Пьяных Олег Станиславович
Факультет компьютерных наук
Профессиональные интересы
Должности
- Профессор — Факультет компьютерных наук, Департамент анализа данных и искусственного интеллекта
Био
- · Начал работать в НИУ ВШЭ в 2012 году.
- · Научно-педагогический стаж: 14 лет.
Образование
- 1998 · PhD: Университет штата Луизиана
- 1994 · Специалитет: Московский государственный университет им. М.В. Ломоносова, специальность «Математика, прикладная математика», квалификация «Математик»
Опыт работы
- · Преподаватель Медицинской школы при Гарвадском Университете и постоянный участник рабочей группы Международного Комитета DICOM (Индустриальный Стандарт создания, хранения, передачи и визуализации медицинских изображений и документов обследованных пациентов)
Награды и поощрения
- · Надбавка за публикацию в международном рецензируемом научном издании (2019–2021, 2017–2018)
- · Надбавка за статью в зарубежном рецензируемом журнале (2013–2015)
Идентификаторы исследователя
- ORCID:
0000-0002-9107-5432 - ResearcherID:
N-2456-2015 - Google Scholar: https://scholar.google.com/citations?hl=en&user=1vmkqacAAAAJ&view_op=list_works&sortby=pubdate
- Scopus AuthorID:
6603235403
Публикации (15)
Leveraging a Quality and Safety Continuous Process Improvement Framework to Increase Breast Cancer Screening Access
2025 в печати · ARTICLE · en
Purpose To apply a Quality and Safety Continuous Process Improvement approach guided by Continuous Quality Improvement and Plan-Do-Study-Act (PDSA) cycles to develop, refine and assess a digital reminder program’s effect on Screening Mammography Missed Care Opportunity (SM-MCO) rates. Methods Study conducted at two FQHCs and a mobile mammography unit. The pre-PDSA period was October 2020-June 2023, and the post-PDSA period was July 2023-January 2025. PDSA 1 launched a multilingual Short Messaging System (SMS) reminder across all sites. PDSA 2 standardized reminder process. PDSA 3 implemented an SM educational video. The primary outcome assessed the PDSA cycles’ effect on SM-MCO rates. The secondary outcome assessed digital engagement. QI SPC p-chart tracked appointment-level data. Univariate and logistic regression analyses assessed primary and secondary outcomes. Results 18,654 appointments were included in the analysis; average age was 56.8. (SD = 9.6 years) and 51.9% identified as Hispanic. The overall SM-MCO rate declined from 29.2% pre-PDSA to 26.9% post-PDSA (p Conclusion Although a modest decrease in overall SM-MCOs rate was observed, SM-MCO rates were higher among appointments that received SMS reminders but lower among appointments with digital engagement, underscoring the digital divide complexity. QI frameworks can continuously monitor and refine digital strategies to increase access to radiology.
Using Optimal Feature Selection and Continuous-Learning to Implement Efficient Model Arrays for Predicting Daily Clinical Radiology Workload
2025 · ARTICLE · en
Rationale and Objective Clinical workload can fluctuate daily in radiology practice. We sought to design, validate, and implement an efficient and sustainable machine learning model to forecast daily clinical image interpretation workload. Materials and Methods A year of radiology exam volume data at two academic medical centers was analyzed with an optimal feature selection algorithm and several machine learning models, to produce the most accurate and explainable prediction of the next weekday’s clinical workload. Continuous learning was used to maintain high model quality over time. Results After evaluating several AI models of differing complexity on a large set of 707 workflow features, a continuously learning linear regression model array was selected based on three optimal features: the current number of unread exams, the number of exams scheduled to be performed after 5 pm, and the number of exams scheduled to be performed the next day. The model array had an average R2 of 0.83 (IQR 0.13) across the tested radiology divisions; it significantly outperformed trivial estimates and provided an accurate daily prediction pattern. The solution was successfully implemented into an online dashboard, displaying the forecasted clinical volume as a percentile in reference to the past year’s daily clinical volume. Retraining the model on a weekly basis using live data resulted in high, and sometimes increased, model quality. Conclusion An AI model can be developed and implemented to forecast daily clinical radiology workload, as a practice management tool.
Impact of optimized and conventional facility designs on outpatient abdominal MRI workflow efficiency
2025 в печати · ARTICLE · en
Purpose: The goal of this study was to evaluate the outpatient workflow efficiency of an optimized facility (OF) compared to an established reference facility (RF) for abdominal magnetic resonance imaging (MRI). Methods: In this retrospective study, we analyzed 2,723 contrast-enhanced liver and prostate MRI examinations conducted between March 2022 and April 2024. All examinations were performed on 3T scanners (MAGNETOM Vida, Siemens Healthineers) at two different imaging facilities within our institution. The optimized facility featured a three-bay setup, with each bay consisting of one magnet, two dockable tables, and one dedicated preparation room, while the reference facility utilized a single scanner-single table setup with one dedicated preparation room. Workflow metrics were extracted from scanner logs and electronic health records. Three-way ANOVA and chi-square tests were used to assess the impact of facility design, body region, and date on workflow metrics. Results: The OF significantly reduced mean table turnaround times (4.6 min vs. 8.3 min, p p p p p p
Discrete scheduling and critical utilization
2024 · ARTICLE · en
Efficient scheduling is essential for optimizing resource allocation and robust system performance in a wide range of real-life applications. In most of these cases, the success of scheduling largely depends on one's ability to ensure that system resources can be utilized to their maximum capacity, yet without overloading the system. In this work, we study the problem of critical utilization and efficient scheduling by considering systems with discrete schedules, widely used in real-life workflows. Using an implementation-based approach, we introduce discrete scheduling by developing its analytic equations, which enables us to express the behavior of the scheduling metrics with respect to system utilization. Using this result, we define critical resource utilization and solve for its exact value as a function of schedule length. Finally, we compare our results with the equations from the classical queueing theory, and discuss their applicability. Our findings have immediate practical implications in developing robust schedules and controlling for optimal system performance.
Human knowledge models: Learning applied knowledge from the data
2022 · ARTICLE · en
Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI forms of processing and human ways of discovering and using knowledge. In this work, we introduce a new concept of “Human Knowledge Models” (HKMs), designed to reproduce human computational abilities. Departing from a vast body of cognitive research, we formalized the definition of HKMs into a new form of machine learning. Then, by training the models with human processing capabilities, we learned human-like knowledge, that humans can not only understand, but also compute, modify, and apply. We used several datasets from different applied fields to demonstrate the advantages of HKMs, including their high predictive power and resistance to noise and overfitting. Our results proved that HKMs can efficiently mine knowledge directly from the data and can compete with complex AI models in explaining the main data patterns. As a result, our study reveals the great potential of HKMs, particularly in the decision-making applications where “black box” models cannot be accepted. Moreover, this improves our understanding of how well human decision-making, modeled by HKMs, can approach the ideal solutions in real-life problems.
An alternative to the black box: Strategy learning
2022 · ARTICLE · en
In virtually any practical field or application, discovering and implementing near-optimal decision strategies is essential for achieving desired outcomes. Workflow planning is one of the most common and important problems of this kind, as sub-optimal decision-making may create bottlenecks and delays that decrease efficiency and increase costs. Recently, machine learning has been used to attack this problem, but unfortunately, most proposed solutions are “black box” algorithms with underlying logic unclear to humans. This makes them hard to implement and impossible to trust, significantly limiting their practical use. In this work, we propose an alternative approach: using machine learning to generate optimal, comprehensible strategies which can be understood and used by humans directly. Through three common decision-making problems found in scheduling, we demonstrate the implementation and feasibility of this approach, as well as its great potential to attain near-optimal results.
Modeling Human Perception of Image Quality
2018 · ARTICLE · en
Humans can determine image quality instantly and intuitively, but the mechanism of human perception of image quality is unknown. The purpose of this work was to identify the most important quantitative metrics responsible for the human perception of digital image quality. Digital images from two different datasets—CT tomography (MedSet) and scenic photographs of trees (TreeSet)—were presented in random pairs to unbiased human viewers. The observers were then asked to select the best-quality image from each image pair. The resulting human-perceived image quality (HPIQ) ranks were obtained from these pairwise comparisons with two different ranking approaches. Using various digital image quality metrics reported in the literature, we built two models to predict the observed HPIQ rankings, and to identify the most important HPIQ predictors. Evaluating the quality of our HPIQ models as the fraction of falsely predicted pairwise comparisons (inverted image pairs), we obtained 70–71% of correct HPIQ predictions for the first, and 73–76%for the second approach. Taking into account that 10–14% of inverted pairs were already present in the original rankings, limitations of the models, and only a few principal HPIQ predictors used, we find this result very satisfactory. We obtained a small set of most significant quantitative image metrics associated with the human perception of image quality. This can be used for automatic image quality ranking, machine learning, and quality-improvement algorithms.
How secure is your radiology department? Mapping digital radiology adoption and security worldwide
2016 · ARTICLE · en
OBJECTIVE: Despite the long history of digital radiology, one of its most critical aspects-information security-still remains extremely underdeveloped and poorly standardized. To study the current state of radiology security, we explored the worldwide security of medical image archives. MATERIALS AND METHODS: Using the DICOM data-transmitting standard, we implemented a highly parallel application to scan the entire World Wide Web of networked computers and devices, locating open and unprotected radiology servers. We used only legal and radiology-compliant tools. Our security-probing application initiated a standard DICOM handshake to remote computer or device addresses, and then assessed their security posture on the basis of handshake replies. RESULTS: The scan discovered a total of 2774 unprotected radiology or DICOM servers worldwide. Of those, 719 were fully open to patient data communications. Geolocation was used to analyze and rank our findings according to country utilization. As a result, we built maps and world ranking of clinical security, suggesting that even the most radiology-advanced countries have hospitals with serious security gaps. CONCLUSION: Despite more than two decades of active development and implementation, our radiology data still remains insecure. The results provided should be applied to raise awareness and begin an earnest dialogue toward elimination of the problem. The application we designed and the novel scanning approach we developed can be used to identify security breaches and to eliminate them before they are compromised. © American Roentgen Ray Society.
Can We Predict Patient Wait Time?
2015 в печати · ARTICLE · en
Purpose The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors. Methods To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data. Results We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed. Conclusions Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time.
Losing Images in Digital Radiology: More than You Think
2014 · ARTICLE · en
It is a common belief that the shift to digital imaging some 20 years ago helped medical image exchange and got rid of any potential image loss that was happening with printed image films. Unfortunately, this is not the case: despite the most recent advances in digital imaging, most hospitals still keep losing their imaging data, with these losses going completely unnoticed. As a result, not only does image loss affect the faith in digital imaging but it also affects patient diagnosis and daily quality of clinical work. This paper identifies the origins of invisible image losses, provides methods and procedures to detect image loss, and demonstrates modes of action that can be taken to stop the problem from happening.
Курсы (3)
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Big Data and Machine Learning in Healthcare · 3 раза
2025/2026, 2024/2025, 2023/2024 · Магистратура / Маго-лего · Анг
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01.04.02. Прикладная математика и информатика
2023/2024 · Магистратура · Анг
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Medical Informatics
2021/2022 · Магистратура · Анг