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Anderson JW, Visweswaran S. Algorithmic Individual Fairness and Healthcare: A Scoping Review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.25.24304853. [PMID: 38585746 PMCID: PMC10996729 DOI: 10.1101/2024.03.25.24304853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Objective Statistical and artificial intelligence algorithms are increasingly being developed for use in healthcare. These algorithms may reflect biases that magnify disparities in clinical care, and there is a growing need for understanding how algorithmic biases can be mitigated in pursuit of algorithmic fairness. Individual fairness in algorithms constrains algorithms to the notion that "similar individuals should be treated similarly." We conducted a scoping review on algorithmic individual fairness to understand the current state of research in the metrics and methods developed to achieve individual fairness and its applications in healthcare. Methods We searched three databases, PubMed, ACM Digital Library, and IEEE Xplore, for algorithmic individual fairness metrics, algorithmic bias mitigation, and healthcare applications. Our search was restricted to articles published between January 2013 and September 2023. We identified 1,886 articles through database searches and manually identified one article from which we included 30 articles in the review. Data from the selected articles were extracted, and the findings were synthesized. Results Based on the 30 articles in the review, we identified several themes, including philosophical underpinnings of fairness, individual fairness metrics, mitigation methods for achieving individual fairness, implications of achieving individual fairness on group fairness and vice versa, fairness metrics that combined individual fairness and group fairness, software for measuring and optimizing individual fairness, and applications of individual fairness in healthcare. Conclusion While there has been significant work on algorithmic individual fairness in recent years, the definition, use, and study of individual fairness remain in their infancy, especially in healthcare. Future research is needed to apply and evaluate individual fairness in healthcare comprehensively.
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Affiliation(s)
| | - Shyam Visweswaran
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
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Hasannejadasl H, Roumen C, van der Poel H, Vanneste B, van Roermund J, Aben K, Kalendralis P, Osong B, Kiemeney L, Van Oort I, Verwey R, Hochstenbach L, J. Bloemen- van Gurp E, Dekker A, Fijten RRR. Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer. PLoS One 2023; 18:e0276815. [PMID: 36867616 PMCID: PMC9983834 DOI: 10.1371/journal.pone.0276815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/14/2022] [Indexed: 03/04/2023] Open
Abstract
While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly. Erectile dysfunction (ED) is a common burden associated with increasing age as well as prostate cancer treatment. Although many studies have investigated the factors affecting erectile dysfunction (ED) after prostate cancer treatment, only limited studies have investigated whether ED can be predicted before the start of treatment. The advent of machine learning (ML) based prediction tools in oncology offers a promising approach to improve the accuracy of prediction and quality of care. Predicting ED may help aid shared decision-making by making the advantages and disadvantages of certain treatments clear, so that a tailored treatment for an individual patient can be chosen. This study aimed to predict ED at 1-year and 2-year post-diagnosis based on patient demographics, clinical data and patient-reported outcomes (PROMs) measured at diagnosis. We used a subset of the ProZIB dataset collected by the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) that contained information on 964 localized prostate cancer cases from 69 Dutch hospitals for model training and external validation. Two models were generated using a logistic regression algorithm coupled with Recursive Feature Elimination (RFE). The first predicted ED 1 year post-diagnosis and required 10 pre-treatment variables; the second predicted ED 2 years post-diagnosis with 9 pre-treatment variables. The validation AUCs were 0.84 and 0.81 for 1 year and 2 years post-diagnosis respectively. To immediately allow patients and clinicians to use these models in the clinical decision-making process, nomograms were generated. In conclusion, we successfully developed and validated two models that predicted ED in patients with localized prostate cancer. These models will allow physicians and patients alike to make informed evidence-based decisions about the most suitable treatment with quality of life in mind.
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Affiliation(s)
- Hajar Hasannejadasl
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Cheryl Roumen
- Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Henk van der Poel
- Department of Urology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Joep van Roermund
- Department of Urology, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Katja Aben
- Department of Research & Development, Netherlands Comprehensive Cancer Organization, Utrecht, The Netherlands
- Institute for Health Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Petros Kalendralis
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Biche Osong
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lambertus Kiemeney
- Department of Research & Development, Netherlands Comprehensive Cancer Organization, Utrecht, The Netherlands
| | - Inge Van Oort
- Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Renee Verwey
- Zuyd University of Applied Sciences, Heerlen, The Netherlands
| | | | - Esther J. Bloemen- van Gurp
- Zuyd University of Applied Sciences, Heerlen, The Netherlands
- Fontys University of Applied Sciences, Eindhoven, The Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Rianne R. R. Fijten
- Department of Radiation Oncology (MAASTRO), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
- * E-mail: ,
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Singh V, Rani R, Singla A. Preliminary algorithm for a personalized diagnosis of cardiovascular disease and dependent renal complications using decision tree. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2021. [DOI: 10.1007/s43538-021-00026-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Xu W, Sun NN, Gao HN, Chen ZY, Yang Y, Ju B, Tang LL. Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning. Sci Rep 2021; 11:2933. [PMID: 33536460 PMCID: PMC7858607 DOI: 10.1038/s41598-021-82492-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/11/2021] [Indexed: 01/08/2023] Open
Abstract
COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.
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Affiliation(s)
- Wan Xu
- Hangzhou Xiaoshan District Center for Disease Control and Prevention, Hangzhou, China
| | - Nan-Nan Sun
- Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China
| | - Hai-Nv Gao
- Department of Infectious Diseases, ShuLan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
| | - Zhi-Yuan Chen
- Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China
| | - Ya Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
| | - Bin Ju
- Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China.
| | - Ling-Ling Tang
- Department of Infectious Diseases, ShuLan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China.
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Johnson A, Cooper GF, Visweswaran S. Patient-Specific Modeling with Personalized Decision Paths. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:602-611. [PMID: 33936434 PMCID: PMC8075540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Predictive models can be useful in predicting patient outcomes under uncertainty. Many algorithms employ "population" methods, which optimize a single model to perform well on average over an entire population, but the model may perform poorly on some patients. Personalized methods optimize predictive performance for each patient by tailoring the model to the individual. We present a new personalized method based on decision trees: the Personalized Decision Path using a Bayesian score (PDP-Bay). Performance on eight synthetic, genomic, and clinical datasets was compared to that of decision trees and a previously described personalized decision path method in terms of area under the ROC curve (AUC) and expected calibration error (ECE). Model complexity was measured by average path length. The PDP-Bay model outperformed the decision tree in terms of both AUC and ECE. The results support the conclusion that personalization may achieve better predictive performance and produce simpler models than population approaches.
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Affiliation(s)
- Adriana Johnson
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
| | - Gregory F Cooper
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA
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Kirks RC, Cochran A, Barnes TE, Murphy K, Baker EH, Martinie JB, Iannitti DA, Vrochides D. Developing and validating a center-specific preoperative prediction calculator for risk of pancreaticoduodenectomy. Am J Surg 2018. [PMID: 29519551 DOI: 10.1016/j.amjsurg.2018.02.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND The American College of Surgeons (ACS) Surgical Risk Calculator predicts postoperative risk based on preoperative variables. The ACS model was compared to an institution-specific risk calculator for pancreaticoduodenectomy (PD). METHODS Observed outcomes were compared with those predicted by the ACS and institutional models. Receiver operating characteristic (ROC) analysis evaluated the models' predictive ability. Institutional models were evaluated with retrospective and prospective internal validation. RESULTS Brier scores indicate equivalent aggregate predictive ability. ROC values for the institutional model (ROC: 0.675-0.881, P < 0.01) indicate superior individual event occurrence prediction (ACS ROC: 0.404-0.749, P < 0.01-0.860). Institutional models' accuracy was upheld in retrospective (ROC: 0.765-0.912) and prospective (ROC: 0.882-0.974) internal validation. CONCLUSIONS Identifying higher-risk patients allows for individualized care. While ACS and institutional models accurately predict average complication occurrence, the institutional models are superior at predicting individualized outcomes. Predictive metrics specific to PD center volume may more accurately predict outcomes.
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Affiliation(s)
- Russell C Kirks
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Allyson Cochran
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - T Ellis Barnes
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Keith Murphy
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Erin H Baker
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - John B Martinie
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - David A Iannitti
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA
| | - Dionisios Vrochides
- Division of Hepatopancreaticobiliary Surgery, Department of Surgery, Carolinas Medical Center, Charlotte, NC, USA.
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