scholarly journals Clinical Decision Support Using a Partially Instantiated Probabilistic Graphical Model Optimized Through Quantum Annealing: Proof-of-Concept of a Computational Method Using a Clinical Data Set

2021 ◽  
Author(s):  
David Sahner ◽  
Richard J. Williams
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Graphical Model ◽  
Clinical Decision ◽  
Computational Method ◽  
Probabilistic Graphical Model ◽  
Proof Of Concept ◽  
Quantum Annealing ◽  
Data Set

scholarly journals Clinical Decision Support Using a Partially Instantiated Probabilistic Graphical Model Optimized through Quantum Annealing: Proof-of-Concept of a Computational Method Using a Clinical Data Set

2020 ◽  
Author(s):  
David Sahner ◽  
Richard Williams
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Graphical Model ◽  
Clinical Decision ◽  
Patient Data ◽  
Computational Method ◽  
Future Research ◽  
Proof Of Concept ◽  
Quantum Annealing ◽  
Data Set

AbstractAn approach is described to building a clinical decision support tool which leverages a partially instantiated graphical model optimized through quantum annealing. Potential advantages of such a strategy include the practical, and potentially real-time, use of multidimensional patient data to make a host of intuitively understandable predictions and recommendations in complex cases which are informed by a data-driven probabilistic model. Preliminary proof-of-concept of the general approach is demonstrated using a large well-established anonymized patient data set, revealing the predictive capability of a specific model. Ideas for future research are discussed.

scholarly journals Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jannik Schaaf ◽  
Martin Sedlmayr ◽  
Johanna Schaefer ◽  
Holger Storf
Keyword(s):  
Decision Support ◽  
Data Integration ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Rare Diseases ◽  
Scoping Review ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

scholarly journals MACHINE LEARNING BASED CLINICAL DECISION SUPPORT SYSTEM TO PREDICT FETAL HYPOXIA IN WOMEN DURING ANTENATAL CHECK-UP.

2021 ◽  
pp. 82-90
Author(s):  
Sajal Baxi
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Stress Test ◽  
Clinical Decision ◽  
Data Set ◽  
Remote Areas

BACKGROUND:Most under-five deaths occur within the first month after birth and intrapartum complications are a major contributor to the cause of death. These defects can be easily identified during the ante-natal check-up by use of a non-stress test. Due to the lack of availability of resources and medical experts in remote areas clinical decision support systems powered by machine learning models can provide information to the healthcare provider to make timely and better-informed decisions based on which course of treatment can be planned. AIM:The study aims to develop an accurate and sensitive clinical decision support system model that can identify pathological fetuses based on the fetal heart rate recordings taken during the non-stress test. METHOD: Foetal Heart rate recordings along with 10 other variables were collected from 1800 pregnant women in their third trimester. The data was put through a feature selection algorithm to identify important variables in the set. The data set was randomly divided into 2 independent random samples in the ratio of 70% for training and 30% for testing. After testing various machine learning algorithms based on specificity, sensitivity to accurately classify the fetus into normal, suspected, or pathological Random Forest algorithm was chosen. RESULT:The fetal status determined by Obstetrician 77.85% observations from the normal category, 19.88% from the suspected category, and 8.28% from the pathological category. The Boruta algorithm revealed that all 11 independent variables in the data set were important to predict the outcome in the test set. In the training set the model had an accuracy of 99.04% and in the testing set accuracy was 94.7% (p-value=< 2.2e-16) with the precision of 97.56% to detect the pathological category. CONCLUSION:With the ability of the model to accurately predict the pathological category the CDS can be used by healthcare providers in remote areas to identify high-risk pregnant women and take the decision on the medical care to be provided.

Abstract WP315: An Electronic Health Record Phenotype of Ischemic Stroke Using Non-Claims Clinical Data and Machine Learning

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Benjamin R Kummer ◽  
Jorge M Luna ◽  
Charles C Esenwa ◽  
Hojjat Salmasian ◽  
David K Vawdrey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Clinical Laboratory ◽  
Clinical Decision ◽  
Health Record ◽  
Negative Controls

Introduction: Real-time identification of patients with acute ischemic stroke (AIS) in the electronic health record (EHR) can enhance care delivery systems, clinical decision support, and research subject recruitment. EHR data that is accessible during a patient’s admission may be used to identify patients with AIS, but established methods for characterizing which data to use have not yet been determined. Hypotheses: 1. An EHR “phenotype” of AIS can be identified using clinical EHR data. 2. Machine learning can identify the AIS phenotype using similar inputs with greater accuracy than clinician-specified identification algorithms. Methods: Two stroke neurologists selected generalizable AIS-related clinical data points from the Columbia University Medical Center EHR (clinical laboratory results and medication, imaging, and stroke service list orders) to identify the AIS phenotype, and determined pre-specified priority logic based on institutional practice patterns. Separately, a regularized logistic regression (RLR) model was applied to all available neurology-related order sets and clinical laboratory inputs. The classification accuracy of the two algorithms was compared using a “gold standard” data set, consisting of our institution’s ischemic stroke registry from January 1 st , 2015 to March 31 st , 2016. Negative controls were selected from all patients admitted to the neurology service at our institution during the same time period. Results: Our data contained 482 patients with AIS and 3,628 negative controls. The clinician-specified identification algorithm identified the AIS phenotype with sensitivity of 90.6%, specificity of 50.4%, and positive predictive value (PPV) of 93.5%. In comparison, the RLR-based algorithm had a sensitivity of 96.3%, specificity of 52.2%, and PPV of 93.8%. Conclusions: We determined an AIS phenotype that could be identified using clinical, non-claims data that is available during a patient’s admission, and used machine learning to optimize the classifying ability. While specificity is low, the high sensitivity may allow use for screening and clinical decision support. Further studies are needed to externally validate these findings and optimize algorithm specificity.

scholarly journals Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support

2017 ◽  
Vol 24 (4) ◽  
pp. 806-812 ◽  
Author(s):  
Kin Wah Fung ◽  
Joan Kapusnik-Uner ◽  
Jean Cunningham ◽  
Stefanie Higby-Baker ◽  
Olivier Bodenreider
Keyword(s):  
Decision Support ◽  
Drug Interactions ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Reference List ◽  
Prescription Data ◽  
Data Set ◽  
Drug Ingredient ◽  
Clinical Drug

Abstract Objective: To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice. Methods: Drugs in the DDI tables from First DataBank (FDB), Micromedex, and Multum were mapped to RxNorm. The KBs were compared at the clinical drug, ingredient, and DDI rule levels. The KBs were evaluated against a reference list of highly significant DDIs from the Office of the National Coordinator for Health Information Technology (ONC). The KBs and the ONC list were applied to a prescription data set to simulate their use in clinical decision support. Results: The KBs contained 1.6 million (FDB), 4.5 million (Micromedex), and 4.8 million (Multum) clinical drug pairs. Altogether, there were 8.6 million unique pairs, of which 79% were found only in 1 KB and 5% in all 3 KBs. However, there was generally more agreement than disagreement in the severity rankings, especially in the contraindicated category. The KBs covered 99.8–99.9% of the alerts of the ONC list and would have generated 25 (FDB), 145 (Micromedex), and 84 (Multum) alerts per 1000 prescriptions. Conclusion: The commercial KBs differ considerably in size and quantity of alerts generated. There is less variability in severity ranking of DDIs than suggested by previous studies. All KBs provide very good coverage of the ONC list. More work is needed to standardize the editorial policies and evidence for inclusion of DDIs to reduce variation among knowledge sources and improve relevance. Some DDIs considered contraindicated in all 3 KBs might be possible candidates to add to the ONC list.

scholarly journals Giving Your EHR a Checkup after COVID-19: A Practical Framework for Reviewing Clinical Decision Support in Light of the Telemedicine Expansion (Preprint)

2020 ◽  
Author(s):  
Jonah Feldman ◽  
Adam Szerencsy ◽  
Devin Mann ◽  
Jonathan Austrian ◽  
Ulka Kothari ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Data ◽  
Chronic Care ◽  
Medical Center ◽  
Academic Medical Center ◽  
Clinical Decision ◽  
Rapid Expansion ◽  
Office Visits ◽  
Academic Medical

BACKGROUND The transformation of healthcare during COVID-19 with the rapid expansion of telemedicine visits presents new challenges to chronic care and preventive health providers. Clinical decision support (CDS) is critically important to chronic care providers, and CDS malfunction is common during times of change. It is essential to regularly re-assess an organization's ambulatory CDS program to maintain care quality. This is especially true after an immense change, like the COVID-19 telemedicine expansion. OBJECTIVE Our objective is to re-assess the ambulatory CDS program at a large academic medical center in light of telemedicine's expansion in response to COVID-19. METHODS Our clinical informatics team devised a practical framework for an intra-pandemic ambulatory CDS assessment focused on the impact of the telemedicine expansion. This assessment began with a quantitative analysis comparing CDS alert performance in the context of in-person and telemedicine visits. Board-certified physician informaticists then completed a formal workflow review of alerts with inferior performance in telemedicine visits. Informaticists then reported on themes and optimization opportunities through the existing CDS governance structure. RESULTS Our assessment revealed that 10 of our top 40 alerts by volume were not firing as expected in telemedicine visits. In 3 out of the top 5 alerts, providers were significantly less likely to take action in telemedicine when compared to office visits. Cumulatively, alerts in telemedicine encounters had an action taken rate of 5.3% (3,257/64,938) compared to 8.3% (19,427/233,636) for office visits. Observations from a clinical informaticist workflow review included: (1) Telemedicine visits have different workflows than office visits. Some alerts developed for the office were not appearing at the optimal time in the telemedicine workflow. (2) Missing clinical data is a common reason for decreased alert firing seen in telemedicine visits. (3) Remote patient monitoring and patient-reported clinical data entered through the portal could replace data collection usually completed in the office by an MA or RN. CONCLUSIONS Conclusions: In a large academic medical center at the pandemic epicenter, an intra-pandemic ambulatory CDS assessment revealed clinically significant CDS malfunctions that highlight the importance of re-assessing ambulatory CDS performance after the telemedicine expansion. CLINICALTRIAL

scholarly journals A Multivariable Prediction Model to Select Colorectal Surgical Patients for Co-Management

2020 ◽  
Vol 33 (13) ◽  
Author(s):  
Alexandra Bayão Horta ◽  
Carlos Geraldes ◽  
Cátia Salgado ◽  
Susana Vieira ◽  
Miguel Xavier ◽  
...  
Keyword(s):  
Decision Support ◽  
Prediction Model ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Surgical Patients ◽  
Outcome Variable ◽  
Data Set ◽  
Support Tool ◽  
Clinical Decision Support Tool

Introduction: Increased life expectancy leads to older and frailer surgical patients. Co-management between medical and surgical specialities has proven favourable in complex situations. Selection of patients for co-management is full of difficulties. The aim of this study was to develop a clinical decision support tool to select surgical patients for co-management.Material and Methods: Clinical data was collected from patient electronic health records with an ICD-9 code for colorectal surgery from January 2012 to December 2015 at a hospital in Lisbon. The outcome variable consists in co-management signalling. A dataset from 344 patients was used to develop the prediction model and a second data set from 168 patients was used for external validation.Results: Using logistic regression modelling the authors built a five variable (age, burden of comorbidities, ASA-PS status, surgical risk and recovery time) predictive referral model for co-management. This model has an area under the curve (AUC) of 0.86 (95% CI: 0.81 - 0.90), a predictive Brier score of 0.11, a sensitivity of 0.80, a specificity of 0.82 and an accuracy of 81.3%.Discussion: Early referral of high-risk patients may be valuable to guide the decision on the best level of post-operative clinical care. We developed a simple bedside decision tool with a good discriminatory and predictive performance in order to select patients for comanagement.Conclusion: A simple bed-side clinical decision support tool of patients for co-management is viable, leading to potential improvement in early recognition and management of postoperative complications and reducing the ‘failure to rescue’. Generalizability to other clinical settings requires adequate customization and validation.

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