The majority of medical documents and electronic health records are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have been developed in recent years to make the text processing automatic. One of the popular approaches to retrieve information based on discovering the themes in health and medical corpora is topic modeling; however, this approach still needs new perspectives. In this research, we describe fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can handle health and medical corpora redundancy issue and provides a new method to estimate the number of topics. The quantitative evaluations show that FLSA produces superior performance and features to latent Dirichlet allocation, the most popular topic model.
LDA has a wide range of health and medical applications such as predicting protein-protein relationships based on the literature knowledge @cite_45 , discovering relevant clinical concepts and structures in patients' health records @cite_0 , identifying patterns of clinical events in a cohort of brain cancer patients @cite_46 , and analyzing time-to-event outcomes @cite_10 . The discovery of clinical pathway (CP) patterns is a method for revealing the structure, semantics, and dynamics of CPs to provide clinicians with explicit knowledge used to guide treatment activities of individual patients. LDA has used for CPs to find treatment behaviors of patients @cite_4 , to predict clinical order patterns, and to model various treatment activities @cite_41 and their occurring time stamps in CPs @cite_19 . LDA has also customized to determine patient mortality @cite_21 , and to discover knowledge from modeling disease and patient characteristics @cite_5 . Redundancy-aware LDA (Red-LDA) is one of the versions of LDA for handling redundancy issue in medical documents and has shown better performance than LDA @cite_51 .
@cite_4 @cite_41 @cite_21 @cite_0 @cite_19 @cite_45 @cite_51 @cite_5 @cite_46 @cite_10
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Clinical pathways leave traces, described as event sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis (CPA), which mainly focus on looking at aggregated data seen from an external perspective. Most existing methods measure similarities between patient traces via computing the relative distance between their event sequences. However, clinical pathways, as typical human-centered processes, always take place in an unstructured fashion, i.e., clinical events occur arbitrarily without a particular order. Bringing order in the chaos of clinical pathways may decline the accuracy of similarity measure between patient traces, and may distort the efficiency of further analysis tasks. In this paper, we present a behavioral topic analysis approach to measure similarities between patient traces. More specifically, a probabilistic graphical model, i.e., latent Dirichlet allocation (LDA), is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method provides a basis for further applications in CPA. In particular, three possible applications are introduced in this paper, i.e., patient trace retrieval, clustering, and anomaly detection. The proposed approach and the presented applications are evaluated via a real-world dataset of several specific clinical pathways collected from a Chinese hospital. Objective Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. @PARASPLIT Materials and Methods The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. @PARASPLIT Results Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16 , and recall 35 . This can be improved to 0.90, 24 , and 47 ( P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). @PARASPLIT Discussion Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. @PARASPLIT Conclusion Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. Clinical reporting is often performed with minimal consideration for secondary computational analysis of concepts. This fact makes the comparison of patients challenging as records lack a representation in a space where their similarity may be judged quantitatively. We present a method by which the entirety of a patient’s clinical records may be compared using latent topics. To capture topics at a clinically relevant level, patient reports are partitioned based on their type, allowing for a more granular characterization of topics. The resulting probabilistic patient topic representations are directly comparable to one another using distance measures. To navigate a collection of patient records we have developed a workstation that allows users to weight different report types and displays succinct summarizations of why two patients are deemed similar, tailoring and expediting searches. Results show the system is able to capture clinically significant topics that can be used for case-based retrieval. Background Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity. This paper investigates applying statistical topic models to extract and predict relationships between biological entities, especially protein mentions. A statistical topic model, Latent Dirichlet Allocation (LDA) is promising; however, it has not been investigated for such a task. In this paper, we apply the state-of-the-art Collapsed Variational Bayesian Inference and Gibbs Sampling inference to estimating the LDA model, and compared them from the viewpoints of log-likelihoods, classification accuracy and retrieval effectiveness. We demonstrate through experiments that the Collapsed Variational LDA gives better results than the other, especially in terms of classification accuracy and retrieval effectiveness in the task of the protein-protein relationship prediction. The clinical notes in a given patient record contain much redundancy, in large part due to clinicians’ documentation habit of copying from previous notes in the record and pasting into a new note. Previous work has shown that this redundancy has a negative impact on the quality of text mining and topic modeling in particular. In this paper we describe a novel variant of Latent Dirichlet Allocation (LDA) topic modeling, Red-LDA, which takes into account the inherent redundancy of patient records when modeling content of clinical notes. To assess the value of Red-LDA, we experiment with three baselines and our novel redundancy-aware topic modeling method: given a large collection of patient records, (i) apply vanilla LDA to all documents in all input records; (ii) identify and remove all redundancy by chosing a single representative document for each record as input to LDA; (iii) identify and remove all redundant paragraphs in each record, leaving partial, non-redundant documents as input to LDA; and (iv) apply Red-LDA to all documents in all input records. Both quantitative evaluation carried out through log-likelihood on held-out data and topic coherence of produced topics and qualitative assessement of topics carried out by physicians show that Red-LDA produces superior models to all three baseline strategies. This research contributes to the emerging field of understanding the characteristics of the electronic health record and how to account for them in the framework of data mining. The code for the two redundancy-elimination baselines and Red-LDA is made publicly available to the community. Display Omitted We present the UPhenome model, which derives phenotypes in an unsupervised manner.UPhenome scales easily to large sets of diseases and clinical observations.The learned phenotypes combine clinical text, ICD9 codes, lab tests, and medications.UPhenome learns phenotypes that can be applied to unseen patient records. We present the Unsupervised Phenome Model (UPhenome), a probabilistic graphical model for large-scale discovery of computational models of disease, or phenotypes. We tackle this challenge through the joint modeling of a large set of diseases and a large set of clinical observations. The observations are drawn directly from heterogeneous patient record data (notes, laboratory tests, medications, and diagnosis codes), and the diseases are modeled in an unsupervised fashion. We apply UPhenome to two qualitatively different mixtures of patients and diseases: records of extremely sick patients in the intensive care unit with constant monitoring, and records of outpatients regularly followed by care providers over multiple years. We demonstrate that the UPhenome model can learn from these different care settings, without any additional adaptation. Our experiments show that (i) the learned phenotypes combine the heterogeneous data types more coherently than baseline LDA-based phenotypes; (ii) they each represent single diseases rather than a mix of diseases more often than the baseline ones; and (iii) when applied to unseen patient records, they are correlated with the patients ground-truth disorders. Code for training, inference, and quantitative evaluation is made available to the research community. Clinical narrative in the medical record provides perhaps the most detailed account of a patient's history. However, this information is documented in free-text, which makes it challenging to analyze. Efforts to index unstructured clinical narrative often focus on identifying predefined concepts from clinical terminologies. Less studied is the problem of analyzing the text as a whole to create temporal indices that capture relationships between learned clinical events. Topic models provide a method for analyzing large corpora of text to discover semantically related clusters of words. This work presents a topic model tailored to the clinical reporting environment that allows for individual patient timelines. Results show the model is able to identify patterns of clinical events in a cohort of brain cancer patients. Two challenging problems in the clinical study of cancer are the characterization of cancer subtypes and the classification of individual patients according to those subtypes. Statistical approaches addressing these problems are hampered by population heterogeneity and challenges inherent in data integration across high-dimensional, diverse covariates. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these concerns. LDA models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a document' with text' constructed from clinical and high-dimensional genomic measurements. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas (TCGA) ovarian project identifies informative patient subgroups that are characterized by different propensities for exhibiting abnormal mRNA expression and methylations, corresponding to differential rates of survival from primary therapy.