Enhancing Clinical Decision Support Systems with Public Knowledge Bases

With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would help doctors if an effective clinical decision support system could generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the useful-ness of patients’ diagnosis information in such scenario, but diagnosis is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy pa-tient medical records. In this paper, we propose automatic diagnosis prediction meth-ods for enhancing the retrieval in a clinical decision support system, where the predic-tion is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods perfor-mance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015 and 2016. The results show that our best method can au-tomatically predict diagnosis with about 65.56% usefulness, and such predictions can significantly improve the biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors

Rolling element bearing weak fault diagnosis based on spatial correlation and ALIFD

Vibration signals of rolling element bearings during operation are always very complex, random strongly and broadband. Adaptive Local Iterative Filtering Decomposition (ALIFD) can overcome the smoothness and adaptive flaws of Iterative Filtering Decomposition (IFD), but it is so susceptible to random noise that it’s less effective. Here, spatial correlation was proposed. Firstly, the signal was denoised by spatial correlation and decomposed into several modes by ALIFD. Finally, the envelope demodulation was analyzed to extract fault feature. The simulating signal analysis and bearing fault simulator show that this method can be available for separating different frequencies of bearing fault vibration signals

Data Reliability Assessment based on subjective opinions

In the big data era, numerous data fluctuates society and people's life. These data come from diverse sources, and various information can be inferred and extracted. However, data quality usually cannot be guaranteed, and hence decision making with such unreliable data may lead to considerable losses. Accurate data reliability assessment mechanisms can help recognize the distrustful information and then filter unreliable data out. In this work, I consider a novel approach to assess data reliability based on subjective opinions. I structure the data propagation model in terms of data sources producing and evaluating different statements. Next, I explore data history labels, value conflicts, and uncertainty. For different combinations of those parameters, I consider common scenarios, including handling fake news, truth discovery, data cleaning, as well as discovering cancer-driving genes. In my dissertation, I explore how to accurately assess data reliability and how to make a decision based on evaluated reliability. I propose a series of subjective opinion based models to assess each scenario's reliability and compare them with state-of-art models through experiments on real-world data

Wikipedia-Based Automatic Diagnosis Prediction in Clinical Decision Support Systems

When making clinical decisions, physicians often consult biomedical literatures for reference. In this case, an effective clinical decision support system, provided with a patient’s health information, should be able to generate accurate queries and return to the physicians with useful articles. Related works in the Clinical Decision Support (CDS) track of TREC 2015 demonstrated the usefulness of knowing patients’ diagnosis information for supporting more effective retrieval, but the diagnosis information is often missing in most cases. Furthermore, it is still a great challenge to perform large-scale automatic diagnosis prediction. This motivates us to propose an automatic diagnosis prediction method to enhance the retrieval in a clinical decision support system, where the evidence for the prediction is extracted from Wikipedia. Through the evaluation conducted on 2014 CDS tasks, our method reaches the best performance among all submitted runs. In the next step, graph structured evidence will be integrated to make the prediction more accurate

SLFTD: a subjective logic based framework for truth discovery

Finding truth from various conflicting candidate values provided by different data sources is called truth discovery, which is of vital importance in data integration. Several algorithms have been proposed in this area, which usually have similar procedure: iterativly inferring the truth and provider's reliability on providing truth until converge. Therefore, an accurate provider's reliability evaluation is essential. However, no work pays attention to ``how reliable this provider continuously providing truth". Therefore, we introduce subjective logic, which can records both (1) the provider's reliability of generating truth, and (2) reliability of provider continuously doing so. Our proposed methods provides a better evaluation for data providers, and based which, truth are discovered more accurately. Our framework can handle both categorical and numerical data, and can identify truth in either a generative or discriminative way. Experiments on two popular real world datasets, Book and Population, validates that our proposed subjective logic based framework can discover truth much more accurately than state-of-art methods

Can Word Embedding Help Term Mismatch Problem?–A Result Analysis on Clinical Retrieval Tasks

Clinical Decision Support (CDS) systems assist doctors to make clinical decisions by searching for medical literature based on patients’ medical records. Past studies showed that correctly predicting patient’s diagnosis can significantly increase the performance of such clinical retrieval systems. However, our studies showed that there are still a large portion of relevant documents ranked very low due to term mismatch problem. Different to other retrieval tasks, queries issued to this clinical retrieval system have already been expanded with the most informative terms for disease prediction. It is therefore a great challenge for traditional Pseudo Relevance Feedback (PRF) methods to incorporate new informative terms from top K pseudo relevant documents. Consequently, we explore in this paper word embedding for obtaining further improvements because the word vectors were all trained on much larger collections and they can identify words that are used in similar contexts. Our study utilized test collections from the CDS track in TREC 2015, trained on 2014 data. Experiment results show that word embedding can significantly improve retrieval performance, and term mismatch problem can be largely resolved, particularly for the low ranked relevant documents. However, for highly ranked documents with less term mismatching problem, word emending’s improvement can also be replaced by a traditional language model

Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed

Assigning a standard ICD-9-CM code to disease symptoms in medical texts is an important task in the medical domain. Automating this process could greatly reduce the costs. However, the effectiveness of an automatic ICD-9-CM code classifier faces a serious problem, which can be triggered by unbalanced training data. Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease. However, a disease’s frequency does not necessarily reflect its importance. To resolve this training data shortage problem, we propose to strategically draw data from PubMed to enrich the training data when there is such need. We validate our method on the CMC dataset, and the evaluation results indicate that our method can significantly improve the code assignment classifiers' performance at the macro-averaging level

Fault feature extraction for rolling element bearings based on multi-scale morphological filter and frequency-weighted energy operator

In order to extract impulse components from bearing vibration signals with strong background noise, a fault feature extraction method based on multi-scale average combination difference morphological filter and Frequency-Weighted Energy Operator is proposed in this paper. The average combination difference morphological filter (ACDIF) is used to enhance the positive and negative impulse components in the signal. The double-dot structure element (SE) is used instead of zero amplitude flat SE to improve the effectiveness of fault feature extraction. The weight coefficients of the filtered results at different scales in multi-scale ACDIF are adaptively determined by an optimization algorithm called hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC). At last, as the Frequency-Weighted Energy Operator (FWEO) outperforms the enveloping method in detecting impulse components of signals, the filtered signal is processed by FWEO to extract the fault features of bearings. Results on simulation and experimental bearing vibration signals show that the proposed method can effectively suppress noise and extract the fault features from bearing vibration signals