Data Mining Practical Machine Learning Tools and Techniques

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A Framework for Ethics in Cyber-Physical-Human Systems

An important trend in meeting the needs of modern caregiving is providing a well-rounded care delivery. In doing this, access to data of care receiver is important. This, however, has been shown to be possible with connected health with the aid of modern technology. In this paper, we are presenting a design that will expand on the opportunities for better data accessibility and use, by integrating the recommendation system into connected health. In order to ensure a design that meets the needs of care receivers, we conducted two independent surveys with a view to gathering requirements for the design. The result has shown the relevance of timeliness in caregiving along with what the care receivers are interested in such as what their data say about their conditions, the immediate and future expectation of their conditions, and collaborative efforts in managing their conditions. The requirements gathered were used in the design and implementation of the mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). The benefits of this solution have been real-time recommendations' availability, cost saving, adequate care, and access to health data and information for better care decision-making.

/pdf/integration-of-recommendation-systems-into-connected-health-1a1ucywl01.pdf

Integration of Recommendation Systems Into Connected Health for Effective Management of Chronic Diseases

The Upanishads are known as one of the oldest philosophical texts in the world that form the foundation of Hindu philosophy. The Bhagavad Gita is the core text of Hindu philosophy and is known as a text that summarises the key philosophies of the Upanishads with a major focus on the philosophy of karma. These texts have been translated into many languages and there exist studies about themes and topics that are prominent; however, there is not much done using language models which are powered by deep learning. In this paper, we use advanced language models such as BERT to provide topic modelling of the Upanishads and the Bhagavad Gita. We then map those topics of the Bhagavad Gita and the Upanishads since it is well known that Bhagavad Gita summarizes the key messages in the Upanishads. We also analyse the distinct and overlapping topics amongst the texts and visualise the link of selected texts of the Upanishads with the Bhagavad Gita. Our results show very high similarity between the topics of these two texts with the mean cosine similarity of 73%. We find that out of the fourteen topics extracted from the Bhagavad Gita, nine of them have a cosine similarity of more than 70% with the topics of the Upanishads. We also find that topics generated by the BERT-based models show very high coherence when compared to the conventional models. Our best-performing model gives a coherence score of 73% on the Bhagavad Gita and 69% on the Upanishads. The visualization of the low-dimensional embeddings of these texts shows very clear overlapping themes among their topics adding another level of validation to our results.

Artificial intelligence for topic modelling in Hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita

The Smart and Connected Health (SCH) program at the National Science Foundation (NSF) has been established as a stand-alone solicitation since 2012. This article reviews and analyzes the 100 projects that have been funded since 2012 to understand their characteristics and the research challenges they have addressed in SCH. Descriptive analysis, topic analysis based on Latent Dirichlet Allocation (LDA), and comparative content analysis were performed. Our study indicated that NSF SCH projects, featured with collaborative and multidisciplinary research endeavor, have been exploring more than 36 diseases or health problems, and five major research challenges including electronic health record (HER) data processing, system design or computational model building, personalized or patient-centered medicine, training and education, and privacy preserving. Much more research projects are needed to investigate algorithms, devices, and impacts of smart health on diseases and communities.

Smart and Connected Health Projects: Characteristics and Research Challenges

Latent Dirichlet Allocation (LDA), as the most popular topic model, models documents as mixtures of topics and topics as mixtures of words. Topic mixture well represents documents while words mixture extracts meaningful topics from a corpus. The nature of LDA makes it a powerful tool in documents organizing and corpus summarizing. One limitation of LDA is that its performance depends heavily on the priors. Researchers show priors matters in LDA and propose methods to learn the priors for better modeling, regardless of using symmetric priors. However, LDA modeling ability does not necessarily consent with the performance of LDA in documents representation and topic extraction. In this paper, we propose a novel prior setting for LDA. The setting improves LDA in both documents representation and topic extraction performance. We experiment to compare our setting with symmetric priors and previously proposed priors that enhances modeling ability. Experiments on the topic quality show that LDA with our prior setting extracts better topics than LDA with other kinds of prior settings. We compare LDA document representation ability through tasks such as document clustering and document classification. These experiments demonstrate LDA with our proposed priors represents document better. Moreover, our analyses also reveal that better modeling does not necessarily lead to better performance in documents representation and topic extraction.

A Prior Setting that Improves LDA in both Document Representation and Topic Extraction

Given hypotheses that connect two “irrelevant” concepts of interest via one or multiple concepts, Hypothesis Generation (HG) attempts to judge their meaningfulness and compare or rank them accordingly. HG can accelerate scientific research and is becoming increasingly important. The basic idea of prior studies is to conduct a two or higher-order search between the two input concepts for the one-concept and multiple-concept hypotheses to evaluate them. However, these approaches inevitably encounter exponential-growing searching space when addressing multiple-concept hypotheses, making it impractical to tackle such hypotheses. We propose HG Set Net (HSN) that forms a hypothesis with any number of connecting concepts as a set and learns to evaluate the set to address this problem. HSN can evaluate any hypotheses with the same complexity and avoid higher-order search, making it computationally possible to evaluate hypotheses with numerous concepts. Besides, we present a double-margin loss to train HSN to resolve the lack of labeled hypotheses. Experiments show that HSN can not only address hypotheses with efficiency but also outperform previous approaches. The double-margin loss also reveals to boost HSN’s performance.

Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets

The embedding of Medical Subject Headings (MeSH) terms has become a foundation for many downstream bioinformatics tasks. Recent studies employ different data sources, such as the corpus (in which each document is indexed by a set of MeSH terms), the MeSH term ontology, and the semantic predications between MeSH terms (extracted by SemMedDB), to learn their embeddings. While these data sources contribute to learning the MeSH term embeddings, current approaches fail to incorporate all of them in the learning process. The challenge is that the structured relationships between MeSH terms are different across the data sources, and there is no approach to fusing such complex data into the MeSH term embedding learning. In this paper, we study the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. We propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings. COS converts the corpus, ontology, and semantic predications into MeSH term sequences, merges these sequences, and learns MeSH term embeddings using the sequences. Extensive experiments on different datasets show that COS outperforms various baseline embeddings and traditional non-embedding-based baselines.

/pdf/cos-a-new-mesh-term-embedding-incorporating-corpus-ontology-4gvvr9o6gu.pdf

COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.

Literature-Based Discovery (LBD) aims to find associations, as one or more connecting concepts, of two “unrelated” concepts hidden in the literature. The most recent studies fulfilled the task by learning the representations of the concepts and using the representations’ similarities to decide whether a connecting concept is a reasonable one. However, these approaches cannot handle “complex” associations with multiple connecting concepts properly. To address this issue, we propose a neural network model LBDSetNet which can assign a “credibility” score to a plausible association with either one or more connecting concepts. By unifying both the literature and the candidate associations as bags of concepts, we can generate “less credible” literature and train LBDSetNet by enforcing it to distinguish the generated and original literature, overcoming the lack of labeled associations. We also propose a new double-margin cost function for better model training by generating “more credible” documents. We experiment to show that our model can find “complex” associations effectively and efficiently. Comparative experiments reveal that the LBDSetNet solution performs significantly better than the previously proposed models on “simple” associations. The double margin cost function also proves its advantage in model training.

Exploring Self-Supervised Graph Learning in Literature-Based Discovery

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