Nonconfidential Patient Types in Emergency Clinical Decision Support

Chignell, Mark; Rouzbahman, Mahsa; Kealey, Ryan; Samavi, Reza; Yu, Erin; Sieminowski, Tammy

doi:10.1109/msp.2013.103

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…K-means cluster analysis was carried out, varying number of partitions between 2 and 50 and with the use of different random input conditions to provide more stable estimates of mean accuracy. The same method was used previously in other studies of ICU data (Chignell, Rouzbahman, Kealey, Samavi, & Sieminowski, 2013) (Rouzbahman, Jovicic, & Chignell, 2016).…”

Section: Discussionmentioning

confidence: 99%

Predicting Emergency Department Visits Based on Cancer Patient Types

Rouzbahman

Wang

Chignell

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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K MDI RE P OR T S Pr edi c ng Emer g enc y Depar t ment Vi s i t s Bas ed on Canc er Pa ent T ypes Aut hor s : Ma hs a Rouz ba hma n, Ma r k Chi g nel l &L i s a Ba r ber a AbstractThis study evaluated the predictive ability of patient types (clusters of similar patients) in identifying cancer patients at high risk for ED visits within one year (365 days) following their index date. A descriptive and retrospective cohort study of 254,552 unique cancer patients with only one primary cancer type was done using linked administrative sources of health care data. Three outcomes were investigated in this study. First, the time of ED visit following an index date was predicted using multiple linear regression. Second, those patients who visited an ED within seven days of their index date were detected using logistic regression. In addition to predicting emergency department visit, vital status of patients was also predicted using logistic regression. We implemented the linear/logistic regression once on unclustered raw data. Then cluster analysis was done before the prediction step and the results of these two analyses were compared to each other. Clustering was found to contribute to a modest improvement in prediction accuracy for all three outcome variables. In addition, for the first outcome variable, a privacy preserving analysis was carried out using summarized clustered data (mean, standard deviation and correlation). The results are discussed in terms of the ability of summarized patient type data to provide clinical support tools while also respecting the privacy of patients.

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Section: Discussionmentioning

confidence: 99%

Predicting Emergency Department Visits Based on Cancer Patient Types

Rouzbahman

Wang

Chignell

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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“…The details of the original cluster-boosted regression framework can be found in Rouzbahman [36] and a similar method was used for creating the numerical features used, along with the textual features described above, in the analyses reported below. For the numerical variables that were not derived from PCA, we first standardized the variables using a standard normal distribution (z-scores), to ensure that clustering results were not influenced by differences in scale [2]. A brief description of the clustering prior to regression is as follows.…”

Section: Cluster-boosted Regressionmentioning

confidence: 99%

“…Although physician's decisions are influenced by previous cases they have seen [1], performing diagnosis and treatment physicians generally rely on electronically stored information [2]. Ideally, Electronic Health Records (EHRs) should be shared across many health-care providers and organizations, so that the EHR database or repository can support several care-related activities, enabling caregivers to make better decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Data mining plays an important role in big data analytics of EHR repositories. Chignell et al [2] addressed the question: how can summarized de-identified data support clinical decision-making while preserving privacy constraints. Researchers reduced individuality in original identifiable attributes by grouping individuals into types.…”

Section: Introductionmentioning

confidence: 99%

“…However, the previously described methods focused on numerical data only. The use of textual data is seen as being problematic, since certain textual data, such as physician's notes, can pose privacy issues [2]. In addition, textual data cannot be directly used for statistical analyses, unless converted into numerical form.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Predictive Power of Cluster-Boosted Regression With Text-Based Indexing

et al. 2019

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Clustering prior to regression analysis improves the accuracy of prediction in clinical decision making. However, most previously described methods focused on numerical data only. This paper investigated how well textual features can improve the accuracy of regression predictions. Preliminary diagnosis, diagnosis summary, and drug names used in prescriptions as provided in the MIMIC II dataset were used to derive textual features. We proposed the bag-of-entities indexing method, which relies on named entity recognition, a machine learning technique used for locating and identifying words into predefined classes. The proposed technique captured meaningful phrases from texts in health records and represented them in numerical vector format. Dimensionality of the data space was reduced using principal component analysis. The additional well-tuned textual features were then combined with existing numerical features in using cluster-boosted regression to predict patient mortality in ICU. The experimental results showed prediction improvement obtained from textual features over the use of numerical features only. We found that using the proposed indexing method outperformed traditional word-vector representation approaches (bag-of-words and bag-of-bigrams) as well as a state-of-the-art approach (Doc2vec) in terms of resulting accuracy in predicting death status. Moreover, instead of directly interpreting, the identifiable individual features were grouped into types and summarized. The summarized de-identified data of textual features handled by the proposed framework can support predictive classification while also reducing privacy concerns. Grouping of similar patients based on their electronic health records also benefits physicians through the improved differential diagnosis and effective treatment planning. INDEX TERMS Bag-of-entities, clustering, electronic health record, named entity recognition, regression analysis, summarized data.

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Graña

Echaniz

2016

Innovation in Medicine and Healthcare 2016

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Nonconfidential Patient Types in Emergency Clinical Decision Support

Cited by 10 publications

References 12 publications

Predicting Emergency Department Visits Based on Cancer Patient Types

Predicting Emergency Department Visits Based on Cancer Patient Types

Enhancing Predictive Power of Cluster-Boosted Regression With Text-Based Indexing

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