Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning

OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. We hypothesized that laboratory test results and other related diagnoses would be associated with mortality in this population. Our another assumption was that a deep learning model could outperform the current Model for End Stage Liver disease (MELD) score in predicting mortality. MATERIALS AND METHODS: We utilized electronic health record data from 34,575 patients with a diagnosis of cirrhosis from a large medical center to study associations with mortality. Three time-windows of mortality (365 days, 180 days and 90 days) and two cases with different number of variables (all 41 available variables and 4 variables in MELD-NA) were studied. Missing values were imputed using multiple imputation for continuous variables and mode for categorical variables. Deep learning and machine learning algorithms, i.e., deep neural networks (DNN), random forest (RF) and logistic regression (LR) were employed to study the associations between baseline features such as laboratory measurements and diagnoses for each time window by 5-fold cross validation method. Metrics such as area under the receiver operating curve (AUC), overall accuracy, sensitivity, and specificity were used to evaluate models. RESULTS: Performance of models comprising all variables outperformed those with 4 MELD-NA variables for all prediction cases and the DNN model outperformed the LR and RF models. For example, the DNN model achieved an AUC of 0.88, 0.86, and 0.85 for 90, 180, and 365-day mortality respectively as compared to the MELD score, which resulted in corresponding AUCs of 0.81, 0.79, and 0.76 for the same instances. The DNN and LR models had a significantly better f1 score compared to MELD at all time points examined. CONCLUSION: Other variables such as alkaline phosphatase, alanine aminotransferase, and hemoglobin were also top informative features besides the 4 MELD-Na variables. Machine learning and deep learning models outperformed the current standard of risk prediction among patients with cirrhosis. Advanced informatics techniques showed promise for risk prediction in patients with cirrhosis

Synthesis of iron based hydrocracking catalysts

Disclosed are method of preparing a fine particle iron based hydrocracking catalyst and the catalyst prepared thereby. An iron (III) oxide powder and elemental sulfur are reacted with a liquid hydrogen donor having a hydroaromatic structure present in the range of from about 5 to about 50 times the weight of iron (III) oxide at 180C to 240C for 0 to 8 hours. Various specific hydrogen donors are disclosed. The catalysts are active at low temperature (<350C) and low pressure

Value of systolic pulmonary arterial pressure as a prognostic factor of death in the systemic sclerosis EUSTAR population.

The aim of this study was to assess the prognostic value of systolic pulmonary artery pressure (sPAP) estimated by echocardiography in the multinational European League Against Rheumatism Scleroderma Trial and Research (EUSTAR) cohort.Data for patients with echocardiography documented between 1 January 2005 and 31 December 2011 were extracted from the EUSTAR database. Stepwise forward multivariable statistical Cox pulmonary hypertension analysis was used to examine the independent effect on survival of selected variables.Based on our selection criteria, 1476 patients were included in the analysis; 87\% of patients were female, with a mean age of 56.3 years (s.d. 13.5) and 31\% had diffuse SSc. The mean duration of follow-up was 2.0 years (s.d. 1.2, median 1.9). Taking index sPAP of 50 mmHg. In a multivariable Cox model, sPAP and the diffusing capacity for carbon monoxide (DLCO) were independently associated with the risk of death [HR 1.833 (95\% CI 1.035, 3.247) and HR 0.973 (95\% CI 0.955, 0.991), respectively]. sPAP was an independent risk factor for death with a HR of 3.02 (95\% CI 1.91, 4.78) for sPAP ≥36 mmHg.An estimated sPAP >36 mmHg at baseline echocardiography was significantly and independently associated with reduced survival, regardless of the presence of pulmonary hypertension based on right heart catheterization

Flow Phase Diagram for the Helium Superfluids

The flow phase diagram for He II and $^3$He-B is established and discussed based on available experimental data and the theory of Volovik [JETP Letters {\bf{78}} (2003) 553]. The effective temperature - dependent but scale - independent Reynolds number $Re_{eff}=1/q=(1+\alpha')/\alpha$, where $\alpha$ and $\alpha'$ are the mutual friction parameters and the superfluid Reynolds number characterizing the circulation of the superfluid component in units of the circulation quantum are used as the dynamic parameters. In particular, the flow diagram allows identification of experimentally observed turbulent states I and II in counterflowing He II with the turbulent regimes suggested by Volovik.Comment: 2 figure

Bringing together coproduction and community participatory research approaches: Using first person reflective narrative to explore coproduction and community involvement in mental health research

Background A growing literature explores the coproduction of research knowledge. Barriers to coproduction in mental health research have been identified, especially for the people from marginalized communities. There is an established body of participatory research that has potential to inform coproduction in mental health research. Objectives To explore and articulate how learning from community participatory approaches to research enable barriers to knowledge coproduction to be overcome in mental health research. Setting An evaluation of a primary care mental health service, led by an experienced survivor researcher, supported by a health service researcher and involving a team of community co‐researchers. Design Cycles of reflective writing (first‐person narrative) by the authors, and feedback from the co‐researcher team, on their experiences of undertaking the evaluation were used to explore the ways in which community actors, including those from marginalized communities, might be meaningfully involved in producing research knowledge about mental health services. Results A space was created where community co‐researchers, including those from traditionally marginalized communities, felt safe and empowered to move beyond essentialized “service user” identities and bring a range of skills and expertise to the evaluation. There was meaningful rebalancing of power between traditional university and community roles, although the issues around leadership remained complex and more could be done to explore how our different experiences of race and mental health shape the research we do. Conclusions Potential was demonstrated for participatory research approaches to inform coproduction of knowledge in mental health research that fully reflects the diversity of identity and experience

Does Treewidth Help in Modal Satisfiability?

Many tractable algorithms for solving the Constraint Satisfaction Problem (CSP) have been developed using the notion of the treewidth of some graph derived from the input CSP instance. In particular, the incidence graph of the CSP instance is one such graph. We introduce the notion of an incidence graph for modal logic formulae in a certain normal form. We investigate the parameterized complexity of modal satisfiability with the modal depth of the formula and the treewidth of the incidence graph as parameters. For various combinations of Euclidean, reflexive, symmetric and transitive models, we show either that modal satisfiability is FPT, or that it is W[1]-hard. In particular, modal satisfiability in general models is FPT, while it is W[1]-hard in transitive models. As might be expected, modal satisfiability in transitive and Euclidean models is FPT.Comment: Full version of the paper appearing in MFCS 2010. Change from v1: improved section 5 to avoid exponential blow-up in formula siz

On the Complexity of Query Result Diversification

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a database D, a query Q and a positive integer k, it is to find a set of k tuples from Q(D) such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets of Q(D) are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval and operations research. This paper studies the complexity of result diversification for relational queries. We identify three problems in connection with query result diversification, to determine whether there exists a set of k tuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a given k-element set, and to count how many k-element sets are ranked above a given bound. We study these problems for a variety of query languages and for three objective functions. We establish the upper and lower bounds of these problems, all matching, for both combined complexity and data complexity. We also investigate several special settings of these problems, identifying tractable cases. 1

Smoking in Systemic Sclerosis: a Longitudinal European Scleroderma Trials and Research Group Study

Data on the role of tobacco exposure in systemic sclerosis (SSc) severity and progression are scarce. We aimed to assess the effects of smoking on the evolution of pulmonary and skin manifestations in the EUSTAR database

The Complexity of Computing Minimal Unidirectional Covering Sets

Given a binary dominance relation on a set of alternatives, a common thread in the social sciences is to identify subsets of alternatives that satisfy certain notions of stability. Examples can be found in areas as diverse as voting theory, game theory, and argumentation theory. Brandt and Fischer [BF08] proved that it is NP-hard to decide whether an alternative is contained in some inclusion-minimal upward or downward covering set. For both problems, we raise this lower bound to the Theta_{2}^{p} level of the polynomial hierarchy and provide a Sigma_{2}^{p} upper bound. Relatedly, we show that a variety of other natural problems regarding minimal or minimum-size covering sets are hard or complete for either of NP, coNP, and Theta_{2}^{p}. An important consequence of our results is that neither minimal upward nor minimal downward covering sets (even when guaranteed to exist) can be computed in polynomial time unless P=NP. This sharply contrasts with Brandt and Fischer's result that minimal bidirectional covering sets (i.e., sets that are both minimal upward and minimal downward covering sets) are polynomial-time computable.Comment: 27 pages, 7 figure