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Clinical case reports are the 'eyewitness reports' of medicine and provide a valuable, unique, albeit noisy and underutilized type of evidence. Generally, a case report has a single main finding that represents the reason for writing up the report in the first place. However, no one has previously created an automatic way of identifying main finding sentences in case reports. We previously created a manual corpus of main finding sentences extracted from the abstracts and full text of clinical case reports. Here, we have utilized the corpus to create a machine learning-based model that automatically predicts which sentence(s) from abstracts state the main finding. The model has been evaluated on a separate manual corpus of clinical case reports and found to have good performance. This is a step toward setting up a retrieval system in which, given one case report, one can find other case reports that report the same or very similar main findings. The code and necessary files to run the main finding model can be downloaded from https://github.com/qi29/main_ finding_recognition, released under the Apache License, Version 2.0., (© The Author(s) 2020. Published by Oxford University Press.)

Traditional Chinese Medicine (TCM) has been developed for several thousand years and plays a significant role in health care for Chinese people. This paper studies the problem of classifying TCM clinical records into 5 main disease categories in TCM. We explored a number of state-of-the-art deep learning models and found that the recent Bidirectional Encoder Representations from Transformers can achieve better results than other deep learning models and other state-of-the-art methods. We further utilized an unlabeled clinical corpus to fine-tune the BERT language model before training the text classifier. The method only uses Chinese characters in clinical text as input without preprocessing or feature engineering. We evaluated deep learning models and traditional text classifiers on a benchmark data set. Our method achieves a state-of-the-art accuracy 89.39% ± 0.35%, Macro F1 score 88.64% ± 0.40% and Micro F1 score 89.39% ± 0.35%. We also visualized attention weights in our method, which can reveal indicative characters in clinical text., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

As with many other aspects of the modern world, in healthcare, the explosion of data and resources opens new opportunities for the development of added-value services. Still, a number of specific conditions on this domain greatly hinders these developments, including ethical and legal issues, fragmentation of the relevant data in different locations, and a level of (meta)data complexity that requires great expertise across technical, clinical, and biological domains. We propose the Patient Dossier paradigm as a way to organize new innovative healthcare services that sorts the current limitations. The Patient Dossier conceptual framework identifies the different issues and suggests how they can be tackled in a safe, efficient, and responsible way while opening options for independent development for different players in the healthcare sector. An initial implementation of the Patient Dossier concepts in the Rbbt framework is available as open-source at https://github.com/mikisvaz and https://github.com/Rbbt-Workflows., Competing Interests: The authors have declared that no competing interests exist

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Initially, the privacy of the medical data is ensured by using the key matrix developed based on the privacy utility coefficient matrix using the chronological-Whale optimization algorithm. The privacy protected data are subjected to classification by using ACNN that performs the optimal classification using the proposed TGD algorithm. The proposed TGD algorithm is the integration of Taylor series in the gradient descent algorithm that updates the optimal weight of ACNN based on the weights in the previous iterations. The analysis using the Cleveland, Switzerland, and Hungarian dataset proves that the proposed classification strategy obtains an accuracy of 0.9252, a sensitivity of 0.8419, and a specificity of 0.8387, respectively.

The following Article discusses the extent to which the constitutional right to informational privacy protects medical data from improper acquisition or dissemination by state agents. Part I provides background on Whalen v. Roe , the Supreme Court case that has been understood to establish the right to informational privacy. Part I also discusses the variations across the circuit courts as to what medical information is afforded protection by the right. Part II analyzes the well-established approaches adopted by the Second and Third Circuits as they present opposing interpretations of Whalen , one wholly protecting medical information and the other protecting scarcely any. Finally, Part III explains why the Supreme Court and courts that have yet to adopt a uniform approach should follow the Third Circuit and constitutionally protect all medical information from improper government acquisition or dissemination. Part III also argues for an amendment to the Privacy Act to provide individuals whose medical conditions are not afforded protection under the Constitution an alternative remedy., (Copyright by Cleveland State University.)

Background: To analyse structured and free text reports of shoulder X-ray examinations evaluating the quality of reports and potential contributions to clinical decision-making., Methods: We acquired both standard free text and structured reports of 31 patients with a painful shoulder without history of previous trauma who received X-ray exams. A template was created for the structured report based on the template ID 0000154 (Shoulder X-ray) from radreport.org using online software with clickable decision trees with concomitant generation of structured semantic reports. All reports were evaluated regarding overall quality and key features: content, information extraction and clinical relevance., Results: Two experienced orthopaedic surgeons reviewed and rated structured and free text reports of 31 patients independently. The structured reports achieved significantly higher median ratings in all key features evaluated (P < 0.001), including facilitation of information extraction (P < 0.001) and better contribution to subsequent clinical decision-making (P < 0.001). The overall quality of structured reports was significantly higher than in free text report (P < 0.001)., Conclusions: A comprehensive structured template may be a useful tool to assist in clinical decision-making and is, thus, recommended for the reporting of degenerative changes regarding X-ray examinations of the shoulder.

The current medical readiness category (CAT) status system used for military working dogs (MWDs) simply outlines the deployability of an MWD. This system, however, does not detail any other restrictions or the reason for assigning the current CAT status. The question is often raised as to whether the MWD can continue to work and perform everyday duties despite not being a CAT I. Using the Physical Profile Record system established for human providers, a system was adapted for MWDs. This system will allow Veterinary Corps Officers to give specific instructions to the handler and owning unit about the nature, progression, and details of injury or dysfunction beyond the CAT status. Furthermore, the ability to track chronic conditions and duration of illness will increase overall readiness of a kennel.

Background: The combined comorbidity score, which merges the Charlson and Elixhauser comorbidity indices, uses the ninth revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM). In October 2015, the United States adopted the 10th revision (ICD-10-CM)., Objective: The objective of this study is to examine different coding algorithms for the ICD-10-CM combined comorbidity score and compare their performance to the original ICD-9-CM score., Methods: Four ICD-10-CM coding algorithms were defined: 2 using General Equivalence Mappings (GEMs), one based on ICD-10-CA (Canadian modification) codes for Charlson and Elixhauser measures, and one including codes from all 3 algorithms. We used claims data from the Clinfomatics Data Mart to identify 2 cohorts. The ICD-10-CM cohort comprised patients who had a hospitalization between January 1, 2016 and March 1, 2016. The ICD-9-CM cohort comprised patients who had a hospitalization between January 1, 2015 and March 1, 2015. We used logistic regression models to predict 30-day hospital readmission for the original score in the ICD-9-CM cohort and for each ICD-10-CM algorithm in the ICD-10-CM cohort., Results: Distributions of each version of the score were similar. The algorithm based on ICD-10-CA codes [c-statistic, 0.646; 95% confidence interval (CI), 0.640-0.653] had the most similar discrimination for readmission to the ICD-9-CM version (c, 0.646; 95% CI, 0.639-0.653), but combining all identified ICD-10-CM codes had the highest c-statistic (c, 0.651; 95% CI, 0.644-0.657)., Conclusions: We propose an ICD-10-CM version of the combined comorbidity score that includes codes identified by ICD-10-CA and GEMs. Compared with the original score, it has similar performance in predicting readmission in a population of United States commercially insured individuals.

Importance: Hospital readmissions are being used increasingly as an indicator of quality of care. However, it remains difficult to identify potentially preventable readmissions., Objectives: To evaluate the identification of potentially preventable hospital readmissions by using a classification of readmissions based on administrative data., Design and Setting: We classified a random sample of 455 readmissions to a Dutch university hospital in 2014 using administrative data. We compared these results to a classification based on reviewing the medical records of these readmissions to evaluate the accuracy of classification by administrative data., Main Outcome Measures: Frequencies of categories of readmissions based on reviewing records versus those based on administrative data. Cohen's kappa for the agreement between both methods. The sensitivity and specificity of the identification of potentially preventable readmissions with classification by administrative data., Results: Reviewing the medical records of acute readmissions resulted in 28.5% of the records being classified as potentially preventable. With administrative data this was 44.1%. There was slight agreement between both methods: ƙ 0.08 (95% CI: 0.02-0.15, P < 0.05). The sensitivity of the classification of potentially preventable readmissions by administrative data was 63.1% and the specificity was 63.5%., Conclusions: This explorative study demonstrated differences between categorizing readmissions based on reviewing records compared to using administrative data. Therefore, this tool can only be used in practice with great caution. It is not suitable for penalizing hospitals based on their number of potentially preventable readmissions. However, hospitals might use this classification as a screening tool to identify potentially preventable readmissions more efficiently., (© The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)

Infectious and inflammatory diseases of the central nervous system are difficult to identify early. Case definitions for aseptic meningitis, encephalitis, myelitis, and acute disseminated encephalomyelitis (ADEM) are available, but rarely put to use. The VACC-Tool (Vienna Vaccine Safety Initiative Automated Case Classification-Tool) is a mobile application enabling immediate case ascertainment based on consensus criteria at the point-of-care. The VACC-Tool was validated in a quality management program in collaboration with the Robert-Koch-Institute. Results were compared to ICD-10 coding and retrospective analysis of electronic health records using the same case criteria. Of 68,921 patients attending the emergency room in 10/2010-06/2013, 11,575 were hospitalized, with 521 eligible patients (mean age: 7.6 years) entering the quality management program. Using the VACC-Tool at the point-of-care, 180/521 cases were classified successfully and 194/521 ruled out with certainty. Of the 180 confirmed cases, 116 had been missed by ICD-10 coding, 38 misclassified. By retrospective application of the same case criteria, 33 cases were missed. Encephalitis and ADEM cases were most likely missed or misclassified. The VACC-Tool enables physicians to ask the right questions at the right time, thereby classifying cases consistently and accurately, facilitating translational research. Future applications will alert physicians when additional diagnostic procedures are required.

Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve health care. In this paper, we build an integrating system for extracting medication names and symptom names from clinical notes. Then we apply nonnegative matrix factorization (NMF) and multi-view NMF to cluster clinical notes into meaningful clusters based on sample-feature matrices. Our experimental results show that multi-view NMF is a preferable method for clinical document clustering. Moreover, we find that using extracted medication/symptom names to cluster clinical documents outperforms just using words.

Background: Clinical data gathered for administrative purposes often lack sufficient information to separate the records of radiotherapy given for palliation from those given for cure. An absence, incompleteness, or inaccuracy of such information could hinder or bias the study of the utilization and outcome of radiotherapy. This study has three specific purposes: 1) develop a method to determine the therapeutic role of radiotherapy (TRR); 2) assess the accuracy of the method; 3) report the quality of the information on treatment "intent" recorded in the clinical data in Ontario, Canada. A general purpose is to use this study as a prototype to demonstrate and test a method to assess the quality of administrative data., Methods: This is a population based retrospective study. A random sample was drawn from the treatment records with "intent" assigned in treating hospitals. A decision tree is grown using treatment parameters as predictors and "intent" as outcome variable to classify the treatments into curative or palliative. The tree classifier was applied to the entire dataset, and the classification results were compared with those identified by "intent". A manual audit was conducted to assess the accuracy of the classification., Results: The following parameters predicted the TRR, from the strongest to the weakest: radiation dose per fraction, treated body-region, disease site, and time of treatment. When applied to the records of treatments given between 1990 and 2008 in Ontario, Canada, the classification rules correctly classified 96.1% of the records. The quality of the "intent" variable was as follows: 77.5% correctly classified, 3.7% misclassified, and 18.8% did not have an "intent" assigned., Conclusions: The classification rules derived in this study can be used to determine the TRR when such information is unavailable, incomplete, or inaccurate in administrative data. The study demonstrates that data mining approach can be used to effectively assess and improve the quality of large administrative datasets.