scholarly journals Personalizing Medication Recommendation with a Graph-Based Approach

2022 ◽  
Vol 40 (3) ◽  
pp. 1-23
Author(s):  
Suman Bhoi ◽  
Mong Li Lee ◽  
Wynne Hsu ◽  
Hao Sen Andrew Fang ◽  
Ngiap Chuan Tan
Keyword(s):  
Drug Interaction ◽  
Electronic Health Records ◽  
Recommender System ◽  
Information Sources ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Clinical Practices ◽  
Health Records ◽  
Patient’S History ◽  
Mimic Iii

The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender system called PREMIER that incorporates information from the EHR. We utilize the various weights in the system to compute the contributions from the information sources for the recommended medications. Our system models the drug interaction from an external drug database and the drug co-occurrence from the EHR as graphs. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Case studies demonstrate that the justifications provided by PREMIER are appropriate and aligned to clinical practices.

scholarly journals Treatment effect prediction with adversarial deep learning using electronic health records

2020 ◽  
Vol 20 (S4) ◽  
Author(s):  
Jiebin Chu ◽  
Wei Dong ◽  
Jinliang Wang ◽  
Kunlun He ◽  
Zhengxing Huang
Keyword(s):  
Logistic Regression ◽  
Electronic Health Records ◽  
Large Volume ◽  
Treatment Effect ◽  
Learning Strategy ◽  
State Of The Art ◽  
Patient Characteristics ◽  
Adversarial Learning ◽  
Health Records ◽  
Proposed Model

Abstract Background Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study. Method We examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the problem. Our model employed two auto-encoders for learning the representative and discriminative features of both patient characteristics and treatments from EHR data. The discriminative power of the learned features was further enhanced by decoding the correlational information between the patient characteristics and subsequent treatments by means of a generated adversarial learning strategy. Thereafter, a logistic regression layer was appended on the top of the resulting feature representation layer for TEP. Result The proposed model was evaluated on two real clinical datasets collected from the cardiology department of a Chinese hospital. In particular, on acute coronary syndrome (ACS) dataset, the proposed adversarial deep treatment effect prediction (ADTEP) (0.662) exhibited 1.4, 2.2, and 6.3% performance gains in terms of the area under the ROC curve (AUC) over deep treatment effect prediction (DTEP) (0.653), logistic regression (LR) (0.648), and support vector machine (SVM) (0.621), respectively. As for heart failure (HF) case study, the proposed ADTEP also outperformed all benchmarks. The experimental results demonstrated that our proposed model achieved competitive performance compared to state-of-the-art models in tackling the TEP problem. Conclusion In this work, we propose a novel model to address the TEP problem by utilizing a large volume of observational data from EHR. With adversarial learning strategy, our proposed model can further explore the correlational information between patient statuses and treatments to extract more robust and discriminative representation of patient samples from their EHR data. Such representation finally benefits the model on TEP. The experimental results of two case studies demonstrate the superiority of our proposed method compared to state-of-the-art methods.

scholarly journals Clinician Perspectives on Electronic Health Records, Communication, and Patient Safety Across Diverse Medical Oncology Practices

2019 ◽  
Vol 15 (6) ◽  
pp. e529-e536 ◽  
Author(s):  
Minal R. Patel ◽  
Christopher R. Friese ◽  
Kari Mendelsohn-Victor ◽  
Alex J. Fauer ◽  
Bidisha Ghosh ◽  
...  
Keyword(s):  
Patient Safety ◽  
Electronic Health Records ◽  
Safety Culture ◽  
Care Quality ◽  
Clinical Practices ◽  
Seamless Integration ◽  
Health Records ◽  
Clinician Communication ◽  
Electronic Health

PURPOSE: We know little about how increased technological sophistication of clinical practices affects safety of chemotherapy delivery in the outpatient setting. This study investigated to what degree electronic health records (EHRs), satisfaction with technology, and quality of clinician-to-clinician communication enable a safety culture. METHODS: We measured actions consistent with a safety culture, satisfaction with practice technology, and quality of clinician communication using validated instruments among 297 oncology nurses and prescribers in a statewide collaborative. We constructed an index to reflect practice reliance on EHRs (1 = “all paper” to 5 = “all electronic”). Linear regression models (with robust SEs to account for clustering) examined relationships between independent variables of interest and safety. Models were adjusted for clinician age. RESULTS: The survey response rate was 68% (76% for nurses and 59% for prescribers). The mean (standard deviation) safety score was 5.3 (1.1), with a practice-level range of 4.9 to 5.4. Prescribers reported fewer safety actions than nurses. Higher satisfaction with technology and higher-quality clinician communication were significantly associated with increased safety actions, whereas increased reliance on EHRs was significantly associated with lower safety actions. CONCLUSION: Practices vary in their performance of patient safety actions. Supporting clinicians to integrate technology and strengthen communication are promising intervention targets. The inverse relationship between reliance on EHRs and safety suggests that technology may not facilitate clinicians’ ability to attend to patient safety. Efforts to improve cancer care quality should focus on more seamless integration of EHRs into routine care delivery and emphasize increasing the capacity of all care clinicians to communicate effectively and coordinate efforts when administering high-risk treatments in ambulatory settings.

Deep Learning for Natural Language Processing in Urology: State-of-the-Art Automated Extraction of Detailed Pathologic Prostate Cancer Data From Narratively Written Electronic Health Records

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Sami-Ramzi Leyh-Bannurah ◽  
Zhe Tian ◽  
Pierre I. Karakiewicz ◽  
Ulrich Wolffgang ◽  
Guido Sauter ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Clinical Research ◽  
Language Processing ◽  
Gold Standard ◽  
Tumor Volume ◽  
State Of The Art ◽  
Surgical Margin ◽  
Tumor Stage ◽  
Health Records

Purpose Entering all information from narrative documentation for clinical research into databases is time consuming, costly, and nearly impossible. Even high-volume databases do not cover all patient characteristics and drawn results may be limited. A new viable automated solution is machine learning based on deep neural networks applied to natural language processing (NLP), extracting detailed information from narratively written (eg, pathologic radical prostatectomy [RP]) electronic health records (EHRs). Methods Within an RP pathologic database, 3,679 RP EHRs were randomly split into 70% training and 30% test data sets. Training EHRs were automatically annotated, providing a semiautomatically annotated corpus of narratively written pathologic reports with initially context-free gold standard encodings. Primary and secondary Gleason pattern, corresponding percentages, tumor stage, nodal stage, total volume, tumor volume and diameter, and surgical margin were variables of interest. Second, state-of-the-art NLP techniques were used to train an industry-standard language model for pathologic EHRs by transfer learning. Finally, accuracy of the named entity extractors was compared with the gold standard encodings. Results Agreement rates (95% confidence interval) for primary and secondary Gleason patterns each were 91.3% (89.4 to 93.0), corresponding to the following: Gleason percentages, 70.5% (67.6 to 73.3) and 80.9% (78.4 to 83.3); tumor stage, 99.3% (98.6 to 99.7); nodal stage, 98.7% (97.8 to 99.3); total volume, 98.3% (97.3 to 99.0); tumor volume, 93.3% (91.6 to 94.8); maximum diameter, 96.3% (94.9 to 97.3); and surgical margin, 98.7% (97.8 to 99.3). Cumulative agreement was 91.3%. Conclusion Our proposed NLP pipeline offers new abilities for precise and efficient data management from narrative documentation for clinical research. The scalable approach potentially allows the NLP pipeline to be generalized to other genitourinary EHRs, tumor entities, and other medical disciplines.

scholarly journals Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i436-i444 ◽  
Author(s):  
Mengshi Zhou ◽  
Chunlei Zheng ◽  
Rong Xu
Keyword(s):  
Drug Discovery ◽  
Electronic Health Records ◽  
Drug Target ◽  
Cross Validation ◽  
De Novo ◽  
State Of The Art ◽  
P Value ◽  
Prediction System ◽  
Health Records ◽  
Drug Candidates

Abstract Motivation Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. Results We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision–recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value < 0.0001]. The EHR-based case–control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value < 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients’ EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. Availability and implementation nlp.case.edu/public/data/TargetPredict.

scholarly journals Finding Rare Disease Patients in EHR Databases via Lightly-Supervised Learning

2020 ◽  
Author(s):  
Rich Colbaugh ◽  
Kristin Glass
Keyword(s):  
Electronic Health Records ◽  
Rare Disease ◽  
Rare Diseases ◽  
High Performance ◽  
Computational Models ◽  
State Of The Art ◽  
Performance Models ◽  
Health Records ◽  
Learning Procedure ◽  
Population Scale

There is considerable interest in developing computational models capable of detecting rare disease patients in population-scale databases such as electronic health records (EHRs). Deriving these models is challenging for several reasons, perhaps the most daunting being the limited number of already-diagnosed, 'labeled' patients from which to learn. We overcome this obstacle with a novel lightly-supervised algorithm that leverages unlabeled and/or unreliably-labeled patient data - which is typically plentiful - to facilitate model induction. Importantly, we prove the algorithm is safe: adding unlabeled/unreliably-labeled data to the learning procedure produces models which are usually more accurate, and guaranteed never to be less accurate, than models learned from reliably-labeled data alone. The proposed method is shown to substantially outperform state-of-the-art models in patient-finding experiments involving two different rare diseases and a country-scale EHR database. Additionally, we demonstrate feasibility of transforming high-performance models generated through light supervision into simpler models which, while still accurate, are readily-interpretable by non-experts.

scholarly journals medExtractR: A targeted, customizable approach to medication extraction from electronic health records

2020 ◽  
Vol 27 (3) ◽  
pp. 407-418 ◽  
Author(s):  
Hannah L Weeks ◽  
Cole Beck ◽  
Elizabeth McNeer ◽  
Michael L Williams ◽  
Cosmin A Bejan ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
High Performance ◽  
Medical Information ◽  
Medical Center ◽  
Processing System ◽  
Small Sample ◽  
Health Records ◽  
Electronic Health ◽  
Mimic Iii

Abstract Objective We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records. Materials and Methods Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database. Results On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset. Discussion The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied. Conclusions The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

State-of-the-art automated extraction of detailed pathological data from narratively written electronic health records

2018 ◽  
Vol 17 (2) ◽  
pp. e1209
Author(s):  
S.-R. Leyh-Bannurah ◽  
Z. Tian ◽  
P.I. Karakiewicz ◽  
U. Wolffgang ◽  
D. Pehrke ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
State Of The Art ◽  
Automated Extraction ◽  
Health Records ◽  
Electronic Health
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