Time Series Analysis of Nursing Notes for Mortality Prediction via a State Transition Topic Model

Jo, Yohan; Loghmanpour, Natasha A.; Rosé, Carolyn Penstein

doi:10.1145/2806416.2806541

Cited by 16 publications

(18 citation statements)

References 18 publications

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“…After assessing the remaining 197 articles, most studies (189 of 197, ie, 96%) were excluded because they had not used or attempted to use unstructured clinical text in their ML models to identify, detect, or predict sepsis onset. For instance, there were sepsis-related studies that used text but for other purposes such as mortality prediction, 61–65 phenotyping, 66 visualization, 67 exploratory data analysis, 68 and manual chart review. 69–71 Additionally, 6 articles about infection detection, 60 central venous catheter adverse events, 58 postoperative sepsis adverse events, 72–74 and septic shock identification 75 were excluded because they used manually human-curated rules instead of ML methods that automatically learn from data.…”

Section: Resultsmentioning

confidence: 99%

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Yan

Gustad

Nytrø

2021

Journal of the American Medical Informatics Association

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Objective To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis. Materials and methods PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted. Results The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies. Discussion Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units. Conclusions Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.

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Section: Resultsmentioning

confidence: 99%

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Yan

Gustad

Nytrø

2021

Journal of the American Medical Informatics Association

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“…Then, the reports are classified into critical or non-critical categories which help physicians to identify high priority reports that need urgent treatment. [7] proposed a mortality prediction for the patients in the intensive care units in order to make a most appropriate decision. The authors believe that the nursing notes within a recent time period can identify hidden clues about the physical condition of a patient, which is useful to decide the priority of handling matters.…”

Section: Related Workmentioning

confidence: 99%

“…In this part of experiment, the data set consists of the paragraph (2), paragraph (6), and paragraph (7) in the reports, which describe the special examination items. The terms of the examination items in a structured form of the report, which are listed by a medical expert, are used as the correct…”

Section: Evaluation On Keyword Extraction Of Examination Items 431 mentioning

confidence: 99%

Keyword extraction and structuralization of medical reports

Tsai

et al. 2020

Health Inf Sci Syst

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“…This approach may find more representative units of student interests than predefined building blocks. For this purpose, we propose an extension of the previously published State Transition Topic Model (STTM) [1], in order to infer learning paths from student behavior traces in a course. STTM is a combination of a Hidden Markov Model and Latent Dirichlet Allocation, where each state is represented as a topic distribution.…”

Section: Learning Analytics-sequence Modelmentioning

confidence: 99%

Pipeline for expediting learning analytics and student support from data in social learning

Tomar

Ferschke

et al. 2016

Proceedings of the Sixth International Conference on Learning Analytics &Amp; Knowledge

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An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student progress and the improvement of student support. For this goal in the context of social learning, we propose a pipeline that includes data infrastructure, learning analytics, and intervention, along with computational models for individual components. Next, we describe an example of applying this pipeline to real data in a case study, whose goal is to investigate the positive effects that goal-setting students have on their peers, which suggests ways in which we might foster these social benefits through intervention.

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Time Series Analysis of Nursing Notes for Mortality Prediction via a State Transition Topic Model

Cited by 16 publications

References 18 publications

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review

Keyword extraction and structuralization of medical reports

Pipeline for expediting learning analytics and student support from data in social learning

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