Technology for large-scale translation of clinical practice guidelines : a pilot study of the performance of a hybrid human and computer-assisted approach

Background: The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, was initiated in 2011 to optimize quality of care by promoting evidence-based decision-making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL Multiterm termbases) from medical termbases and support from online machine translation. This has resulted in a validated translation memory which is now in use for the translation of new and updated guidelines. Objective: The objective of this study was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. Methods: We conducted a pilot trial in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (a) by certificated junior translators without medical specialization using the hybrid method (b) by an experienced medical translator without this support and (c) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The Human Translation Edit Rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. Results: The average number of words per guideline was 1,195 and the mean total translation time was 100.2 min/1,000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 min/1,000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Conclusions: Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator

Prospective evaluation of an internet-linked handheld computer critical care knowledge access system

INTRODUCTION: Critical care physicians may benefit from immediate access to medical reference material. We evaluated the feasibility and potential benefits of a handheld computer based knowledge access system linking a central academic intensive care unit (ICU) to multiple community-based ICUs. METHODS: Four community hospital ICUs with 17 physicians participated in this prospective interventional study. Following training in the use of an internet-linked, updateable handheld computer knowledge access system, the physicians used the handheld devices in their clinical environment for a 12-month intervention period. Feasibility of the system was evaluated by tracking use of the handheld computer and by conducting surveys and focus group discussions. Before and after the intervention period, participants underwent simulated patient care scenarios designed to evaluate the information sources they accessed, as well as the speed and quality of their decision making. Participants generated admission orders during each scenario, which were scored by blinded evaluators. RESULTS: Ten physicians (59%) used the system regularly, predominantly for nonmedical applications (median 32.8/month, interquartile range [IQR] 28.3–126.8), with medical software accessed less often (median 9/month, IQR 3.7–13.7). Eight out of 13 physicians (62%) who completed the final scenarios chose to use the handheld computer for information access. The median time to access information on the handheld handheld computer was 19 s (IQR 15–40 s). This group exhibited a significant improvement in admission order score as compared with those who used other resources (P = 0.018). Benefits and barriers to use of this technology were identified. CONCLUSION: An updateable handheld computer system is feasible as a means of point-of-care access to medical reference material and may improve clinical decision making. However, during the study, acceptance of the system was variable. Improved training and new technology may overcome some of the barriers we identified

Open Knowledge Accessing Method in IoT-based Hospital Information System for Medical Record Enrichment

For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based Hospital Information System (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer and other medical devices, they are raw, simple, weak-content and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in Linked Open Data (LOD). A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing

Computationally Enhanced Medical Decision Support for Pancreatic Cancer

This project investigated and applied computational techniques to enlarge a pancreatic cancer database and to enhance the medical decision-making process supported by this database. The database was previously developed by the Department of Surgical Oncology of the University of Massachusetts Medical School in conjunction with the Department of Computer Science at WPI. We substantially increased the number of patients included in the database, and conducted data mining experiments. These experiments compared the accuracies of predictions made by medical doctors and by data mining methods for two separate patient outcomes: tumor malignancy and survival time after surgery. The results of our experiments show that data mining techniques can be used to enhance the quality of medical decisions

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Artifical intelligence in medical application: An exploration

The advancement in computer technology has encouraged the researchers to develop software for assisting doctors in making decision without consulting the specialists directly. The software development exploits the potential of human intelligence such as reasoning, making decision, learning (by experiencing) and many others. Artificial intelligence is not a new concept, yet it has been accepted as a new technology in computer science. It has been applied in many areas such as education, business, medical and manufacturing. This paper explores the potential of artificial intelligence techniques particularly for web-based medical applications. In addition, a model for web-based medical diagnosis and prediction is proposed

Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer's Disease

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer's disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network's classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer's disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.Comment: MLCN 201

The influence of personal knowledge management on individual health care decision-making : an action learning approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, Auckland, New Zealand

Appendix B and Appendix K were removed at the author's request.Background: Making effective health care decisions is important. Despite the large volumes of information available, individuals often face personal limitations evaluating this information and making optimal decisions. Personal knowledge management has been suggested as a method of addressing information barriers and improving decision-making. Personal knowledge management has, however, been mostly applied within an education context, in order to improve individuals’ learning performance. From the available literature in this area, very limited research or significant conceptual development has been undertaken on personal knowledge management and its influence on decision-making, particularly in the health care context. Aims and Significance: This study examines an effective personal knowledge management strategy for older adults (aged between 46 and 75) with limited computer/technological skills by answering the following questions: How do older adults access and evaluate information and knowledge for health care decision-making? How can personal knowledge management help older adults with limited computer/technological abilities manage their information and knowledge for health care decision-making? How effective is an action learning training program in supporting older adults with limited computer/technological abilities for health care decision-making? The aim of this study is to provide an understanding of the use of action learning and personal knowledge management pertaining to older adults’ health care decision-making. Examples of relevant health care concerns include, diabetes and obesity or other issues of this nature, but are exclusive of severe health issues, such as cancer. The findings will offer educators and researchers an understanding of ways to help these individuals to navigate the world of information regarding critical personal decision-making, with specific reference to health care. Method: To investigate this issue, a qualitative study was conducted using action learning with thematic and grounded theory coding techniques. New Zealand patient health care support groups and churches provided a source of older adults with health-related issues as volunteers. Participants were asked to practice personal knowledge management strategies, focusing on their personal health-related issues after each learning session. In the following session, the issues or experiences that the participants encountered whilst conducting their self-practice exercises, within their groups were discussed. Findings: This study found that the older adult participants in this study used Google, Facebook closed groups, YouTube, online videos, health care support groups, family and medical professionals as information sources before embarking upon this training program. To evaluate alternative treatment options, these participants rely predominantly on family, friends, medical professionals and their personal life experience for decisions. This study found that major factors that negatively impacted older adults’ effective information interpretation and decision-making include: barriers to accessing accurate and relevant health care information and knowledge, barriers to computer-based technology use, and humanistic barriers. The findings suggest that a four-stage personal knowledge management strategy could help older adults (with limited computer/technological skills) to overcome the barriers to effective information interpretation, and making informed health care decisions. Finally, this study suggests some practical training/learning techniques for older adults. For instance, major individual health-related issues of the older adults within the pre-training program need to be confirmed, followed by a warm welcome prior to the commencement of the training program. I learned that it is important to pre-diagnose participants’ abilities in learning and computer-based technology before designing the training program. This can help to develop an appropriate training program for a specific cohort. Conclusions: The findings of this study contribute to the development of an academic understanding of personal knowledge management conceptualisation in the consumer decision-making field, with the aim of improving older adults’ information and knowledge management processes. This study serves as a vantage point for further empirical research in personal knowledge management and older adult education and training