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Journal articles on the topic 'Personalized prediction'

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Author: Grafiati
Published: 1 June 2024

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1

Galetzka, Wolfgang, Bernd Kowall, Cynthia Jusi, Eva-Maria Huessler, and Andreas Stang. "Distance-Metric Learning for Personalized Survival Analysis." Entropy 25, no. 10 (September 30, 2023): 1404. http://dx.doi.org/10.3390/e25101404.

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Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can be explained by providing the user with the most influential data points for this prediction, i.e., the closest data points and their weights. The strengths and weaknesses in terms of predictive performance are highlighted on simulated data and an application of the method on two different real-world datasets of breast cancer patients shows its competitiveness with established methods.
2

Thoma, Clemens. "Personalized response prediction." Nature Reviews Gastroenterology & Hepatology 15, no. 11 (October 2, 2018): 657. http://dx.doi.org/10.1038/s41575-018-0072-z.

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Liu, Jie, Bin Liu, Yanchi Liu, Huipeng Chen, Lina Feng, Hui Xiong, and Yalou Huang. "Personalized Air Travel Prediction." ACM Transactions on Intelligent Systems and Technology 9, no. 3 (February 13, 2018): 1–26. http://dx.doi.org/10.1145/3078845.

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TAYEBI, MOHAMMAD A., UWE GLÄSSER, MARTIN ESTER, and PATRICIA L. BRANTINGHAM. "Personalized crime location prediction." European Journal of Applied Mathematics 27, no. 3 (April 28, 2016): 422–50. http://dx.doi.org/10.1017/s0956792516000140.

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Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.
5

Gusev, I. V., D. V. Gavrilov, R. E. Novitsky, T. Yu Kuznetsova, and S. A. Boytsov. "Improvement of cardiovascular risk assessment using machine learning methods." Russian Journal of Cardiology 26, no. 12 (October 25, 2021): 4618. http://dx.doi.org/10.15829/1560-4071-2021-4618.

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The increase in the prevalence of cardiovascular diseases (CVDs) specifies the importance of their prediction, the need for accurate risk stratification, preventive and treatment interventions. Large medical databases and technologies for their processing in the form of machine learning algorithms that have appeared in recent years have the potential to improve predictive accuracy and personalize treatment approaches to CVDs. The review examines the application of machine learning in predicting and identifying cardiovascular events. The role of this technology both in the calculation of total cardiovascular risk and in the prediction of individual diseases and events is discussed. We compared the predictive accuracy of current risk scores and various machine learning algorithms. The conditions for using machine learning and developing personalized tactics for managing patients with CVDs are analyzed.
6

Localio, A. Russell, Cynthia D. Mulrow, and Michael E. Griswold. "Advancing Personalized Medicine Through Prediction." Annals of Internal Medicine 172, no. 1 (November 12, 2019): 63. http://dx.doi.org/10.7326/m19-3010.

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Xu, Yanyu, Shenghua Gao, Junru Wu, Nianyi Li, and Jingyi Yu. "Personalized Saliency and Its Prediction." IEEE Transactions on Pattern Analysis and Machine Intelligence 41, no. 12 (December 1, 2019): 2975–89. http://dx.doi.org/10.1109/tpami.2018.2866563.

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Vassileva, Vessela. "Prostate cancer—personalized response prediction." Nature Reviews Clinical Oncology 6, no. 11 (November 2009): 618. http://dx.doi.org/10.1038/nrclinonc.2009.156.

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Lee, Chuan-Chun, Chia-Jui Yen, and Tsunglin Liu. "Prediction of personalized microRNA activity." Gene 518, no. 1 (April 2013): 101–6. http://dx.doi.org/10.1016/j.gene.2012.11.068.

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Chen, Rirong, Jieqi Zheng, Li Li, Chao Li, Kang Chao, Zhirong Zeng, Minhu Chen, and Shenghong Zhang. "Metabolomics facilitate the personalized management in inflammatory bowel disease." Therapeutic Advances in Gastroenterology 14 (January 2021): 175628482110644. http://dx.doi.org/10.1177/17562848211064489.

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Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.
11

Puranikath, Jagadevi, Fathima Farheen, Aruna Patil, and Aishwarya K. M. "Analysis and Prediction of Multiple Disease using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 3570–74. http://dx.doi.org/10.22214/ijraset.2024.62250.

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Abstract: This paper uses the machine learning methods for predicting diabetes, heart disease, and Parkinson's disease based on user-provided input data. Its primary goal is to enhance early disease detection and prompt medical intervention using personalized predictions. By training models on relevant datasets, accurate predictions are achieved across various diseases, expanding our understanding and predictive capabilities in healthcare. The research's broad scope contributes significantly to addressing multiple medical conditions, fostering a more holistic approach to healthcare delivery. Through data-driven insights, this tool aids in reducing medical costs by enabling early intervention and proactive management of medical conditions. Overall, this study highlights the potential of model in healthcare for disease pre-diction and underscores its role in advancing personalized medicine and improving clinical decision-making.
12

Anis, Hiba K., Gregory J. Strnad, Alison K. Klika, Alexander Zajichek, Kurt P. Spindler, Wael K. Barsoum, Carlos A. Higuera, and Nicolas S. Piuzzi. "Developing a personalized outcome prediction tool for knee arthroplasty." Bone & Joint Journal 102-B, no. 9 (September 1, 2020): 1183–93. http://dx.doi.org/10.1302/0301-620x.102b9.bjj-2019-1642.r1.

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Aims The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193.
13

Kyrochristos, Ioannis D., Demosthenes E. Ziogas, and Dimitrios H. Roukos. "Precision in personalized prediction-based medicine." Personalized Medicine 15, no. 6 (November 2018): 467–70. http://dx.doi.org/10.2217/pme-2018-0079.

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14

Zheng, Zibin, and Michael R. Lyu. "Personalized Reliability Prediction of Web Services." ACM Transactions on Software Engineering and Methodology 22, no. 2 (March 2013): 1–25. http://dx.doi.org/10.1145/2430545.2430548.

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Li, Juan, and Chandima Fernando. "Smartphone-based personalized blood glucose prediction." ICT Express 2, no. 4 (December 2016): 150–54. http://dx.doi.org/10.1016/j.icte.2016.10.001.

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Wang, Yan, Xinyu Bao, Song Zhang, Lin Yang, Guoli Liu, Yimin Yang, Xuwen Li, et al. "Fetal growth prediction: Establishing fetal growth prediction curves in the second trimester." Technology and Health Care 29 (March 25, 2021): 345–50. http://dx.doi.org/10.3233/thc-218032.

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BACKGROUND: Monitoring fetal weight during pregnancy has a guiding role in prenatal care. OBJECTIVE: To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. METHODS: (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. RESULTS: (1) Delivery gestational age, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P< 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman. CONCLUSIONS: A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring.
17

Pei, Zejun, Manhong Shi, Junping Guo, and Bairong Shen. "Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives." Current Topics in Medicinal Chemistry 20, no. 18 (August 24, 2020): 1640–50. http://dx.doi.org/10.2174/1568026620666200603105002.

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Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.
18

Cai, Weihong, Xin Du, and Jianlong Xu. "A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization." Sensors 19, no. 12 (June 19, 2019): 2749. http://dx.doi.org/10.3390/s19122749.

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Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.
19

Zhang, Zhijun, Gongwen Xu, and Pengfei Zhang. "Research on E-Commerce Platform-Based Personalized Recommendation Algorithm." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5160460.

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Aiming at data sparsity and timeliness in traditional E-commerce collaborative filtering recommendation algorithms, when constructing user-item rating matrix, this paper utilizes the feature that commodities in E-commerce system belong to different levels to fill in nonrated items by calculating RF/IRF of the commodity’s corresponding level. In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. The experimental results on real dataset verify the feasibility and validity of the algorithm and it owns higher predicting accuracy compared with present recommendation algorithms.
20

Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (December 20, 2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.
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Lococo, Filippo, Galal Ghaly, Marco Chiappetta, Sara Flamini, Jessica Evangelista, Emilio Bria, Alessio Stefani, et al. "Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review." Cancers 16, no. 10 (May 10, 2024): 1832. http://dx.doi.org/10.3390/cancers16101832.

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Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.
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Babu, Mr M. Jeevan. "Mental Health Prediction Using Catboost Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 3449–53. http://dx.doi.org/10.22214/ijraset.2024.59219.

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Abstract: This study investigates the application of the CatBoost algorithm in predicting mental health outcomes using Python programming language. Mental health prediction is a critical area of research due to its significant impact on individuals and society. Traditional predictive modeling techniques often encounter challenges in handling complex and highdimensional data inherent in mental health datasets. CatBoost , a state- of-the-art gradient boosting algorithm, has shown promise in effectively addressing these challenges by handling categorical variables seamlessly and exhibiting robust performance in various domains. Leveraging its powerful capabilities, this study aims to develop predictive models for mental health outcomes utilizing a comprehensive dataset encompassing diverse socio- demographic, behavioural , and clinical factors. The predictive performance of the CatBoost algorithm will be evaluated and compared against other commonly used machine learning algorithms, demonstrating its effectiveness in accurately predicting mental health outcomes. This research contributes to the advancement of predictive modeling in mental health research and holds potential implications for personalized interventions and resource allocation in mental healthcare systems
23

Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (October 1, 2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.

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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Information on HBV and HCV infection status, liver function test, AFT, family history of liver cancer, demographic characteristics, lifestyle variables and relevant biomarkers were collected. The occurrence of HCC was ascertained by the linkage of the nationwide cancer registry till the end of 2007. Bayesian clinical reasoning model was adopted by constructing the basic model taken as the prior model for average-risk subject. We then updated the basic model by sequentially incorporating other risk factors for HCC encrypted in the likelihood ratio to form posterior probability that was used for predicting individual risk of HCC. Results: By dint of Bayesian clinical reasoning model with a step-by-step update of the risk of HCC for the sequentially obtained information, a 57-year-old man was predicted to yield 0.69% of HCC risk with the prior model. After history-taking of having hepatitis B carrier (likelihood ratio [LR]: 3.65), family history (LR: 1.43), and no alcohol drinking (LR: 0.89), the posterior risk for HCC was enhanced up to 3.13%. After further biochemical examination, the updated risk of HCC for a man [the following biomarkers [ALT = 30 IU/L (LR: 0.78), AST = 56 IU/L (LR: 8.99), platelets = (203 × /μL) (unit cube of ten) (LR: 0.55)] was increase to 11.07%. Conclusion: We proposed a individually tailored prediction model for HCC by incorporating routine information with a sequential Bayesian clinical reasoning approach.
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Xu, Ziqi, Jingwen Zhang, Jacob Greenberg, Madelyn Frumkin, Saad Javeed, Justin K. Zhang, Braeden Benedict, et al. "Predicting Multi-dimensional Surgical Outcomes with Multi-modal Mobile Sensing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 2 (May 13, 2024): 1–30. http://dx.doi.org/10.1145/3659628.

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Pre-operative prediction of post-surgical recovery for patients is vital for clinical decision-making and personalized treatments, especially with lumbar spine surgery, where patients exhibit highly heterogeneous outcomes. Existing predictive tools mainly rely on traditional Patient-Reported Outcome Measures (PROMs), which fail to capture the long-term dynamics of patient conditions before the surgery. Moreover, existing studies focus on predicting a single surgical outcome. However, recovery from spine surgery is multi-dimensional, including multiple distinctive but interrelated outcomes, such as pain interference, physical function, and quality of recovery. In recent years, the emergence of smartphones and wearable devices has presented new opportunities to capture longitudinal and dynamic information regarding patients' conditions outside the hospital. This paper proposes a novel machine learning approach, Multi-Modal Multi-Task Learning (M3TL), using smartphones and wristbands to predict multiple surgical outcomes after lumbar spine surgeries. We formulate the prediction of pain interference, physical function, and quality of recovery as a multi-task learning (MTL) problem. We leverage multi-modal data to capture the static and dynamic characteristics of patients, including (1) traditional features from PROMs and Electronic Health Records (EHR), (2) Ecological Momentary Assessment (EMA) collected from smartphones, and (3) sensing data from wristbands. Moreover, we introduce new features derived from the correlation of EMA and wearable features measured within the same time frame, effectively enhancing predictive performance by capturing the interdependencies between the two data modalities. Our model interpretation uncovers the complementary nature of the different data modalities and their distinctive contributions toward multiple surgical outcomes. Furthermore, through individualized decision analysis, our model identifies personal high risk factors to aid clinical decision making and approach personalized treatments. In a clinical study involving 122 patients undergoing lumbar spine surgery, our M3TL model outperforms a diverse set of baseline methods in predictive performance, demonstrating the value of integrating multi-modal data and learning from multiple surgical outcomes. This work contributes to advancing personalized peri-operative care with accurate pre-operative predictions of multi-dimensional outcomes.
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Park, Hayoung, Se Young Jung, Min Kyu Han, Yeonhoon Jang, Yeo Rae Moon, Taewook Kim, Soo-Yong Shin, and Hee Hwang. "Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management." Journal of Personalized Medicine 14, no. 3 (March 18, 2024): 316. http://dx.doi.org/10.3390/jpm14030316.

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This study investigates the feasibility of accurately predicting adverse health events without relying on costly data acquisition methods, such as laboratory tests, in the era of shifting healthcare paradigms towards community-based health promotion and personalized preventive healthcare through individual health risk assessments (HRAs). We assessed the incremental predictive value of four categories of predictor variables—demographic, lifestyle and family history, personal health device, and laboratory data—organized by data acquisition costs in the prediction of the risks of mortality and five chronic diseases. Machine learning methodologies were employed to develop risk prediction models, assess their predictive performance, and determine feature importance. Using data from the National Sample Cohort of the Korean National Health Insurance Service (NHIS), which includes eligibility, medical check-up, healthcare utilization, and mortality data from 2002 to 2019, our study involved 425,148 NHIS members who underwent medical check-ups between 2009 and 2012. Models using demographic, lifestyle, family history, and personal health device data, with or without laboratory data, showed comparable performance. A feature importance analysis in models excluding laboratory data highlighted modifiable lifestyle factors, which are a superior set of variables for developing health guidelines. Our findings support the practicality of precise HRAs using demographic, lifestyle, family history, and personal health device data. This approach addresses HRA barriers, particularly for healthy individuals, by eliminating the need for costly and inconvenient laboratory data collection, advancing accessible preventive health management strategies.
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Toivonen, Jarkko, Yrjö Koski, Esa Turkulainen, Femmeke Prinsze, Pietro della Briotta Parolo, Markus Heinonen, and Mikko Arvas. "Prediction and impact of personalized donation intervals." Vox Sanguinis 117, no. 4 (November 26, 2021): 504–12. http://dx.doi.org/10.1111/vox.13223.

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Bao, Naren, Alexander Carballo, Chiyomi Miyajima, Eijiro Takeuchi, and Kazuya Takeda. "Personalized Subjective Driving Risk: Analysis and Prediction." Journal of Robotics and Mechatronics 32, no. 3 (June 20, 2020): 503–19. http://dx.doi.org/10.20965/jrm.2020.p0503.

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Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.
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Jiang, Miao, Yi Fang, Huangming Xie, Jike Chong, and Meng Meng. "User click prediction for personalized job recommendation." World Wide Web 22, no. 1 (April 23, 2018): 325–45. http://dx.doi.org/10.1007/s11280-018-0568-z.

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Liu, Jin-Hu, Yu-Xiao Zhu, and Tao Zhou. "Improving personalized link prediction by hybrid diffusion." Physica A: Statistical Mechanics and its Applications 447 (April 2016): 199–207. http://dx.doi.org/10.1016/j.physa.2015.12.036.

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Fan, Yali, Zhen Tu, Yong Li, Xiang Chen, Hui Gao, Lin Zhang, Li Su, and Depeng Jin. "Personalized Context-aware Collaborative Online Activity Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (December 11, 2019): 1–28. http://dx.doi.org/10.1145/3369829.

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Fan, Zipei, Xuan Song, Renhe Jiang, Quanjun Chen, and Ryosuke Shibasaki. "Decentralized Attention-based Personalized Human Mobility Prediction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, no. 4 (December 11, 2019): 1–26. http://dx.doi.org/10.1145/3369830.

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Zeevi, David, Tal Korem, Niv Zmora, David Israeli, Daphna Rothschild, Adina Weinberger, Orly Ben-Yacov, et al. "Personalized Nutrition by Prediction of Glycemic Responses." Cell 163, no. 5 (November 2015): 1079–94. http://dx.doi.org/10.1016/j.cell.2015.11.001.

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Vinarti, Retno A., and Lucy M. Hederman. "A personalized infectious disease risk prediction system." Expert Systems with Applications 131 (October 2019): 266–74. http://dx.doi.org/10.1016/j.eswa.2019.04.042.

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., Sangeeta. "Pharmacogenomics: Personalized medicine and drug response prediction." Pharma Innovation 8, no. 1 (January 1, 2019): 845–48. http://dx.doi.org/10.22271/tpi.2019.v8.i1n.25487.

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Lo, Adeline, Herman Chernoff, Tian Zheng, and Shaw-Hwa Lo. "Why significant variables aren’t automatically good predictors." Proceedings of the National Academy of Sciences 112, no. 45 (October 26, 2015): 13892–97. http://dx.doi.org/10.1073/pnas.1518285112.

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Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.
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Alharbi, Eman, Asma Cherif, and Farrukh Nadeem. "Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation." Smart Cities 6, no. 5 (October 20, 2023): 2910–31. http://dx.doi.org/10.3390/smartcities6050130.

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Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.
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Toffanin, Chiara, Eleonora Maria Aiello, Claudio Cobelli, and Lalo Magni. "Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions." Journal of Diabetes Science and Technology 13, no. 6 (October 23, 2019): 1008–16. http://dx.doi.org/10.1177/1932296819880864.

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Background: The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial. Methods: A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs. Results: The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson’s correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results. Conclusion: The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.
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An, Ziyan, Taylor T. Johnson, and Meiyi Ma. "Formal Logic Enabled Personalized Federated Learning through Property Inference." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 10882–90. http://dx.doi.org/10.1609/aaai.v38i10.28962.

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Recent advancements in federated learning (FL) have greatly facilitated the development of decentralized collaborative applications, particularly in the domain of Artificial Intelligence of Things (AIoT). However, a critical aspect missing from the current research landscape is the ability to enable data-driven client models with symbolic reasoning capabilities. Specifically, the inherent heterogeneity of participating client devices poses a significant challenge, as each client exhibits unique logic reasoning properties. Failing to consider these device-specific specifications can result in critical properties being missed in the client predictions, leading to suboptimal performance. In this work, we propose a new training paradigm that leverages temporal logic reasoning to address this issue. Our approach involves enhancing the training process by incorporating mechanically generated logic expressions for each FL client. Additionally, we introduce the concept of aggregation clusters and develop a partitioning algorithm to effectively group clients based on the alignment of their temporal reasoning properties. We evaluate the proposed method on two tasks: a real-world traffic volume prediction task consisting of sensory data from fifteen states and a smart city multi-task prediction utilizing synthetic data. The evaluation results exhibit clear improvements, with performance accuracy improved by up to 54% across all sequential prediction models.
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Giglia, Giuseppe, Giuditta Gambino, and Pierangelo Sardo. "Through Predictive Personalized Medicine." Brain Sciences 10, no. 9 (August 28, 2020): 594. http://dx.doi.org/10.3390/brainsci10090594.

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Neuroblastoma (NBM) is a deadly form of solid tumor mostly observed in the pediatric age. Although survival rates largely differ depending on host factors and tumor-related features, treatment for clinically aggressive forms of NBM remains challenging. Scientific advances are paving the way to improved and safer therapeutic protocols, and immunotherapy is quickly rising as a promising treatment that is potentially safer and complementary to traditionally adopted surgical procedures, chemotherapy and radiotherapy. Improving therapeutic outcomes requires new approaches to be explored and validated. In-silico predictive models based on analysis of a plethora of data have been proposed by Lombardo et al. as an innovative tool for more efficacious immunotherapy against NBM. In particular, knowledge gained on intracellular signaling pathways linked to the development of NBM was used to predict how the different phenotypes could be modulated to respond to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. Prediction or forecasting are important targets of artificial intelligence and machine learning. Hopefully, similar systems could provide a reliable opportunity for a more targeted approach in the near future.
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Nandedkar, Ameya, Chaitanya Gadve, Chirag Gowda, Shashwat Deep, and Rahesha Mulla. "Athlete Performance Prediction Using Random Forest." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 2370–77. http://dx.doi.org/10.22214/ijraset.2024.62112.

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Abstract: Fitness and health applications are increasingly being integrated into athletes' training routines, providing new opportunities for personalized performance optimization. This research paper investigates the utilization of smartwatch metrics and machine learning algorithms for predicting athlete performance, focusing on metrics such as power. Through a comprehensive analysis of collected data and employing advanced machine learning techniques, the study aims to provide insights into the predictive capabilities of smartwatch data in the realm of sports science. The introduction of a user-friendly athlete interface further enhances the accessibility and usability of this innovative technology. Smartwatch metrics combined with machine learning algorithms offer a potent toolset for predicting athlete performance. Smartwatches collect vast amounts of data on an athlete's biometrics, such as heart rate, activity levels, and more. Machine learning algorithms analyse this data to uncover patterns, correlations, and trends that are often imperceptible to human observation. By training models on historical data from athletes and their performances, these algorithms can make predictions about future performance, injury risk, optimal training schedules, and even suggest personalized strategies for improvement. This fusion of technology enables coaches and athletes to make data-driven decisions, optimize training regimens, and enhance overall performance while minimizing the risk of injury.
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Romero-Garmendia, Irati, and Koldo Garcia-Etxebarria. "From Omic Layers to Personalized Medicine in Colorectal Cancer: The Road Ahead." Genes 14, no. 7 (July 11, 2023): 1430. http://dx.doi.org/10.3390/genes14071430.

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Colorectal cancer is a major health concern since it is a highly diagnosed cancer and the second cause of death among cancers. Thus, the most suitable biomarkers for its diagnosis, prognosis, and treatment have been studied to improve and personalize the prevention and clinical management of colorectal cancer. The emergence of omic techniques has provided a great opportunity to better study CRC and make personalized medicine feasible. In this review, we will try to summarize how the analysis of the omic layers can be useful for personalized medicine and the existing difficulties. We will discuss how single and multiple omic layer analyses have been used to improve the prediction of the risk of CRC and its outcomes and how to overcome the challenges in the use of omic layers in personalized medicine.
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Sánchez-Herrero, Sergio, Laura Calvet, and Angel A. Juan. "Machine Learning Models for Predicting Personalized Tacrolimus Stable Dosages in Pediatric Renal Transplant Patients." BioMedInformatics 3, no. 4 (October 14, 2023): 926–47. http://dx.doi.org/10.3390/biomedinformatics3040057.

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Tacrolimus, characterized by a narrow therapeutic index, significant toxicity, adverse effects, and interindividual variability, necessitates frequent therapeutic drug monitoring and dose adjustments in renal transplant recipients. This study aimed to compare machine learning (ML) models utilizing pharmacokinetic data to predict tacrolimus blood concentration. This prediction underpins crucial dose adjustments, emphasizing patient safety. The investigation focuses on a pediatric cohort. A subset served as the derivation cohort, creating the dose-prediction algorithm, while the remaining data formed the validation cohort. The study employed various ML models, including artificial neural network, RandomForestRegressor, LGBMRegressor, XGBRegressor, AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, KNeighborsRegressor, and support vector regression, and their performances were compared. Although all models yielded favorable fit outcomes, the ExtraTreesRegressor (ETR) exhibited superior performance. It achieved measures of −0.161 for MPE, 0.995 for AFE, 1.063 for AAFE, and 0.8 for R2, indicating accurate predictions and meeting regulatory standards. The findings underscore ML’s predictive potential, despite the limited number of samples available. To address this issue, resampling was utilized, offering a viable solution within medical datasets for developing this pioneering study to predict tacrolimus trough concentration in pediatric transplant recipients.
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Ughade, Ashwini Arun. "Personalized Location Recommendation System Personalized Location Recommendation System." International Journal of Applied Evolutionary Computation 10, no. 1 (January 2019): 49–58. http://dx.doi.org/10.4018/ijaec.2019010104.

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Location acquisition and wireless communication technologies are growing in location-based social networks. With the rapid development of location-based social networks (LBSNs), location recommendation has become an important for helping users to discover interesting locations. Most current studies on spatial item recommendations do not consider the sequential influence of locations. The authors proposed a personalized location recommendation system as a probabilistic generative model that aims to mimic the process of human decision-making when visiting locations. In this system, three tasks are involved, such as: extracting user's personal interests; extracting sequential influence; and combining them into unified networks. This system utilizes data collected from LBSNs to model a user's behavior and locations with real datasets, and it determines a user's preferred locations using collaborative filtering and a Locality Sensitive Hashing (ALSH) technique. It overcomes the challenges of the user's check-in data in LBSNs having a low sampling rate in both space and time and a huge prediction space.
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DOSAY-AKBULUT, Mine. "A Review on Determination and Future of the Predictive and Personalized Medicine." International Journal of Biology 8, no. 1 (November 11, 2015): 32. http://dx.doi.org/10.5539/ijb.v8n1p32.

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<p class="1Body">Medicine contents’ have extended to predictive, personalized, preventive and participatory medicine (P4). ‘Personalized medicine' focuses on the prediction of potential benefits or risks for individuals as possible as in detailed. Biomarker discovery, biocomputing and nanotechnology have opened a new horizon on ‘personalized medicine’ (including disease detection, diagnosis and therapy by using individual's molecular profile) and ‘predictive medicine’ (to predict disease development, progression and clinical outcome, by using the genetic and molecular information).</p><p class="1Body">Personalized medicine can be applied to a lot of different areas. P4 medicine, based on use of marker-assisted diagnosis and targeted therapies, comes from an individual's molecular profile, will form a new way on drugs development and medicine administration. Genetic screening aimed to identify carrier and affected individuals in a particular population. Molecular diagnostic test, including genome-derived tests are getting more attention within the medicine with genotyping, RNA expression analyses, metabolic profiling, and other biomarkers. Genomics research has getting more attention on the biomedical research, translational science, and personalized medicine; divided into 3 main parts: 1) genomics to biology, 2) genomics to health, and 3) genomics to society.</p><p class="1Body">We conducted a literature search via PubMed databases with using “personalized medicine”, and “application areas of P4” keywords, and summarized some of new studies.</p><p class="1Body">Personalized medicine is described as an individualized treatment based on the individual's genetic variants. In other words, “for predicting health, preventing and preempting disease, and personalizing treatment depending on the each person’ unique biology", has a speedy improvement.</p>
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Shreve, Jacob Tyler, Sarah Lee, Christina Felix, Rachel Benish Shirley, Cameron Beau Hilton, Nathan Radakovich, James Stevenson, Alberto J. Montero, and Aziz Nazha. "A personalized prediction model for hospital readmission risk for cancer patients." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 7057. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.7057.

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7057 Background: Cancer patients (pts) are at high risk of unplanned hospital readmissions. Predicting which cancer patients are at higher risk of readmission would improve post-discharge follow-up/navigation, decrease cost, and improve pt outcomes. Methods: We conducted a retrospective cohort study of non-surgical cancer pts hospitalized at our center between 12/2014 to 7/2018. A machine learning algorithm was trained on 348 medical, sociodemographic and cancer-specific variables with a total of 1,801,944 data points. The cohort was randomly divided into training (80%) and validation (20%) subsets. Prediction performance was measured by area under the receiver operator characteristic curve (AUC). Results: A total of 5,178 hospitalizations were included, of which 45.1% were women, and 27.6% experienced an unplanned readmission within 30 days. The most frequently represented cancers were hematologic malignancies (30.5%), followed by GI (18.1%), lung (13.7%), and GU (10.9%). Significant variables that impacted the algorithm decision are ranked from the most to the least important, including: days from last admission; planned index chemotherapy admission; number of vascular access lines, drains, and airways in use; length of stay; cancer diagnosis; total ED visits in past 6 months; age; discharge lab values (sodium, albumin, alkaline phosphatase, bilirubin, platelets); number of prior admissions; and discharge disposition. The AUC for the validation subset was 0.80. To ease the translation of this model into the clinic, we developed a web application whereby users can supply the aforementioned variables to the model and receive a personalized prediction that highlights those variables most affecting a subject’s readmission risk status: www.Cancer-Readmission.com. Conclusions: A cancer-specific readmission risk model with high AUC for 30-days unplanned readmission has been developed. The model is embedded in a freely available web application that provides personalized, patient-specific predictions. Programs that integrate this model can identify cancer patients with a greater risk for unplanned hospital readmission, thus providing a personalized approach to prevent future unplanned readmissions.
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Ma, Liantao, Chaohe Zhang, Yasha Wang, Wenjie Ruan, Jiangtao Wang, Wen Tang, Xinyu Ma, Xin Gao, and Junyi Gao. "ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 833–40. http://dx.doi.org/10.1609/aaai.v34i01.5428.

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Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not suitable for all conditions. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be effectively captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. The medical findings extracted by ConCare are also empirically confirmed by human experts and medical literature.
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Nagraj, Shruthi, and Blessed Prince Palayyan. "Personalized E-commerce based recommendation systems using deep-learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 610. http://dx.doi.org/10.11591/ijai.v13.i1.pp610-618.

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As technology is surpassing each day, with the variation of personalized drifts relevant to the explicit behavior of users using the internet. Recommendation systems use predictive mechanisms like predicting a rating that a customer could give on a specific item. This establishes a ranked list of items according to the preferences each user makes concerning exhibiting personalized recommendations. The existing recommendation techniques are efficient in systematically creating recommendation techniques. This approach encounters many challenges such as determining the accuracy, scalability, and data sparsity. Recently deep learning attains significant research to enhance the performance to improvise feature specification in learning the efficiency of retrieving the necessary information as well as a recommendation system approach. Here, we provide a thorough review of the deep-learning mechanism focused on the learning-rates-based prediction approach modeled to articulate the widespread summary for the state-of-art techniques. The novel techniques ensure the incorporation of innovative perspectives to pertain to the unique and exciting growth in this field.
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Marko, Nicholas F., Robert J. Weil, Jason L. Schroeder, Frederick F. Lang, Dima Suki, and Raymond E. Sawaya. "Extent of Resection of Glioblastoma Revisited: Personalized Survival Modeling Facilitates More Accurate Survival Prediction and Supports a Maximum-Safe-Resection Approach to Surgery." Journal of Clinical Oncology 32, no. 8 (March 10, 2014): 774–82. http://dx.doi.org/10.1200/jco.2013.51.8886.

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Purpose Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy. Patients and Methods We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution. Results AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival. Conclusion Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.
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Xing, Wanli, and Dongping Du. "Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention." Journal of Educational Computing Research 57, no. 3 (March 15, 2018): 547–70. http://dx.doi.org/10.1177/0735633118757015.

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Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.
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Ouzounoglou, Eleftherios, Eleni Kolokotroni, Martin Stanulla, and Georgios S. Stamatakos. "A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator." Interface Focus 8, no. 1 (December 15, 2017): 20160163. http://dx.doi.org/10.1098/rsfs.2016.0163.

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Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.
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