Comparative Study of Variations in Quantum Approximate Optimization Algorithms for the Traveling Salesman Problem.

The traveling salesman problem (TSP) is one of the most often-used NP-hard problems in computer science to study the effectiveness of computing models and hardware platforms. In this regard, it is also heavily used as a vehicle to study the feasibility of the quantum computing paradigm for this class of problems. In this paper, we tackle the TSP using the quantum approximate optimization algorithm (QAOA) approach by formulating it as an optimization problem.

Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients.

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.

Framework for Mining and Analysis of Standardized Nursing Care Plan Data.

Despite an unprecedented amount of health-related data being amassed from various technological innovations, our ability to process this data and extract hidden knowledge has yet to catch up with this explosive growth. Although nursing care plans can be an effective tool to support the achievement of desired patient outcomes, their online collection, storage, and processing is lagging far behind. As a result, the impact of nursing care is not well understood from qualitative as well as quantitative perspectives.

Predictive Modeling for End-of-Life Pain Outcome using Electronic Health Records.

Electronic health record (EHR) systems are being widely used in the healthcare industry nowadays, mostly for monitoring the progress of the patients. EHR data analysis has become a big data problem as data is growing rapidly. Using a nursing EHR system, we built predictive models for determining what factors influence pain in end-of-life (EOL) patients.

Nurse Continuity and Hospital-Acquired Pressure Ulcers: A Comparative Analysis Using an Electronic Health Record "Big Data" Set.

Background: Little research demonstrating the association between nurse continuity and patient outcomes exists despite an intuitive belief that continuity makes a difference in care outcomes.

Objective: The aim of this study was to examine the association of nurse continuity with the prevention of hospital-acquired pressure ulcers (HAPU).

Methods: A secondary use of data from the Hands on Automated Nursing Data System (HANDS) was performed for this comparative study.

Proposing a New Conceptual Model and an Exemplar Measure Using Health Information: Technology to Examine the Impact of Relational Nurse Continuity on Hospital-Acquired Pressure Ulcers.

The influence of the staffing variable relational nurse continuity on patient outcomes has been rarely studied and with inconclusive results. Multiple definitions and an absence of systematic methods for measuring the influence of continuity have resulted in its exclusion from nurse-staffing studies and conceptual models. We present a new conceptual model and an innovative use of health information technology to measure relational nurse continuity and to demonstrate the potential for bringing the results of big data science back to the bedside.

Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.

Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons.

Infomarkers for transition to goals consistent with palliative care in dying patients.

Objective: Electronic health records (EHRs) may contain infomarkers that identify patients near the end of life for whom it would be appropriate to shift care goals to palliative care. Discovery and use of such infomarkers could be used to conduct effectiveness research that ultimately could help to reduce the monumental cost of caring for the dying. The aim of our study was to identify changes in the plans of care that represent infomarkers, which signal a transition of care goals from nonpalliative care ones to those consistent with palliative care.

Death anxiety in hospitalized end-of-life patients as captured from a structured electronic health record: differences by patient and nurse characteristics.

The nursing outcomes of hospitalized patients whose plans of care include death anxiety, which is a diagnosis among patients at the end-of-life, are obscure. The authors of the current article applied data mining techniques to nursing plan-of-care data for patients diagnosed with death anxiety, as defined by North American Nursing Diagnosis Association International, from four different hospitals to examine nursing care outcomes and associated factors. Results indicate that <50% of patients met the expected outcome of comfortable death.

Data mining nursing care plans of end-of-life patients: a study to improve healthcare decision making.

Purpose: To reveal hidden patterns and knowledge present in nursing care information documented with standardized nursing terminologies on end-of-life (EOL) hospitalized patients.

Method: 596 episodes of care that included pain as a problem on a patient's care plan were examined using statistical and data mining tools. The data were extracted from the Hands-On Automated Nursing Data System database of nursing care plan episodes (n = 40,747) coded with NANDA-I, Nursing Outcomes Classification, and Nursing Intervention Classification (NNN) terminologies.

High Performance Multiple Sequence Alignment System for Pyrosequencing Reads from Multiple Reference Genomes.

Genome resequencing with short reads generated from pyrosequencing generally relies on mapping the short reads against a single reference genome. However, mapping of reads from multiple reference genomes is not possible using a pairwise mapping algorithm. In order to align the reads w.

Combinatorial reconstruction of half-sibling groups from microsatellite data.

While full-sibling group reconstruction from microsatellite data is a well-studied problem, reconstruction of half-sibling groups is much less studied, theoretically challenging, and computationally demanding. In this paper, we present a formulation of the half-sibling reconstruction problem and prove its APX-hardness. We also present exact solutions for this formulation and develop heuristics.

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework.

Object trajectory-based activity classification and recognition using hidden Markov models.

Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents novel classification algorithms for recognizing object activity using object motion trajectory. In the proposed classification system, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients.