Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. <s> BIB001 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> In order to develop an artificial intelligence and computer-aided diagnosis system that assists neuroradiologists to interpret magnetic resonance (MR) images. This paper employed Hu moment invariant (HMI) as the brain image features, and we proposed a novel predator-prey particle swarm optimization (PP-PSO) algorithm used to train the weights of single-hidden layer neural-network (SLN). We used five-fold stratified cross validation (FFSCV) for statistical analysis. Our proposed HMI + SLN + PP-PSO method achieved a sensitivity of 96.00 ± 5.16%, a specificity of 98.57 ± 0.75%, and an accuracy of 98.25 ± 0.65% for the DA-160 dataset, and yields a sensitivity of 97.14 ± 2.33%, a specificity of 97.00 ± 0.34%, and an accuracy of 97.02 ± 0.33% for the DA-255 dataset. Our method performs better than six state-of-the-art approaches. <s> BIB002 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases. The created list is used for assessing the nature of the finding - whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases. <s> BIB003 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Purpose ::: ::: ::: ::: ::: In view of the elderly caregiving service being in high demand nowadays, the purpose of this paper is to develop an intelligent e-healthcare system for the domestic care industry by using the Internet of Things (IoTs) and Fuzzy Association Rule Mining (FARM) approach. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The IoTs connected with the e-healthcare system collect real-time vital sign monitoring data for the e-healthcare system. The FARM approach helps to identify the hidden relationships between the data records in the e-healthcare system to support the elderly care management tasks. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: To evaluate the proposed system and approach, a case study was carried out to identify the association between the specific collected demographic data, behavior data and the health measurements data in the e-healthcare system. It is found that the discovered rules are useful for the care management tasks in the elderly healthcare service. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Knowledge discovery in databases uses various data mining techniques and rule-based artificial intelligence algorithms. This paper demonstrates complete processes on how an e-healthcare system connected with IoTs can support the elderly care services via a data collection phase, data analysis phase and data reporting phase by using the FARM to evaluate the fuzzy sets of the data attributes. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness. <s> BIB004 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Abstract Purpose This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. Methods The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. Results The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230 K sets. Both setups reached efficiency of about 105 sets per second. Conclusions The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health. <s> BIB005 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Objective ::: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department. ::: ::: ::: Methods ::: This was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model. ::: ::: ::: Results ::: A total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65–0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81–0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85–0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84–0.86) for the test data set. ::: ::: ::: Conclusion ::: Compared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection. <s> BIB006 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> BACKGROUND ::: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. ::: ::: ::: METHODS ::: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. ::: ::: ::: RESULTS ::: The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). ::: ::: ::: CONCLUSIONS ::: Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society. <s> BIB007 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> We construct an EMR-based medical knowledge network (EMKN) to represent medical knowledge.The network shows some complex properties, including small-world property, scale-free property and community structure.Based on the network, we propose a simple but universal diagnosis model. This model takes symptom entities as inputs, and returns the disease entities as outputs.This model only needs simple features as inputs, and is not restricted to a specific disease.Our study results experimentally demonstrate the effectiveness of EMKN. Background and ObjectiveElectronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support. We attempt to integrate this medical knowledge into a complex network, and then implement a diagnosis model based on this network. MethodsThe dataset of our study contains 992 records which are uniformly sampled from different departments of the hospital. In order to integrate the knowledge of these records, an EMR-based medical knowledge network (EMKN) is constructed. This network takes medical entities as nodes, and co-occurrence relationships between the two entities as edges. Selected properties of this network are analyzed. To make use of this network, a basic diagnosis model is implemented. Seven hundred records are randomly selected to re-construct the network, and the remaining 292 records are used as test records. The vector space model is applied to illustrate the relationships between diseases and symptoms. Because there may exist more than one actual disease in a record, the recall rate of the first ten results, and the average precision are adopted as evaluation measures. ResultsCompared with a random network of the same size, this network has a similar average length but a much higher clustering coefficient. Additionally, it can be observed that there are direct correlations between the community structure and the real department classes in the hospital. For the diagnosis model, the vector space model using disease as a base obtains the best result. At least one accurate disease can be obtained in 73.27% of the records in the first ten results. ConclusionWe constructed an EMR-based medical knowledge network by extracting the medical entities. This network has the small-world and scale-free properties. Moreover, the community structure showed that entities in the same department have a tendency to be self-aggregated. Based on this network, a diagnosis model was proposed. This model uses only the symptoms as inputs and is not restricted to a specific disease. The experiments conducted demonstrated that EMKN is a simple and universal technique to integrate different medical knowledge from EMRs, and can be used for clinical decision support. <s> BIB008 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Objective ::: The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening. ::: ::: ::: Materials and Methods ::: Five years of clinical data were extracted from an electronic health record system for 747 985 patients who had at least one visit during 2012-2013 at practices affiliated with 2 academic medical centers. Data were screened using the system to detect outliers suggestive of potential medication errors. A sample of 300 charts was selected for review from the 15 693 alerts generated. A coding system was developed and codes assigned based on chart review to reflect the accuracy, validity, and clinical value of the alerts. ::: ::: ::: Results ::: Three-quarters of the chart-reviewed alerts generated by the screening system were found to be valid in which potential medication errors were identified. Of these valid alerts, the majority (75.0%) were found to be clinically useful in flagging potential medication errors or issues. ::: ::: ::: Discussion ::: A clinical decision support (CDS) system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated potentially useful alerts with a modest rate of false positives. The performance of such a surveillance and alerting system is critically dependent on the quality and completeness of the underlying data. ::: ::: ::: Conclusion ::: The screening system was able to generate alerts that might otherwise be missed with existing CDS systems and did so with a reasonably high degree of alert usefulness when subjected to review of patients' clinical contexts and details. <s> BIB009 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. © RSNA, 2017 Online supplemental material is available for this article. <s> BIB010 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features with the current case. The prsent study aimed to develop a vector model that could reduce planning time by applying the optimization parameters from those retrieved reference cases. Thirty-six VMA-SRT cases of brain metastasis (gender, male [n = 23], female [n = 13]; age range, 32 to 81 years old) were collected and used as a reference database. Another 10 VMA-SRT cases were planned with both conventional optimization and vector-model-supported optimization, following the oncologists' clinical dose prescriptions. Planning time and plan quality measures were compared using the 2-sided paired Wilcoxon signed rank test with a significance level of 0.05, with positive false discovery rate (pFDR) of less than 0.05. With vector-model-supported optimization, there was a significant reduction in the median planning time, a 40% reduction from 3.7 to 2.2 hours (p = 0.002, pFDR = 0.032), and for the number of iterations, a 30% reduction from 8.5 to 6.0 (p = 0.006, pFDR = 0.047). The quality of plans from both approaches was comparable. From these preliminary results, vector-model-supported optimization can expedite the optimization of VMA-SRT for brain metastasis while maintaining plan quality. <s> BIB011 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Introduction ::: The bone scan index (BSI) was introduced as a quantitative tool for tumor involvement in bone of patients with metastatic prostate cancer (mPCa). The computer-aided diagnosis device for BSI analysis EXINIboneBSI seems to represent technical progress for the quantitative assessment of bone involvement. But it is not yet clear if the automated BSI (aBSI) could contribute to improved evaluation of progression in patients under antiandrogens or chemotherapy in contrast to the visual interpretation and/or conventional biomarkers such as the prostate-specific antigen (PSA). <s> BIB012 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> BACKGROUND ::: Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue. Reasons for this failure include low contrast and noise in DXA images, bone shape variability, and inconsistent X-ray beam penetration and attenuation, which cause shadowing effects and person-to-person variation. ::: ::: ::: OBJECTIVE ::: To present a new method namely, a Pixel Label Decision Tree (PLDT), and test whether it can achieve higher accurate performance in femur segmentation in DXA imaging. ::: ::: ::: METHODS ::: PLDT involves mainly feature extraction and selection. Unlike photographic images, X-ray images include features on the surface and inside an object. In order to reveal hidden patterns in DXA images, PLDT generates seven new feature maps from existing high energy (HE) and low energy (LE) X-ray features and determines the best feature set for the model. The performance of PLDT in femur segmentation is compared with that of three widely used medical image segmentation algorithms, the Global Threshold (GT), Region Growing Threshold (RGT), and artificial neural networks (ANN). ::: ::: ::: RESULTS ::: PLDT achieved a higher accuracy of femur segmentation in DXA imaging (91.4%) than either GT (68.4%), RGT (76%) or ANN (84.4%). ::: ::: ::: CONCLUSIONS ::: The study demonstrated that PLDT outperformed other conventional segmentation techniques in segmenting DXA images. Improved segmentation should help accurate computation of BMD which later improves clinical diagnosis of osteoporosis. <s> BIB013 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> Abstract Background and objective: A new expert system is proposed to discriminate healthy people from people with Parkinson’s Disease (PD) in early stages by using Diadochokinesis tests. Methods: The system is based on temporal and spectral features extracted from the Voice Onset Time (VOT) segments of /ka/ syllables, whose boundaries are delimited by a novel algorithm. For comparison purposes, the approach is applied also to /pa/ and /ta/ syllables. In order to develop and validate the system, a voice recording database composed of 27 individuals diagnosed with PD and 27 healthy controls has been collected. This database reflects an average disease stage of 1.85 ± 0.55 according to Hoehn and Yahr scale. System design is based on feature extraction, feature selection and Support Vector Machine learning. Results: The novel VOT algorithm, based on a simple and computationally efficient approach, demonstrates accurate estimation of VOT boundaries on /ka/ syllables for both healthy and PD-affected speakers. The PD detection approach based on /k/ plosive consonant achieves the highest discrimination capability (92.2% using 10-fold cross-validation and 94.4% in the case of leave-one-out method) in comparison to the corresponding versions based on the other two plosives (/p/ and /t/). Conclusion: A high accuracy has been obtained on a database with a lower average disease stage than previous articulatory databases presented in the literature. <s> BIB014 </s> Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey <s> Data-driven Decision Support <s> This work was supported by the European Unions Horizon 2020 ::: research and innovation programme [grant number 689810, PEPPER]; the University of Girona [grant number MPCUdG2016]; and the Spanish MINECO [grant number DPI2013-47450-C21-R]. <s> BIB015
Out of the 75 retrieved papers, 49 can be categorized as data-driven decision support approaches . As regards to the addressed task, a large part of these contributions deals with prediction, intended as classification or regression [10-15, 17-19, 21-36, 38-42, 44-46, 48, 49, 52-54, 57] . One work is focused on association rule mining BIB004 , and one adopts statistics for risk analysis BIB005 . A set of papers deal with information extraction from natural language texts  BIB006 BIB007 , or more specifically from electronic patient record data [32, 37, 44 ,50 ,56 ]. The extracted information can then be exploited for classification purposes BIB006 , for statistical correlation and knowledge integration BIB008 BIB007 , or for outlier detection BIB009 . Retrieval and interpretation of similar complex data, such as time series, images, voice recordings, or radiotherapy plans are addressed in BIB002 BIB010  BIB011 BIB003 BIB013 BIB014 BIB012 . As regards to the adopted methodologies, interestingly, a large part of the classification approaches adopts more than one machine learning techniques [10-14, 16, 21, 22, 24-27, 33, 39, 42, 45, 48-51, 54, 57] , such as Support Vector Machines (SVM), Neural Networks (NN), Decision Trees, and Bayesian models, in synergy or in competition. The work in BIB015 , for instance, adopts machine learning to find out relevant Single Nucleotide Polymorphisms (SNPs) related to Type 2 diabetes, and to predict patient risk. The authors use the Random Forest technique to search for the most important attributes related to diabetes. SVM and Logistic Regression (LR) are also adopted. Their performances have been compared to that achieved by Random Forest. Moreover, the relevance of the attributes obtained through Random Forest has then been used to perform predictions with the k-Nearest Neighbour method, weighting the attributes in the similarity measure according to the relevance provided by Random Forest itself. Working on 677 subjects, Random Forest has outperformed all the other tested machine learning techniques in terms of prediction accuracy (0.853 with respect to 0.835 with LR and 0.825 with SVM on raw data), and in terms of the stability of the estimated relevance of the attributes. NN deserve a special consideration, since they are the key technique adopted in many of the retrieved papers [15, 17-19, 28-30, 35, 40, 41, 52, 58] . Indeed, image interpretation and classification are fields where NN/deep learning approaches work well BIB001 . As an example, the paper in BIB010 exploits a convolutional NN for detecting haemorrhage, mass effect, or hydrocephalus at non-contrast-enhanced head computed tomographic examinations, and for identifying suspected acute infarct. In a retrospective study analysing 2,583 representative images, the approach has provided promising results in detecting critical findings at non-contrast-enhanced head computed tomography. Suspected acute infarct detection has shown lower sensitivity (62%) with respect to hydrocephalus detection (80%), but higher specificity (96% with respect to 90%). The authors plan to conduct further investigation in a controlled and prospective clinical setting. The use of NN requires the choice of proper methods to train the network itself. The work in BIB002 , for instance, proposes a novel algorithm based on predator-prey particle swarm optimization, to train the weights of a rather simple NN architecture (a single-hidden layer NN). The approach, applied in the field of magnetic resonance image interpretation, has outperformed six state-of-the-art methods. Papers dealing with information extraction from text, images, time series, or voice recordings, adopt proper methodologies, such as natural language processing techniques (e.g., BIB008 ), NN (e.g., BIB012 ), or voice analysis BIB014 . Table 1 shows the distribution of the surveyed data-driven works by task and by methodology. 