scholarly journals Unstructured Text in EMR Improves Prediction of Death after Surgery in Children

Informatics ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 4 ◽  
Author(s):  
Oguz Akbilgic ◽  
Ramin Homayouni ◽  
Kevin Heinrich ◽  
Max Langham ◽  
Robert Davis
Keyword(s):  
Risk Score ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Structured Data ◽  
Risk Scores ◽  
Free Text ◽  
Significant Information ◽  
C Statistic ◽  
Surgery Outcomes ◽  
Fold Cross Validation

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

scholarly journals Unstructured Text in EMR Improves Prediction of Death after Surgery in Children

2018 ◽  
Author(s):  
Oguz Akbilgic ◽  
Ramin Homayouni ◽  
Kevin Heinrich ◽  
Max Raymond langham, Jr ◽  
Robert Lowell Davis
Keyword(s):  
Risk Score ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Structured Data ◽  
Risk Scores ◽  
Free Text ◽  
Significant Information ◽  
C Statistic ◽  
Surgery Outcomes ◽  
Fold Cross Validation

Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6,497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain the text-based risk score algorithm as predictive of death within 30 days of surgery. We studied the additional performance obtained by including text-based risk score as a predictor of death along with other structured data based clinical risk factors. The C-statistic of a logistic regression model with 5-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes.

scholarly journals Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Qi ◽  
Qian Yan ◽  
Ming Lv ◽  
Delei Song ◽  
Xianbin Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Decision ◽  
Cell Types ◽  
Survival Outcome ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis.Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

scholarly journals CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Tiansong Xie ◽  
Xuanyi Wang ◽  
Zehua Zhang ◽  
Zhengrong Zhou
Keyword(s):  
Predictive Value ◽  
Cross Validation ◽  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Cystic Neoplasm ◽  
Cystic Neoplasms ◽  
Serous Cystadenomas ◽  
Radiological Model ◽  
Fold Cross Validation

ObjectivesTo investigate the value of CT-based radiomics analysis in preoperatively discriminating pancreatic mucinous cystic neoplasms (MCN) and atypical serous cystadenomas (ASCN).MethodsA total of 103 MCN and 113 ASCN patients who underwent surgery were retrospectively enrolled. A total of 764 radiomics features were extracted from preoperative CT images. The optimal features were selected by Mann-Whitney U test and minimum redundancy and maximum relevance method. The radiomics score (Rad-score) was then built using random forest algorithm. Radiological/clinical features were also assessed for each patient. Multivariable logistic regression was used to construct a radiological model. The performance of the Rad-score and the radiological model was evaluated using 10-fold cross-validation for area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.ResultsTen screened optimal features were identified and the Rad-score was then built based on them. The radiological model was built based on four radiological/clinical factors. In the 10-fold cross-validation, the Rad-score was proved to be robust and reliable (average AUC: 0.784, sensitivity: 0.847, specificity: 0.745, PPV: 0.767, NPV: 0.849, accuracy: 0.793). The radiological model performed slightly less well in classification (average AUC: average AUC: 0.734 sensitivity: 0.748, specificity: 0.705, PPV: 0.732, NPV: 0.798, accuracy: 0.728.ConclusionsThe CT-based radiomics analysis provided promising performance for preoperatively discriminating MCN from ASCN and showed good potential in improving diagnostic power, which may serve as a novel tool for guiding clinical decision-making for these patients.

P1858Meta-analysis of performance of risks scores for predicting outcomes after percutaneous Mitraclip implantation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
T K M Wang ◽  
M T M Wang
Keyword(s):  
Confidence Interval ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Operative Mortality ◽  
Data Extraction ◽  
Meta Analysis ◽  
Critical Role ◽  
Clinical Decision ◽  
Risk Scores ◽  
Sts Score

Abstract Background The Mitraclip is the most established percutaneous mitral valve intervention indicated for severe mitral regurgitation at high or prohibitive surgical risk. Risk stratification plays a critical role in selecting the appropriate treatment modality in high risk valve disease patients but have been rarely studied in this setting. We compared the performance of risk scores at predicting mortality after Mitraclip in this meta-analysis. Methods MEDLINE, Embase and Cochrane databases from 1 January 1980 to 31 December 2018 were searched. Two authors reviewed studies which reported c-statistics of risk models to predict mortality after Mitraclip for inclusion, followed by data extraction and pooled analyses. Results Amongst 494 articles searched, 47 full-text articles were evaluated, and 4 studies totalling 879 Mitraclip cases were included for analyses. Operative mortality was reported at 3.3–7.4% in three studies, and 1-year mortality 11.2%-13.5% in two studies. C-statistics (95% confidence interval) for operative mortality were EuroSCORE 0.66 (0.57–0.75), EuroSCORE II 0.72 (0.60–0.85) and STS Score 0.64 (0.56–0.73). Respective Peto's odds ratios (95% confidence interval) to assess calibration were EuroSCORE 0.21 (0.14–0.31), EuroSCORE II 0.43 (0.24–0.76) and STS Score 0.36 (0.21–0.61). C-statistics (95% confidence interval) for 1-year mortality were EuroSCORE II 0.64 (0.57–0.70) and STS Score (0.58–0.64). Conclusion All scores over-estimated operative mortality, and EuroSCORE II had the best moderate discrimination while the other two scores were only modestly prognostic. Development of Mitraclip-specific scores may improve accuracy of risk stratification for this procedure to guide clinical decision-making.

scholarly journals SP2.2.9Using high fidelity simulation to improve hospital-based medical student education

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Marc Huttman ◽  
Hui Fen Koo ◽  
Charlotte Boardman ◽  
Michael Saunders
Keyword(s):  
Medical Students ◽  
Clinical Decision Making ◽  
Medical Student Education ◽  
District General Hospital ◽  
Clinical Decision ◽  
Free Text ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Qualitative Feedback ◽  
Before And After

Abstract Introduction The evidence shows that experiential learning has multiple benefits in preparing medical students for foundation training. An immersive ‘on call simulation’ session was designed for final-year medical students at a district general hospital. The aim of this project was to assess how beneficial the sessions were and how they can be improved. Methods Pairs of students received 12 bleeps over 2 hours directing them to wards where mock patient folders were placed. Students prioritised bleeps involving deteriorating patients, chasing results and dealing with nursing queries. Simulated senior input was available from the session facilitator. A structured debrief session allowed discussion of each case. Quantitative feedback was gathered using a sliding scale (measured in percentage) for confidence before and after the session. Qualitative feedback was gathered using a free-text box. Results Four sessions were held between October 2020 and January 2021 for a total of 28 students, of which 26 provided feedback. Average confidence increased from 38% to 66%. 96% of students were ‘extremely satisfied’ with the session. Feedback included: “Incredibly immersive and fun” and “I was made to think through my priorities and decisions”. Improvements could be made by using actors/mannequins to simulate unwell patients and by use of skills models. Conclusion High fidelity simulation training is valuable and should be considered a standard part of the student curriculum. It is particularly suited to final year students who have the required clinical knowledge for foundation training but are still developing confidence in clinical decision making and prioritisation.

scholarly journals DICE: A Drug Indication Classification and Encyclopedia for AI-Based Indication Extraction

2021 ◽  
Vol 4 ◽  
Author(s):  
Arjun Bhatt ◽  
Ruth Roberts ◽  
Xi Chen ◽  
Ting Li ◽  
Skylar Connor ◽  
...  
Keyword(s):  
Language Processing ◽  
Clinical Decision Making ◽  
Short Term Memory ◽  
Drug Repositioning ◽  
External Validation ◽  
Clinical Decision ◽  
Language Models ◽  
Free Text ◽  
Drug Labeling ◽  
Real World Evidence

Drug labeling contains an ‘INDICATIONS AND USAGE’ that provides vital information to support clinical decision making and regulatory management. Effective extraction of drug indication information from free-text based resources could facilitate drug repositioning projects and help collect real-world evidence in support of secondary use of approved medicines. To enable AI-powered language models for the extraction of drug indication information, we used manual reading and curation to develop a Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling. A DICE scheme with 7,231 sentences categorized into five classes (indications, contradictions, side effects, usage instructions, and clinical observations) was developed. To further elucidate the utility of the DICE, we developed nine different AI-based classifiers for the prediction of indications based on the developed DICE to comprehensively assess their performance. We found that the transformer-based language models yielded an average MCC of 0.887, outperforming the word embedding-based Bidirectional long short-term memory (BiLSTM) models (0.862) with a 2.82% improvement on the test set. The best classifiers were also used to extract drug indication information in DrugBank and achieved a high enrichment rate (>0.930) for this task. We found that domain-specific training could provide more explainable models without performance sacrifices and better generalization for external validation datasets. Altogether, the proposed DICE could be a standard resource for the development and evaluation of task-specific AI-powered, natural language processing (NLP) models.

scholarly journals Exploring Interval Graphs of Rare Diseases in Retrospective Analysis of Outpatient Records

10.29007/l7v8 ◽  
2018 ◽  
Author(s):  
Svetla Boytcheva
Keyword(s):  
Pattern Mining ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Interval Graphs ◽  
Insurance Fund ◽  
Free Text ◽  
Rare Disorders ◽  
Patient History ◽  
Structured Information ◽  
Data Collections

This paper deals with investigation of complex temporal relations between some rare disorders. It proposes an interval graphs approach combined with data mining for patient history pattern mining. The processed data are enriched with context information. Some text mining tools extract entities from free text and deliver additional attributes beyond the structured information about the patients. The test corpora contain pseudonymised reimbursement requests submitted to the Bulgarian National Health Insurance Fund in 2010-2015 for more than 5 million citizens yearly. Experiments were run on 2 data collections. Findings in these two collections are discussed on the basis of comparison between patients with and without rare disorders. Exploration of complex relations in rare-disease data can support analyzes of small size patient pools and assist clinical decision making.

scholarly journals Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248477
Author(s):  
Khushal Arjan ◽  
Lui G. Forni ◽  
Richard M. Venn ◽  
David Hunt ◽  
Luke Eliot Hodgson
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Risk Stratification ◽  
Hospital Mortality ◽  
Hospital Stay ◽  
Risk Score ◽  
Clinical Decision Making ◽  
External Validation ◽  
Clinical Decision ◽  
Early Warning Score

Objectives of the study Demographic changes alongside medical advances have resulted in older adults accounting for an increasing proportion of emergency hospital admissions. Current measures of illness severity, limited to physiological parameters, have shortcomings in this cohort, partly due to patient complexity. This study aimed to derive and validate a risk score for acutely unwell older adults which may enhance risk stratification and support clinical decision-making. Methods Data was collected from emergency admissions in patients ≥65 years from two UK general hospitals (April 2017- April 2018). Variables underwent regression analysis for in-hospital mortality and independent predictors were used to create a risk score. Performance was assessed on external validation. Secondary outcomes included seven-day mortality and extended hospital stay. Results Derivation (n = 8,974) and validation (n = 8,391) cohorts were analysed. The model included the National Early Warning Score 2 (NEWS2), clinical frailty scale (CFS), acute kidney injury, age, sex, and Malnutrition Universal Screening Tool. For mortality, area under the curve for the model was 0.79 (95% CI 0.78–0.80), superior to NEWS2 0.65 (0.62–0.67) and CFS 0.76 (0.74–0.77) (P<0.0001). Risk groups predicted prolonged hospital stay: the highest risk group had an odds ratio of 9.7 (5.8–16.1) to stay >30 days. Conclusions Our simple validated model (Older Persons’ Emergency Risk Assessment [OPERA] score) predicts in-hospital mortality and prolonged length of stay and could be easily integrated into electronic hospital systems, enabling automatic digital generation of risk stratification within hours of admission. Future studies may validate the OPERA score in external populations and consider an impact analysis.

A methodological comparison of risk scores versus decision trees for predicting drug-resistant infections: A case study using extended-spectrum beta-lactamase (ESBL) bacteremia

2019 ◽  
Vol 40 (4) ◽  
pp. 400-407 ◽  
Author(s):  
Katherine E. Goodman ◽  
Justin Lessler ◽  
Anthony D. Harris ◽  
Aaron M. Milstone ◽  
Pranita D. Tamma
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Risk Score ◽  
Bloodstream Infections ◽  
Multidrug Resistant ◽  
Risk Scores ◽  
Rapid Diagnostics ◽  
Predictive Values ◽  
Extended Spectrum Beta Lactamase ◽  
C Statistic

AbstractBackground:Timely identification of multidrug-resistant gram-negative infections remains an epidemiological challenge. Statistical models for predicting drug resistance can offer utility where rapid diagnostics are unavailable or resource-impractical. Logistic regression–derived risk scores are common in the healthcare epidemiology literature. Machine learning–derived decision trees are an alternative approach for developing decision support tools. Our group previously reported on a decision tree for predicting ESBL bloodstream infections. Our objective in the current study was to develop a risk score from the same ESBL dataset to compare these 2 methods and to offer general guiding principles for using each approach.Methods:Using a dataset of 1,288 patients with Escherichia coli or Klebsiella spp bacteremia, we generated a risk score to predict the likelihood that a bacteremic patient was infected with an ESBL-producer. We evaluated discrimination (original and cross-validated models) using receiver operating characteristic curves and C statistics. We compared risk score and decision tree performance, and we reviewed their practical and methodological attributes.Results:In total, 194 patients (15%) were infected with ESBL-producing bacteremia. The clinical risk score included 14 variables, compared to the 5 decision-tree variables. The positive and negative predictive values of the risk score and decision tree were similar (>90%), but the C statistic of the risk score (0.87) was 10% higher.Conclusions:A decision tree and risk score performed similarly for predicting ESBL infection. The decision tree was more user-friendly, with fewer variables for the end user, whereas the risk score offered higher discrimination and greater flexibility for adjusting sensitivity and specificity.

Multiple Approaches to the Diagnosis of Attention Deficit Hyperactivity Disorder

2013 ◽  
Vol 8 (4) ◽  
pp. 44-57 ◽  
Author(s):  
Rui Rijo ◽  
Ricardo Martinho ◽  
Xiaocheng Ge
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Clinical Decision Making ◽  
Adult Life ◽  
Clinical Decision ◽  
School Age Children ◽  
Free Text ◽  
Clinical Decisions ◽  
Hyperactivity Disorder

Studies indicate that about 3-7% of school-age children have attention deficit hyperactivity disorder (ADHD). If these disorders are not diagnosed and treated early, its consequences can harshly impair the adult life of the individual. In this context, early diagnosis is critical. Clinical reasoning is a key contributor to the quality of health care. Clinical decisions at the policy level are made within a stochastic domain; decisions for individuals are usually more qualitative. In both cases, poor reasoning can result in an undesirable outcome. Clinical decisions are most typically communicated in a document through free text. Text has significant limitations (particularly ambiguity and poor structuring) whether used for analysis, or to explain the decision-making process. In safety engineering, similar problems are faced in conveying safety arguments to support certification. As a result, approaches have been developed to conveying arguments in ways which improve communication and which are more amenable to analysis. The Goal Structuring Notation (GSN) – a graphical argumentation notation for safety – was developed for those reasons. It has evolved to be one of the most widely used techniques for representing safety arguments. The use of text-mining techniques is another approach in the process of achieving or suggesting a diagnosis to the physician. This paper investigates the relative feasibility of these two approaches and discuss their complementation. Based on a case example, the benefits and problems of adopting GSN and ontology approach in clinical decision-making for ADHD are discussed and illustrated.

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