance between optimizing intervention effectiveness and considering economic and societal well-being.This Ph.D. thesis seeks to unravel the potential of multisource and multimodal health observational data in generating patient phenotypes and predictions for both individual health and public health surveillance. It addresses the following central question: How can multisource and multimodal observational health data be effectively harnessed, using machine learning, to enhance patient and public health? Comprising five studies, the thesis confronts challenges posed by diverse data sources and modalities, exploring strategies for creating comprehensive patient profiles, developing robust classification models, and employing clustering methods tailored to observational health data. The research seeks to provide valuable insights into integrating AI in healthcare, with a specific emphasis on the complexities of multisource and multimodal data integration. It underscores the importance of exploring heterogeneous health observational data to deepen our understanding of patient health and optimize machine learning applications. Emphasizing the intricate nature of health data, the thesis discusses careful data handling and innovative methodologies to maximize its potential impact on improving patient outcomes and informing public health strategies. The effective management of heterogeneous observational health data requires thoughtful consideration due to their varied sources and inherent complexities.
|
|
| 7. |
- Chaliane Junior, Guilherme Dinis, et al.
(author)
-
Policy Evaluation with Delayed, Aggregated Anonymous Feedback
- 2022
-
In: Discovery Science. - Cham : Springer Nature. - 9783031188398 - 9783031188404 ; , s. 114-123
-
Conference paper (peer-reviewed)abstract
- In reinforcement learning, an agent makes decisions to maximize rewards in an environment. Rewards are an integral part of the reinforcement learning as they guide the agent towards its learning objective. However, having consistent rewards can be infeasible in certain scenarios, due to either cost, the nature of the problem or other constraints. In this paper, we investigate the problem of delayed, aggregated, and anonymous rewards. We propose and analyze two strategies for conducting policy evaluation under cumulative periodic rewards, and study them by making use of simulation environments. Our findings indicate that both strategies can achieve similar sample efficiency as when we have consistent rewards.
|
|
| 8. |
- Emma, Briggs, et al.
(author)
-
Mitigating discrimination in clinical machine learning decision support using algorithmic processing techniques
- 2020
-
In: Discovery Science. - Cham : Springer. - 9783030615260 - 9783030615277 ; , s. 19-33
-
Conference paper (peer-reviewed)abstract
- Discrimination on the basis of protected characteristics - such as race or gender - within Machine Learning (ML) is an insufficiently addressed yet pertinent issue. This line of investigation is particularly lacking within clinical decision-making, for which the consequences can be life-altering. Certain real-world clinical ML decision tools are known to demonstrate significant levels of discrimination. There is currently indication that fairness can be improved during algorithmic processing, but this has not been widely examined for the clinical setting. This paper therefore explores the extent to which novel algorithmic processing techniques may be able to mitigate discrimination against protected groups in clinical resource-allocation ML decision-support algorithms. Specifically, three state-of-the-art discrimination mitigation techniques are compared, one for each stage of algorithmic processing, when applied to a real-world clinical ML decision algorithm which is known to discriminate with regards to racial characteristics. The results are promising, revealing that such techniques could significantly improve the fairness of clinical resource-allocation ML decision tools, particularly during pre- and post- processing. Discrimination is shown to be reduced to arbitrary levels at little to no cost to accuracy. Similar studies are needed to consolidate these results. Other future recommendations include working towards a generalisable framework for ML fairness in healthcare.
|
|
| 9. |
- Gopalacharyulu, Peddinti V., et al.
(author)
-
Data integration and visualization system for enabling conceptual biology
- 2005
-
In: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 21 Suppl 1, s. i177-i185
-
Journal article (peer-reviewed)abstract
- MOTIVATION: Integration of heterogeneous data in life sciences is a growing and recognized challenge. The problem is not only to enable the study of such data within the context of a biological question but also more fundamentally, how to represent the available knowledge and make it accessible for mining.RESULTS: Our integration approach is based on the premise that relationships between biological entities can be represented as a complex network. The context dependency is achieved by a judicious use of distance measures on these networks. The biological entities and the distances between them are mapped for the purpose of visualization into the lower dimensional space using the Sammon's mapping. The system implementation is based on a multi-tier architecture using a native XML database and a software tool for querying and visualizing complex biological networks. The functionality of our system is demonstrated with two examples: (1) A multiple pathway retrieval, in which, given a pathway name, the system finds all the relationships related to the query by checking available metabolic pathway, transcriptional, signaling, protein-protein interaction and ontology annotation resources and (2) A protein neighborhood search, in which given a protein name, the system finds all its connected entities within a specified depth. These two examples show that our system is able to conceptually traverse different databases to produce testable hypotheses and lead towards answers to complex biological questions.
|
|
| 10. |
- Hollmén, Jaakko, et al.
(author)
-
Clustering Diagnostic Profiles of Patients
- 2019
-
In: Artificial Intelligence Applications and Innovations. - Cham : Springer. - 9783030198220 - 9783030198237 ; , s. 120-126
-
Conference paper (peer-reviewed)abstract
- Electronic Health Records provide a wealth of information about the care of patients and can be used for checking the conformity of planned care, computing statistics of disease prevalence, or predicting diagnoses based on observed symptoms, for instance. In this paper, we explore and analyze the recorded diagnoses of patients in a hospital database in retrospect, in order to derive profiles of diagnoses in the patient database. We develop a data representation compatible with a clustering approach and present our clustering approach to perform the exploration. We use a k-means clustering model for identifying groups in our binary vector representation of diagnoses and present appropriate model selection techniques to select the number of clusters. Furthermore, we discuss possibilities for interpretation in terms of diagnosis probabilities, in the light of external variables and with the common diagnoses occurring together.
|
|
