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A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN. ALGORITHMS 2022. [DOI: 10.3390/a15060186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient’s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.
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Chu J, Dong W, Wang J, He K, Huang Z. Treatment effect prediction with adversarial deep learning using electronic health records. BMC Med Inform Decis Mak 2020; 20:139. [PMID: 33317502 PMCID: PMC7735418 DOI: 10.1186/s12911-020-01151-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 06/08/2020] [Indexed: 11/29/2022] Open
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.
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Affiliation(s)
- Jiebin Chu
- College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Wei Dong
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | | | - Kunlun He
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
| | - Zhengxing Huang
- College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.
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Mustaqeem A, Anwar SM, Majid M. A modular cluster based collaborative recommender system for cardiac patients. Artif Intell Med 2020; 102:101761. [PMID: 31980098 DOI: 10.1016/j.artmed.2019.101761] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 12/15/2018] [Accepted: 11/07/2019] [Indexed: 10/25/2022]
Abstract
In the last few years, hospitals have been collecting a large amount of health related digital data for patients. This includes clinical test reports, treatment updates and disease diagnosis. The information extracted from this data is used for clinical decisions and treatment recommendations. Among health recommender systems, collaborative filtering technique has gained a significant success. However, traditional collaborative filtering algorithms are facing challenges such as data sparsity and scalability, which leads to a reduction in system accuracy and efficiency. In a clinical setting, the recommendations should be accurate and timely. In this paper, an improvised collaborative filtering technique is proposed, which is based on clustering and sub-clustering. The proposed methodology is applied on a supervised set of data for four different types of cardiovascular diseases including angina, non-cardiac chest pain, silent ischemia, and myocardial infarction. The patient data is partitioned with respect to their corresponding disease class, which is followed by k-mean clustering, applied separately on each disease partition. A query patient once directed to the correct disease partition requires to get similarity scores from a reduced sub-cluster, thereby improving the efficiency of the system. Each disease partition has a separate process for recommendation, which gives rise to modularization and helps in improving scalability of the system. The experimental results demonstrate that the proposed modular clustering based recommender system reduces the spatial search domain for a query patient and the time required for providing accurate recommendations. The proposed system improves upon the accuracy of recommendations as demonstrated by the precision and recall values. This is significant for health recommendation systems particularly for those related to cardiovascular diseases.
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Affiliation(s)
- Anam Mustaqeem
- Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan
| | - Syed Muhammad Anwar
- Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan.
| | - Muhammad Majid
- Department of Computer Engineering, University of Engineering and Technology Taxila, Pakistan
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Karmen C, Gietzelt M, Knaup-Gregori P, Ganzinger M. Methods for a similarity measure for clinical attributes based on survival data analysis. BMC Med Inform Decis Mak 2019; 19:195. [PMID: 31638963 PMCID: PMC6805472 DOI: 10.1186/s12911-019-0917-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 09/11/2019] [Indexed: 02/06/2023] Open
Abstract
Background Case-based reasoning is a proven method that relies on learned cases from the past for decision support of a new case. The accuracy of such a system depends on the applied similarity measure, which quantifies the similarity between two cases. This work proposes a collection of methods for similarity measures especially for comparison of clinical cases based on survival data, as they are available for example from clinical trials. Methods Our approach is intended to be used in scenarios, where it is of interest to use longitudinal data, such as survival data, for a case-based reasoning approach. This might be especially important, where uncertainty about the ideal therapy decision exists. The collection of methods consists of definitions of the local similarity of nominal as well as numeric attributes, a calculation of attribute weights, a feature selection method and finally a global similarity measure. All of them use survival time (consisting of survival status and overall survival) as a reference of similarity. As a baseline, we calculate a survival function for each value of any given clinical attribute. Results We define the similarity between values of the same attribute by putting the estimated survival functions in relation to each other. Finally, we quantify the similarity by determining the area between corresponding curves of survival functions. The proposed global similarity measure is designed especially for cases from randomized clinical trials or other collections of clinical data with survival information. Overall survival can be considered as an eligible and alternative solution for similarity calculations. It is especially useful, when similarity measures that depend on the classic solution-describing attribute “applied therapy” are not applicable. This is often the case for data from clinical trials containing randomized arms. Conclusions In silico evaluation scenarios showed that the mean accuracy of biomarker detection in k = 10 most similar cases is higher (0.909–0.998) than for competing similarity measures, such as Heterogeneous Euclidian-Overlap Metric (0.657–0.831) and Discretized Value Difference Metric (0.535–0.671). The weight calculation method showed a more than six times (6.59–6.95) higher weight for biomarker attributes over non-biomarker attributes. These results suggest that the similarity measure described here is suitable for applications based on survival data.
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Affiliation(s)
- Christian Karmen
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Matthias Gietzelt
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.,Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Petra Knaup-Gregori
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Matthias Ganzinger
- Heidelberg University Hospital, Institute of Medical Biometry and Informatics, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany.
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Rigla M, García-Sáez G, Pons B, Hernando ME. Artificial Intelligence Methodologies and Their Application to Diabetes. J Diabetes Sci Technol 2018; 12:303-310. [PMID: 28539087 PMCID: PMC5851211 DOI: 10.1177/1932296817710475] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.
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Affiliation(s)
- Mercedes Rigla
- Endocrinology and Nutrition Department, Parc Tauli University Hospital, Sabadell, Spain
- Mercedes Rigla, MD, PhD, Endocrinology and Nutrition Department, Parc Tauli University Hospital, I3PT, Autonomous University of Barcelona, Parc Taulí, 1, Sabadell, 08208, Spain.
| | - Gema García-Sáez
- Bioengineering and Telemedicine Centre, Universidad Politécnica de Madrid, Spain
- CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
| | - Belén Pons
- Endocrinology and Nutrition Department, Parc Tauli University Hospital, Sabadell, Spain
| | - Maria Elena Hernando
- Bioengineering and Telemedicine Centre, Universidad Politécnica de Madrid, Spain
- CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain
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Quaglini S, Sacchi L, Lanzola G, Viani N. Personalization and Patient Involvement in Decision Support Systems: Current Trends. Yearb Med Inform 2017; 10:106-18. [PMID: 26293857 DOI: 10.15265/iy-2015-015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care. METHODS We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support. RESULTS We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness. CONCLUSIONS Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.
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Affiliation(s)
- S Quaglini
- Silvana Quaglini, Department of Electrical, Computer, and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy, Tel: +39 0382 985058, Fax: +39 0382 985060, E-mail:
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Huang Z, Dong W, Ji L, Duan H. Outcome Prediction in Clinical Treatment Processes. J Med Syst 2015; 40:8. [DOI: 10.1007/s10916-015-0380-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 10/09/2015] [Indexed: 11/28/2022]
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Azadeh A, Hosseinabadi Farahani M, Torabzadeh S, Baghersad M. Scheduling prioritized patients in emergency department laboratories. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 117:61-70. [PMID: 25214024 DOI: 10.1016/j.cmpb.2014.08.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 07/18/2014] [Accepted: 08/20/2014] [Indexed: 06/03/2023]
Abstract
This research focuses on scheduling patients in emergency department laboratories according to the priority of patients' treatments, determined by the triage factor. The objective is to minimize the total waiting time of patients in the emergency department laboratories with emphasis on patients with severe conditions. The problem is formulated as a flexible open shop scheduling problem and a mixed integer linear programming model is proposed. A genetic algorithm (GA) is developed for solving the problem. Then, the response surface methodology is applied for tuning the GA parameters. The algorithm is tested on a set of real data from an emergency department. Simulation results show that the proposed algorithm can significantly improve the efficiency of the emergency department by reducing the total waiting time of prioritized patients.
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Affiliation(s)
- A Azadeh
- School of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran.
| | - M Hosseinabadi Farahani
- School of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
| | - S Torabzadeh
- School of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
| | - M Baghersad
- School of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Tehran, Iran
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Huang Z, Dong W, Bath P, Ji L, Duan H. On mining latent treatment patterns from electronic medical records. Data Min Knowl Discov 2014. [DOI: 10.1007/s10618-014-0381-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Huang Z, Dong W, Ji L, Gan C, Lu X, Duan H. Discovery of clinical pathway patterns from event logs using probabilistic topic models. J Biomed Inform 2013; 47:39-57. [PMID: 24076435 DOI: 10.1016/j.jbi.2013.09.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Revised: 09/05/2013] [Accepted: 09/07/2013] [Indexed: 11/30/2022]
Abstract
Discovery of clinical pathway (CP) patterns has experienced increased attention over the years due to its importance for revealing the structure, semantics and dynamics of CPs, and to its usefulness for providing clinicians with explicit knowledge which can be directly used to guide treatment activities of individual patients. Generally, discovery of CP patterns is a challenging task as treatment behaviors in CPs often have a large variability depending on factors such as time, location and patient individual. Based on the assumption that CP patterns can be derived from clinical event logs which usually record various treatment activities in CP executions, this study proposes a novel approach to CP pattern discovery by modeling CPs using mixtures of an extension to the Latent Dirichlet Allocation family that jointly models various treatment activities and their occurring time stamps in CPs. Clinical case studies are performed to evaluate the proposed approach via real-world data sets recording typical treatment behaviors in patient careflow. The obtained results demonstrate the suitability of the proposed approach for CP pattern discovery, and indicate the promise in research efforts related to CP analysis and optimization.
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Affiliation(s)
- Zhengxing Huang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China
| | - Wei Dong
- Department of Cardiology, Chinese PLA General Hospital, China
| | - Lei Ji
- IT Department, Chinese PLA General Hospital, China
| | - Chenxi Gan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China
| | - Xudong Lu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China
| | - Huilong Duan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqing Building 510, Zheda Road 38#, Hangzhou, Zhejiang 310008, China.
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Meli CL, Khalil I, Tari Z. Load-sensitive dynamic workflow re-orchestration and optimisation for faster patient healthcare. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:1-14. [PMID: 24099624 DOI: 10.1016/j.cmpb.2013.06.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2012] [Revised: 06/18/2013] [Accepted: 06/27/2013] [Indexed: 06/02/2023]
Abstract
Hospital waiting times are considerably long, with no signs of reducing any-time soon. A number of factors including population growth, the ageing population and a lack of new infrastructure are expected to further exacerbate waiting times in the near future. In this work, we show how healthcare services can be modelled as queueing nodes, together with healthcare service workflows, such that these workflows can be optimised during execution in order to reduce patient waiting times. Services such as X-ray, computer tomography, and magnetic resonance imaging often form queues, thus, by taking into account the waiting times of each service, the workflow can be re-orchestrated and optimised. Experimental results indicate average waiting time reductions are achievable by optimising workflows using dynamic re-orchestration.
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Affiliation(s)
- Christopher L Meli
- School of Computer Science & Information Technology, RMIT University, Melbourne, Australia.
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Huang Z, Lu X, Duan H. Latent Treatment Pattern Discovery for Clinical Processes. J Med Syst 2013; 37:9915. [DOI: 10.1007/s10916-012-9915-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2012] [Accepted: 12/29/2012] [Indexed: 11/29/2022]
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