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An Actionable Expert-System algorithm to Support Nurse-Led Cancer Survivorship Care: algorithm development Study

JMIR CANCER 2023年 9卷 e44332页

作者： Pfisterer, Kaylen J. Lohani, Raima Janes, Elizabeth Ng, Denise Wang, Dan Bryant-Lukosius, Denise Rendon, Ricardo Berlin, Alejandro Bender, Jacqueline Brown, Ian Feifer, Andrew Gotto, Geoffrey Saha, Shumit Cafazzo, Joseph A. Pham, Quynh Univ Hlth Network Techna Inst Ctr Digital Therapeut Toronto ON Canada Univ Waterloo Dept Syst Design Engn Waterloo ON Canada McMaster Univ Sch Nursing Hamilton ON Canada Queen Elizabeth 2 Hlth Sci Ctr Dept Urol Halifax NS Canada Univ Hlth Network Princess Margaret Canc Ctr Toronto ON Canada Niagara Hlth Syst Thorold ON Canada Trillium Hlth Partners Mississauga ON Canada Univ Calgary Dept Surg Calgary AB Canada Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON Canada Univ Ottawa Tefler Sch Management Ottawa ON Canada Univ Hlth Network Toronto Gen Hosp Techna Inst Ctr Digital Therapeut R Fraser Elliot Bldg 4th Floor190 Elizabeth St Toronto ON M5G 2C4 Canada

Background: Comprehensive models of survivorship care are necessary to improve access to and coordination of care. New models of care provide the opportunity to address the complexity of physical and psychosocial problems and long-term health needs experienced by patients following cancer ***: This paper presents our expert-informed, rules-based survivorship algorithm to build a nurse-led model of survivorship care to support men living with prostate cancer (PCa). The algorithm is called No Evidence of Disease (Ned) and supports timelier decision-making, enhanced safety, and continuity of ***: An initial rule set was developed and refined through working groups with clinical experts across Canada (eg, nurse experts, physician experts, and scientists;n=20), and patient partners (n=3). algorithm priorities were defined through a multidisciplinary consensus meeting with clinical nurse specialists, nurse scientists, nurse practitioners, urologic oncologists, urologists, and radiation oncologists (n=17). The system was refined and validated using the nominal group ***: Four levels of alert classification were established, initiated by responses on the Expanded Prostate Cancer Index Composite for Clinical Practice survey, and mediated by changes in minimal clinically important different alert thresholds, alert history, and clinical urgency with patient autonomy influencing clinical acuity. Patient autonomy was supported through tailored education as a first line of response, and alert escalation depending on a patient-initiated request for a nurse ***: The Ned algorithm is positioned to facilitate PCa nurse-led care models with a high nurse-to-patient ratio. This novel expert-informed PCa survivorship care algorithm contains a defined escalation pathway for clinically urgent symptoms while honoring patient preference. Though further validation is required through a pragmatic trial, we anticipate the Ned algorithm

关键词： prostate cancer patient-reported outcomes nurse-led model of care expert system artificial intelligence-powered decision support digital health nursing algorithm development cancer treatment AI survivorship cancer

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On Understanding algorithm Comprehension and development 19

On Understanding Algorithm Comprehension and Development

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15th ACM Annual International Computing Education Research (ICER) Conference

作者： Kather, Philipp Westfal Wilhelms Univ Munster Dept Comp Sci Einsteinstr 62 D-48149 Munster Germany

ISBN: (纸本)9781450361859

Comprehending and developing algorithms are very common activities in computer science and other studies. But what does it mean to comprehend an algorithm? Why are students creating flawed algorithms with correct proofs? And what does it take to comprehend or create an algorithm? Through my dissertation work I want to gain understanding about these processes. By using mixed methods I hope to contribute to finding answers to those questions.

关键词： Computer Science Education algorithm Comprehension algorithm development Mixed Methods

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Factors Influencing Perceived Fairness in algorithmic Decision-Making: algorithm Outcomes, development Procedures, and Individual Differences 20

Factors Influencing Perceived Fairness in Algorithmic Decisi...

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CHI Conference on Human Factors in Computing Systems (CHI)

作者： Wang, Ruotong Harper, F. Maxwell Zhu, Haiyi Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Seattle WA USA

ISBN: (纸本)9781450367080

algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.

关键词： perceived fairness algorithmic decision-making algorithm outcome algorithm development

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Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: algorithm development and Validation Study

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JMIR AI 2023年 2卷 e44909页

作者： Kendale, Samir Bishara, Andrew Burns, Michael Solomon, Stuart Corriere, Matthew Mathis, Michael Beth Israel Deaconess Med Ctr Dept Anesthesia Crit Care & Pain Med 1 Deaconess Rd Boston MA 02215 USA Univ Calif San Francisco Dept Anesthesia & Perioperat Care San Francisco CA USA Univ Calif San Francisco Bakar Computat Hlth Sci Inst San Francisco CA USA Univ Michigan Dept Anesthesiol Med Sch Ann Arbor MI USA Univ Texas Hlth Sci Ctr San Antonio Dept Anesthesiol San Antonio TX USA Univ Michigan Dept Surg Sect Vasc Surg Med Sch Ann Arbor MI USA Univ Michigan Ctr Computat Med & Bioinformat Med Sch Ann Arbor MI USA

Background: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration. Objective: The aim of this study was to determine whether a machine learning algorithm scalable across multiple centers could make estimations of case duration within a tolerance limit because there are substantial resources required for operating room functioning that relate to case duration. Methods: Deep learning, gradient boosting, and ensemble machine learning models were generated using perioperative data available at 3 distinct time points: the time of scheduling, the time of patient arrival to the operating or procedure room (primary model), and the time of surgical incision or procedure start. The primary outcome was procedure duration, defined by the time between the arrival and the departure of the patient from the procedure room. Model performance was assessed by mean absolute error (MAE), the proportion of predictions falling within 20% of the actual duration, and other standard metrics. Performance was compared with a baseline method of historical means within a linear regression model. Model features driving predictions were assessed using Shapley additive explanations values and permutation feature importance. Results: A total of 1,177,893 procedures from 13 academic and private hospitals between 2016 and 2019 were used. Across all procedures, the median procedure duration was 94 (IQR 50-167) minutes. In estimating the procedure duration, the gradient boosting machine was the best-performing model, demonstrating an MAE of 34 (SD 47) minutes, with 46% of the predictions falling within 20% of the actual duration in thetest data set. This represented a statistically and clinically significant improvement in predictions c

关键词： medical informatics artificial intelligence AI machine learning operating room OR management perioperative algorithm development validation patient communication surgical procedure prediction model

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development of a radioactivity estimation algorithm for an arbitrary geometry radiation source in nuclear power plant workspace

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JOURNAL OF THE KOREAN PHYSICAL SOCIETY 2025年第6期86卷 566-574页

作者： Kim, Ju Young Kim, Ji Woo Kim, Min Seong Lee, Dae Ho Kim, Kwang Pyo Kyung Hee Univ 1732 Deokyoung Daero Yongin 17104 South Korea

Nuclear power plant workers can be exposed to radiation under various working conditions, such as during normal operation, maintenance period, and emergency. To establish an optimal work plan, radiation dose prediction and assessment must be conducted in advance. Radioactivity data are essential to assess radiation dose. However, in some situations, these data may not be available, and analyzing precise activity of radiation source in all nuclear power plant work environments may require significant time and financial resource. Therefore, an algorithm is needed that can swiftly estimate the radioactivity of the radiation sources utilizing the radiation dose rate which are readily available data. The objective of this study is to develop an algorithm for estimating the radioactivity of arbitrary geometry radiation sources within the nuclear power plant workspaces. To achieve this, an algorithm was initially developed to estimate the radioactivity of point sources and arbitrary geometry radiation sources. Subsequently, scenarios for the point sources and cylindrical volume sources were established, and the effectiveness of the algorithm was validated with applying these scenarios. In the radioactivity estimation algorithm for the point sources, the radioactivity was estimated by reverse-calculating the external exposure dose assessment equation from the radiation dose rate data. However, various uncertainties may exist in the radiation dose rate data. Because limitations exist in deriving an exact activity value, a numerical analysis method was employed to obtain an optimal solution. The estimation procedure was divided into five steps: (1) obtaining input parameters, (2) establishing the simultaneous equation, (3) deriving an initial solution set, (4) deriving the approximate solution set, and (5) calculating uncertainty and determining the optimal solution. Additionally, a radioactivity estimation algorithm for arbitrary geometry radiation sources was developed inte

关键词： Nuclear power plant Arbitrary geometry radiation source Radioactivity estimation algorithm development

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development and validation of a claims-based algorithm to identify incidents and determine the progression phases of gastric cancer cases in Japan

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JOURNAL OF GASTROENTEROLOGY 2025年第2期60卷 141-151页

作者： Inoue, Takahiro Agatsuma, Nobukazu Utsumi, Takahiro Tanaka, Yukari Nishikawa, Yoshitaka Horimatsu, Takahiro Shimizu, Takahiro Nikaido, Mitsuhiro Nakanishi, Yuki Hoshino, Nobuaki Takahashi, Yoshimitsu Nakayama, Takeo Seno, Hiroshi Kyoto Univ Grad Sch Med Dept Gastroenterol & Hepatol 54 KawaharachoShogoinSakyo Ku Kyoto 6068507 Japan Japanese Red Cross Wakayama Med Ctr Dept Gastroenterol & Hepatol Wakayama Japan Hino Mem Hosp Dept Internal Med Hino Shiga Japan Kyoto Univ Sch Publ Hlth Dept Hlth Informat Kyoto Japan Kyoto Univ Hosp Dept Clin Oncol Kyoto Japan Kyoto Univ Hosp Inst Advancement Clin & Translat Sci iACT Kyoto Japan Kyoto Univ Grad Sch Med Dept Surg Kyoto Japan

BackgroundAlthough health insurance claims data can address questions that clinical trials cannot answer, the uncertainty of disease names and the absence of stage information hinder their use in gastric cancer (GC) research. This study aimed to develop and validate a claims-based algorithm to identify and determine the progression phases of incident GC cases in *** gold standard for validation in this retrospective observational study was medical records of patients with incident GC who underwent specific treatments, defined by the claim codes associated with GC treatment. The algorithm was developed and refined using a cohort from two large tertiary care medical centers (April-September 2017 and April-September 2019) and subsequently validated using two independent cohorts: one from different periods (October 2017-March 2019 and October 2019-March 2021) and the other from a different institution (a community hospital). The algorithm identified incident cases based on a combination of the International Classification of Diseases, 10th Revision diagnosis codes for GC (C160-169), and claim codes for specific treatments, classifying them into endoscopic, surgical, and palliative groups. Positive predictive value (PPV), sensitivity of incident case identification, and diagnostic accuracy of progression phase determination were *** developed algorithm achieved PPVs of 90.0% (1119/1244) and 95.9% (94/98), sensitivities of 98.0% (1119/1142) and 98.9% (94/95) for incident case identification, with diagnostic accuracies of 94.1% (1053/1119) and 93.6% (88/94) for progression phase determination in the two validation cohorts, *** validated claims-based algorithm could advance real-world GC research and assist in decision-making regarding GC treatment.

关键词： Gastric cancer Health insurance claims algorithm development

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LiveLattice: Real-time visualisation of tilted light-sheet microscopy data using a memory-efficient transformation algorithm

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JOURNAL OF MICROSCOPY 2025年第2期297卷 123-134页

作者： Wang, Zichen Hakozaki, Hiroyuki Mcmahon, Gillian Medina-Carbonero, Marta Schoneberg, Johannes Univ Calif San Diego Dept Pharmacol San Diego CA USA Univ Calif San Diego Dept Chem & Biochem San Diego CA USA

Light-sheet fluorescence microscopy (LSFM), a prominent fluorescence microscopy technique, offers enhanced temporal resolution for imaging biological samples in four dimensions (4D;x, y, z, time). Some of the most recent implementations, including inverted selective plane illumination microscopy (iSPIM) and lattice light-sheet microscopy (LLSM), move the sample substrate at an oblique angle relative to the detection objective's optical axis. Data from such tilted-sample-scan LSFMs require subsequent deskewing and rotation for proper visualisation and analysis. Such data preprocessing operations currently demand substantial memory allocation and pose significant computational challenges for large 4D dataset. The consequence is prolonged data preprocessing time compared to data acquisition time, which limits the ability for live-viewing the data as it is being captured by the microscope. To enable the fast preprocessing of large light-sheet microscopy datasets without significant hardware demand, we have developed WH-Transform, a memory-efficient transformation algorithm for deskewing and rotating the raw dataset, significantly reducing memory usage and the run time by more than 10-fold for large image stacks. Benchmarked against the conventional method and existing software, our approach demonstrates linear runtime compared to the cubic and quadratic runtime of the other approaches. Preprocessing a raw 3D volume of 2 GB (512 x 1536 x 600 pixels) can be accomplished in 3 s using a GPU with 24 GB of memory on a single workstation. Applied to 4D LLSM datasets of human hepatocytes, lung organoid tissue and brain organoid tissue, our method provided rapid and accurate preprocessing within seconds. Importantly, such preprocessing speeds now allow visualisation of the raw microscope data stream in real time, significantly improving the usability of LLSM in biology. In summary, this advancement holds transformative potential for light-sheet microscopy, enabling real-time, on

关键词： fluorescence microscopy light-sheet microscopy lattice light-sheet microscopy microscopy data processing microscopy visualisation algorithm development

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Curation and validation of electronic medical record-based dementia diagnoses in the VA Million Veteran Program

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JOURNAL OF ALZHEIMERS DISEASE 2025年第1期103卷 180-193页

作者： Merritt, Victoria C. Zhang, Rui Sherva, Richard Ly, Monica T. Marra, David Panizzon, Matthew S. Tsuang, Debby W. Hauger, Richard L. Logue, Mark W. VA San Diego Healthcare Syst Res Serv San Diego CA USA Univ Calif San Diego Sch Med Dept Med La Jolla CA USA VA San Diego Healthcare Syst Ctr Excellence Stress & Mental Hlth San Diego CA USA VA Boston Healthcare Syst Natl Ctr PTSD Behav Sci Div 150 South Huntington Ave 116B-2 Boston MA 02130 USA Boston Univ Biomed Genet Chobanian & Avedisian Sch Med 150 South Huntington Ave 116B-2 Boston MA 02130 USA Boston Univ Sch Med Dept Neurol Boston MA USA Boston Univ Chobanian & Avedisian Sch Med Dept Psychiat Boston MA USA VA Boston Healthcare Syst Massachusetts Vet Epidemiol Res & Informat Ctr MAV Boston MA 02130 USA Univ Calif San Diego Ctr Behav Genet Aging La Jolla CA 94143 USA VA Puget Sound Hlth Care Syst Geriatr Res Educ & Clin Ctr Seattle WA USA Univ Washington Dept Psychiat & Behav Sci Seattle WA USA Boston Univ Sch Publ Hlth Dept Biostat Boston MA USA

Background The age distribution and diversity of the VA Million Veteran Program (MVP) cohort make it a valuable resource for studying the genetics of Alzheimer's disease (AD) and related dementias (ADRD). Objective We present and evaluate the performance of several International Classification of Diseases (ICD) code-based classification algorithms for AD, ADRD, and dementia for use in MVP genetic studies and other studies using VA electronic medical record (EMR) data. These were benchmarked relative to existing ICD algorithms and AD-medication-identified cases. Methods We used chart review of n = 103 MVP participants to evaluate diagnostic utility of the algorithms. Suitability for genetic studies was examined by assessing association with APOE epsilon 4, the strongest genetic AD risk factor, in a large MVP cohort (n = 286 K). Results The newly developed MVP-ADRD algorithm performed well, comparable to the existing PheCode dementia algorithm (Phe-Dementia) in terms of sensitivity (0.95 and 0.95) and specificity (0.65 and 0.70). The strongest APOE epsilon 4 associations were observed in cases identified using MVP-ADRD and Phe-Dementia augmented with medication-identified cases (MVP-ADRD or medication, p = 3.6 x10-290;Phe-Dementia or medication, p = 1.4 x10-290). Performance was improved when cases were restricted to those with onset age >= 60. Conclusions We found that our MVP-developed ICD-based algorithms had good performance in chart review and generated strong genetic signals, especially after inclusion of medication-identified cases. Ultimately, our MVP-derived algorithms are likely to have good performance in the broader VA, and their performance may also be suitable for use in other large-scale EMR-based biobanks in the absence of definitive biomarkers such as amyloid-PET and cerebrospinal fluid biomarkers.

关键词： algorithm development Alzheimer's disease Alzheimer's disease and related dementias military Veterans phenotyping

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development of a water clear of sea ice detection algorithm from enhanced SeaWinds/QuikSCAT and AMSR-E measurements

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REMOTE SENSING OF ENVIRONMENT 2010年第11期114卷 2594-2609页

作者： Howell, Stephen E. L. Derksen, Chris Tivy, Adrienne Environm Canada Atmospher Sci & Technol Directorate Div Climate Res Climate Processes Sect Toronto ON Canada Univ Alaska Int Arctic Res Ctr Fairbanks AK 99701 USA

We develop and evaluate water clear of sea ice (open water following ice cover) detection algorithms that make use of Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (sigma degrees) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T-B) measurements. algorithm validation was performed within Canadian Arctic waters using the Canadian Ice Service Digital Archive (CISDA) ice charts, NASATeam ice concentration estimates, extended AVHRR Polar Pathfinder (APP-x) albedo data, RADARSAT-1 imagery, and MODIS imagery. Results indicate that the temporal evolution of QuikSCAT sigma degrees, AMSR-E polarization ratio (PR18), and AMSR-E vertical spectral gradient ratio (GR3618) can detect water clear of sea ice events, however mean differences due to frequency dependent characteristics of the data (spatial resolution;sensitivity to open water) were apparent. All water clear of sea ice algorithms are in good agreement with the timing and clearing patterns given by the CISDA. The QuikSCAT algorithm provided a more representative ice edge and more details on the ice clearing process due to higher spatial resolution, however, transient clearing events were better represented by the AMSR-E PR(18) or (GR3618) algorithm. By exploiting the strengths of each sensor, we found that a QuikSCAT and AMSR-E fused algorithm provide improved open water area estimates by as much as 11%. The fusion of QuikSCAT and AMSR-E PR(18) yielded in the most spatially representative open water detection. The residual surface of the water clear of sea ice algorithms was found to provide another measure of the average September minimum pan-Arctic sea ice extent within 6% of the NASATeam algorithm estimates. Crown Copyright (C) 2010 Published by Elsevier Inc. All rights reserved.

关键词： Sea ice Open water QuikSCAT AMSR-E algorithm development

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development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study

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BRAIN INJURY 2024年第13期38卷 1084-1092页

作者： Merritt, Victoria C. Chen, Alicia W. Bonzel, Clara-Lea Hong, Chuan Sangar, Rahul Morini Sweet, Sara Sorg, Scott F. Chanfreau-Coffinier, Catherine VA San Diego Healthcare Syst VASDHS San Diego CA USA Univ Calif San Diego Dept Psychiat La Jolla CA USA VASDHS Ctr Excellence Stress & Mental Hlth San Diego CA USA VA Boston Healthcare Syst Boston MA USA Harvard Med Sch Boston MA USA Duke Univ Dept Biostat & Bioinformat Durham NH USA Home Base Red Sox Fdn & Home Base Program Boston MA USA VA Informat & Comp Infrastructure VINCI VA Salt Lake City Hlth Care Syst Salt Lake City UT USA

The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research.

关键词： Veterans affairs TBI phenotyping electronic medical record military algorithm development

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