BAYESIAN MODELING OF ANOMALIES DUE TO KNOWN AND UNKNOWN CAUSES

Bayesian modeling of unknown causes of events is an important and pervasive problem. However, it has received relatively little research attention. In general, an intelligent agent (or system) has only limited causal knowledge of the world. Therefore, the agent may well be experiencing the influences of causes outside its model. For example, a clinician may be seeing a patient with a virus that is new to humans; the HIV virus was at one time such an example. It is important that clinicians be able to recognize that a patient is presenting with an unknown disease. In general, intelligent agents (or systems) need to recognize under uncertainty when they are likely to be experiencing influences outside their realm of knowledge. This dissertation investigates Bayesian modeling of unknown causes of events in the context of disease-outbreak detection.The dissertation introduces a Bayesian approach that models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities, (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities and (3) partially-known diseases (e.g., a disease that has characteristics of an influenza-like illness) by using semi-informative prior probabilities. I report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A key contribution of this dissertation is that it introduces a Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has broad applicability in artificial intelligence in general and biomedical informatics applications in particular, where the space of known causes of outcomes of interest is seldom complete

An Expression Tree Decoding Strategy for Mathematical Equation Generation

Generating mathematical equations from natural language requires an accurate understanding of the relations among math expressions. Existing approaches can be broadly categorized into token-level and expression-level generation. The former treats equations as a mathematical language, sequentially generating math tokens. Expression-level methods generate each expression one by one. However, each expression represents a solving step, and there naturally exist parallel or dependent relations between these steps, which are ignored by current sequential methods. Therefore, we integrate tree structure into the expression-level generation and advocate an expression tree decoding strategy. To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others. Besides, a bipartite matching algorithm is adopted to align multiple predictions with annotations for each layer. Experiments show our method outperforms other baselines, especially for these equations with complex structures.Comment: Accepted to EMNLP-2023, camera-ready versio

Proteases of haematophagous arthropod vectors are involved in blood-feeding, yolk formation and immunity : a review

Ticks, triatomines, mosquitoes and sand flies comprise a large number of haematophagous arthropods considered vectors of human infectious diseases. While consuming blood to obtain the nutrients necessary to carry on life functions, these insects can transmit pathogenic microorganisms to the vertebrate host. Among the molecules related to the blood-feeding habit, proteases play an essential role. In this review, we provide a panorama of proteases from arthropod vectors involved in haematophagy, in digestion, in egg development and in immunity. As these molecules act in central biological processes, proteases from haematophagous vectors of infectious diseases may influence vector competence to transmit pathogens to their prey, and thus could be valuable targets for vectorial control

Toll様レセプター2およびMyD88依存的なフォスファチジルイノシトール 3キナーゼとRac1の活性化は、マウスマクロファージによるリステリアの貪食を促進する

京都大学0048新制・課程博士博士(医学)甲第15599号医博第3484号新制||医||983(附属図書館)28126京都大学大学院医学研究科病理系専攻(主査)教授 杉田 昌彦, 教授 一山 智, 教授 湊 長博学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDA

Extracting laboratory test information from biomedical text

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure