Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Hospital fall prevention: a systematic review of implementation, components, adherence, and effectiveness.

ObjectivesTo systematically document the implementation, components, comparators, adherence, and effectiveness of published fall prevention approaches in U.S. acute care hospitals.DesignSystematic review. Studies were identified through existing reviews, searching five electronic databases, screening reference lists, and contacting topic experts for studies published through August 2011.SettingU.S. acute care hospitals.ParticipantsStudies reporting in-hospital falls for intervention groups and concurrent (e.g., controlled trials) or historic comparators (e.g., before-after studies).InterventionFall prevention interventions.MeasurementsIncidence rate ratios (IRR, ratio of fall rate postintervention or treatment group to the fall rate preintervention or control group) and ratings of study details.ResultsFifty-nine studies met inclusion criteria. Implementation strategies were sparsely documented (17% not at all) and included staff education, establishing committees, seeking leadership support, and occasionally continuous quality improvement techniques. Most interventions (81%) included multiple components (e.g., risk assessments (often not validated), visual risk alerts, patient education, care rounds, bed-exit alarms, and postfall evaluations). Fifty-four percent did not report on fall prevention measures applied in the comparison group, and 39% neither reported fidelity data nor described adherence strategies such as regular audits and feedback to ensure completion of care processes. Only 45% of concurrent and 15% of historic control studies reported sufficient data to compare fall rates. The pooled postintervention incidence rate ratio (IRR) was 0.77 (95% confidence interval = 0.52-1.12, P = .17; eight studies; I(2) : 94%). Meta-regressions showed no systematic association between implementation intensity, intervention complexity, comparator information, or adherence levels and IRR.ConclusionPromising approaches exist, but better reporting of outcomes, implementation, adherence, intervention components, and comparison group information is necessary to establish evidence on how hospitals can successfully prevent falls

Process mining in healthcare : opportunities beyond the ordinary

Nowadays, in a Hospital Information System (HIS) huge amounts of data are stored about the care processes as they unfold. This data can be used for process mining. This way we can analyse the operational processes within a hospital based on facts rather than fiction. In order to enhance the uptake of process mining within the healthcare domain we present a healthcare reference model which exhaustively lists the typical types of data that exists within a HIS and that can be used for process mining. Based on this reference model, we elaborate on the most interesting kinds of process mining analyses that can be performed in order to illustrate the potential of process mining. As such, a basis is provided for governing and improving the processes within a hospital. Keywords: healthcare, process mining, reference mode

Extractive Summarization : Experimental work on nursing notes in Finnish

Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that is concerned with how a computer machine interacts with human language. With the increasing computational power and the advancement in technologies, researchers have been successful at proposing various NLP tasks that have already been implemented as real-world applications today. Automated text summarization is one of the many tasks that has not yet completely matured particularly in health sector. A success in this task would enable healthcare professionals to grasp patient's history in a minimal time resulting in faster decisions required for better care. Automatic text summarization is a process that helps shortening a large text without sacrificing important information. This could be achieved by paraphrasing the content known as the abstractive method or by concatenating relevant extracted sentences namely the extractive method. In general, this process requires the conversion of text into numerical form and then a method is executed to identify and extract relevant text. This thesis is an attempt of exploring NLP techniques used in extractive text summarization particularly in health domain. The work includes a comparison of basic summarizing models implemented on a corpus of patient notes written by nurses in Finnish language. Concepts and research studies required to understand the implementation have been documented along with the description of the code. A python-based project is structured to build a corpus and execute multiple summarizing models. For this thesis, we observe the performance of two textual embeddings namely Term Frequency - Inverse Document Frequency (TF-IDF) which is based on simple statistical measure and Word2Vec which is based on neural networks. For both models, LexRank, an unsupervised stochastic graph-based sentence scoring algorithm, is used for sentence extraction and a random selection method is used as a baseline method for evaluation. To evaluate and compare the performance of models, summaries of 15 patient care episodes of each model were provided to two human beings for manual evaluations. According to the results of the small sample dataset, we observe that both evaluators seem to agree with each other in preferring summaries produced by Word2Vec LexRank over the summaries generated by TF-IDF LexRank. Both models have also been observed, by both evaluators, to perform better than the baseline model of random selection

A thematic analysis of the prevention of future deaths reports in healthcare from HM coroners in England and Wales 2016–2019

Background The Coroners and Justice Act allows coroners in England or Wales to issue reports after inquest, if they believe that action should be taken to prevent a future death. Coroners are under a statutory duty to issue a Prevention of Future Death (PFD) report to persons or organisations that they believe have the power to act. Cumulatively, these reports may contain useful intelligence for patient safety. The aim of this study was to examine the feasibility of extracting data from these reports and to evaluate if learning was possible from any common themes. Methods Reports were extracted from 2016 to 2019 for deaths in hospitals, care homes and the community in England and Wales. These were subjected to descriptive statistics and thematic analysis of coroner’s concerns. Application of data mining techniques was not possible due to data quality. Results 710 reports were examined, with 3469 concerns being raised (mean 4.88, range 1–33). 36 reports expressed concern about having to issue repeat PFDs to the same organisation for the same or similar concerns. Thematic analysis reliability was high ( κ 0.89 unweighted) with five emerging primary themes: deficit in skill or knowledge, missed, delayed or uncoordinated care, communication and cultural issues, systems issues and lack of resources. A codebook of 53 subthemes were identified. Conclusions PFD reports offer valuable insight. Aggregation and continued analysis of these reports could offer more informed patient safety, workforce development and organisational policy. Improved data quality would allow for possible automation of analysis and faster feedback into practice

Capacity of English NHS hospitals to monitor quality in infection prevention and control using a new European framework: a multilevel qualitative analysis

Objective:(1) To assess the extent to which current English national regulations/policies/guidelines and local hospital practices align with indicators suggested by a European review of effective strategies for infection prevention and control (IPC); (2) to examine the capacity of local hospitals to report on the indicators and current use of data to inform IPC management and practice. Design A national and local-level analysis of the 27 indicators was conducted. At the national level, documentary review of regulations/policies/guidelines was conducted. At the local level data collection comprised: (a) review of documentary sources from 14 hospitals, to determine the capacity to report performance against these indicators; (b) qualitative interviews with 3 senior managers from 5 hospitals and direct observation of hospital wards to find out if these indicators are used to improve IPC management and practice. Setting 2 acute English National Health Service (NHS) trusts and 1 NHS foundation trust (14 hospitals). Participants 3 senior managers from 5 hospitals for qualitative interviews. Primary and secondary outcome measures As primary outcome measures, a ‘Red-Amber-Green’ (RAG) rating was developed reflecting how well the indicators were included in national documents or their availability at the local organisational level. The current use of the indicators to inform IPC management and practice was also assessed. The main secondary outcome measure is any inconsistency between national and local RAG rating results. Results National regulations/policies/guidelines largely cover the suggested European indicators. The ability of individual hospitals to report some of the indicators at ward level varies across staff groups, which may mask required improvements. A reactive use of staffing-related indicators was observed rather than the suggested prospective strategic approach for IPC management. Conclusions For effective patient safety and infection prevention in English hospitals, routine and proactive approaches need to be developed. Our approach to evaluation can be extended to other country settings