114 research outputs found
A clinical decision support system for detecting and mitigating potentially inappropriate medications
Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly.
Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects.
Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches.
Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice
Explainable temporal data mining techniques to support the prediction task in Medicine
In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset
Twitter activity surrounding the Finnish green party's cannabis legalisation proposal : A mixed-methods analysis
Background: In September 2021, a Finnish political party, the Greens, voted to include cannabis policy reform in their party programme, which would legalise the use, possession, manufacture and sale of cannabis. A rapid public discussion has emerged on different social media platforms, including Twitter. Methods: We downloaded 10 days of Twitter data and prepared it for further text analysis, including sentiment, topic modelling and thematic content analysis. Results: Before the proposal, the average daily number of tweets was approximately 140. However, during the week of the proposal, there was a significant increase in tweet volume, reaching a peak of 6,600 tweets on a single day, with a daily average of over 2,700 tweets. Sentiment analysis showed that during the public discussion, the sentiment scores of the tweets were more likely to be positive. Through topic modelling analysis, we obtained the weight of the topic for each tweet, which enabled us to identify the most representative tweets in our corpus. To narrow the sample size for content analysis, we selected tweets that had a topic percentage distribution of over 0.95 (N=188) for closer thematic content analysis. Several positive and negative themes emerged, which were then categorised under broader topics. Similar themes were identified in the most retweeted, liked and commented tweets, which came mainly from known public figures, including politicians, health experts and NGO leaders. Conclusion: Our results show that the discussion was not limited to cannabis legalisation, but instead covered a variety of topics related to drug policy.Peer reviewe
Building explainable graph neural network by sparse learning for the drug-protein binding prediction
Explainable Graph Neural Networks (GNNs) have been developed and applied to
drug-protein binding prediction to identify the key chemical structures in a
drug that have active interactions with the target proteins. However, the key
structures identified by the current explainable GNN models are typically
chemically invalid. Furthermore, a threshold needs to be manually selected to
pinpoint the key structures from the rest. To overcome the limitations of the
current explainable GNN models, we propose our SLGNN, which stands for using
Sparse Learning to Graph Neural Networks. Our SLGNN relies on using a
chemical-substructure-based graph (where nodes are chemical substructures) to
represent a drug molecule. Furthermore, SLGNN incorporates generalized fussed
lasso with message-passing algorithms to identify connected subgraphs that are
critical for the drug-protein binding prediction. Due to the use of the
chemical-substructure-based graph, it is guaranteed that any subgraphs in a
drug identified by our SLGNN are chemically valid structures. These structures
can be further interpreted as the key chemical structures for the drug to bind
to the target protein. We demonstrate the explanatory power of our SLGNN by
first showing all the key structures identified by our SLGNN are chemically
valid. In addition, we illustrate that the key structures identified by our
SLGNN have more predictive power than the key structures identified by the
competing methods. At last, we use known drug-protein binding data to show the
key structures identified by our SLGNN contain most of the binding sites
Effective Entity Augmentation By Querying External Data Sources
Users often want to augment and enrich entities in their datasets with relevant information from external data sources. As many external sources are accessible only via keyword-search interfaces, a user usually has to manually formulate a keyword query that extract relevant information for each entity. This approach is challenging as many data sources contain numerous tuples, only a small fraction of which may contain entity-relevant information. Furthermore, different datasets may represent the same information in distinct forms and under different terms (e.g., different data source may use different names to refer to the same person). In such cases, it is difficult to formulate a query that precisely retrieves information relevant to an entity. Current methods for information enrichment mainly rely on lengthy and resource-intensive manual effort to formulate queries to discover relevant information. However, in increasingly many settings, it is important for users to get initial answers quickly and without substantial investment in resources (such as human attention). We propose a progressive approach to discovering entity-relevant information from external sources with minimal expert intervention. It leverages end users\u27 feedback to progressively learn how to retrieve information relevant to each entity in a dataset from external data sources. Our empirical evaluation shows that our approach learns accurate strategies to deliver relevant information quickly
Benchmarking Inverse Optimization Algorithms for Molecular Materials Discovery
Machine learning-based molecular materials discovery has attracted enormous
attention recently due to its flexibility in dealing with black box models.
Yet, metaheuristic algorithms are not as widely applied to materials discovery
applications. We comprehensively compare 11 different optimization algorithms
for molecular materials design with targeted properties. These algorithms
include Bayesian Optimization (BO) and multiple metaheuristic algorithms. We
performed 5000 material evaluations repeated 5 times with different randomized
initialization to optimize defined target properties. By maximizing the bulk
modulus and minimizing the Fermi energy through perturbing parameterized
molecular representations, we estimated the unique counts of molecular
materials, mean density scan of the objectives space, mean objectives, and
frequency distributed over the materials' representations and objectives. GA,
GWO, and BWO exhibit higher variances for materials count, density scan, and
mean objectives; and BO and Runge Kutta optimization (RUN) display generally
lower variances. These results unveil that nature-inspired algorithms contain
more uncertainties in the defined molecular design tasks, which correspond to
their dependency on multiple hyperparameters. RUN exhibits higher mean
objectives whereas BO displayed low mean objectives compared with other
benchmarked methods. Combined with materials count and density scan, we propose
that BO strives to approximate a more accurate surrogate of the design space by
sampling more molecular materials and hence have lower mean objectives, yet RUN
will repeatedly sample the targeted molecules with higher objective values. Our
work shed light on automated digital molecular materials design and is expected
to elicit future studies on materials optimization such as composite and alloy
design based on specific desired properties.Comment: 15 pages, 5 figures, for the main manuscrip
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