The personalized health care service utilizes the relational patient data and big data analytics to tailor the medication recommendations. However, most of the health care data are in unstructured form and it consumes a lot of time and effort to pull them into relational form. This study proposes a novel data lake architecture to reduce the data ingestion time and improve the precision of healthcare analytics. It also removes the data silos and enhances the analytics by allowing the connectivity to the third-party data providers (such as clinical lab results, chemist, insurance company,etc.). The data lake architecture uses the Hadoop Distributed File System (HDFS) to provide the storage for both structured and unstructured data. This study uses K-means clustering algorithm to find the patient clusters with similar health conditions. Subsequently, it employs a support vector machine to find the most successful healthcare recommendations for the each cluster. Our experiment results demonstrate the ability of data lake to reduce the time for ingesting data from various data vendors regardless of its format. Moreover, it is evident that the data lake poses the potential to generate clusters of patients more precisely than the existing approaches. It is obvious that the data lake provides a unified storage location for the data in its native format. It can also improve the personalized healthcare medication recommendations by removing the data silos.
The proactive, patient-centric and tailored healthcare medication recommendation to the individual based on the analysis of the corresponding person’s EHR, genomic profile, laboratory data and other related supporting data @cite_33 .
@cite_33
2521579474 2419673715 2784499877 2117273822
Objective Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. @PARASPLIT Materials and Methods The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. @PARASPLIT Results Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16 , and recall 35 . This can be improved to 0.90, 24 , and 47 ( P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). @PARASPLIT Discussion Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. @PARASPLIT Conclusion Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. With the advent of electronic health records, more data are continuously collected for individual patients, and more data are available for review from past patients. Despite this, it has not yet been possible to successfully use this data to systematically build clinical decision support systems that can produce personalized clinical recommendations to assist clinicians in providing individualized healthcare. In this paper, we present a novel approach, discovery engine (DE), that discovers which patient characteristics are most relevant for predicting the correct diagnosis and or recommending the best treatment regimen for each patient. We demonstrate the performance of DE in two clinical settings: diagnosis of breast cancer as well as a personalized recommendation for a specific chemotherapy regimen for breast cancer patients. For each distinct clinical recommendation, different patient features are relevant; DE can discover these different relevant features and use them to recommend personalized clinical decisions. The DE approach achieves a 16.6 improvement over existing state-of-the-art recommendation algorithms regarding kappa coefficients for recommending the personalized chemotherapy regimens. For diagnostic predictions, the DE approach achieves a 2.18 and 4.20 improvement over existing state-of-the-art prediction algorithms regarding prediction error rate and false positive rate, respectively. We also demonstrate that the performance of our approach is robust against missing information and that the relevant features discovered by DE are confirmed by clinical references. Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient’s record. We propose a representation of patients’ entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93–0.94), 30-day unplanned readmission (AUROC 0.75–0.76), prolonged length of stay (AUROC 0.85–0.86), and all of a patient’s final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient’s chart. Medical researchers are legally required to protect patients' privacy by removing personally identifiable information from medical records before sharing the data with other researchers. We present an evaluation of methods for computer-assisted removal and replacement of protected health information (PHI) from free-text nursing notes collected in the intensive care unit as part of the MIMIC II project. A semiautomated method was developed to allow clinicians to highlight PHI on the screen of a tablet PC and to compare and combine the selections of different experts reading the same notes. An analysis of the performance of three human expert de-identifiers and of an automated system is presented. Expert adjudication demonstrated that inter-human variability was high, with few false positives and many false negatives. The sensitivity of human experts working alone ranged from 0.63 to 0.93, with an average of 0.81, and the average positive predictive value was 0.98. An algorithm generated few false negatives but many false positives. Its sensitivity was 0.85, but its positive predictive value was only 0.37. The de-identified database of nursing notes was re-identified with realistic surrogate (but unprotected) dates, serial numbers, names, and phrases to provide a gold standard database of over 2600 notes (approximately 340,000 words) with over 1700 instances of PHI. This reference gold standard database of nursing notes and the Java source code used to evaluate algorithm performance will be made freely available on Physionet in order to facilitate the development and validation of future de-identification algorithms.