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Article ; Online: Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification.

Campagner, Andrea / Barandas, Marilia / Folgado, Duarte / Gamboa, Hugo / Cabitza, Federico

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed ... ...

Abstract	In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed framework, we study, for the first time in the literature, the theoretical properties of CP ensembles in a general setting, by focusing on simple and commonly used possibilistic combination rules. We also illustrate the applicability of the proposed methods in the setting of multivariate time-series classification, showing that these methods provide better performance (in terms of both robustness, conservativeness, accuracy and running time) than both standard classification algorithms and other combination rules proposed in the literature, on a large set of benchmarks from the UCR time series archive.
Language	English
Publishing date	2024-04-12
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3388097
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Article ; Online: The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies.

Cabitza, Federico / Campagner, Andrea

International journal of medical informatics

2021 Volume 153, Page(s) 104510

Abstract: This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday care ... ...

Abstract	This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday care practices. We propose a practical checklist to help authors to self assess the quality of their contribution and to help reviewers to recognize and appreciate high-quality medical ML studies by distinguishing them from the mere application of ML techniques to medical data.
MeSH term(s)	Artificial Intelligence ; Checklist ; Humans ; Machine Learning ; Medical Informatics
Language	English
Publishing date	2021-06-02
Publishing country	Ireland
Document type	Editorial
ZDB-ID	1466296-6
ISSN	1872-8243 ; 1386-5056
ISSN (online)	1872-8243
ISSN	1386-5056
DOI	10.1016/j.ijmedinf.2021.104510
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Article ; Online: Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems.

Famiglini, Lorenzo / Campagner, Andrea / Barandas, Marilia / La Maida, Giovanni Andrea / Gallazzi, Enrico / Cabitza, Federico

Computers in biology and medicine

2024 Volume 170, Page(s) 108042

Abstract: This paper proposes a user study aimed at evaluating the impact of Class Activation Maps (CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of thoracolumbar (TL) fractures from vertebral X-rays. In particular, we ... ...

Abstract	This paper proposes a user study aimed at evaluating the impact of Class Activation Maps (CAMs) as an eXplainable AI (XAI) method in a radiological diagnostic task, the detection of thoracolumbar (TL) fractures from vertebral X-rays. In particular, we focus on two oft-neglected features of CAMs, that is granularity and coloring, in terms of what features, lower-level vs higher-level, should the maps highlight and adopting which coloring scheme, to bring better impact to the decision-making process, both in terms of diagnostic accuracy (that is effectiveness) and of user-centered dimensions, such as perceived confidence and utility (that is satisfaction), depending on case complexity, AI accuracy, and user expertise. Our findings show that lower-level features CAMs, which highlight more focused anatomical landmarks, are associated with higher diagnostic accuracy than higher-level features CAMs, particularly among experienced physicians. Moreover, despite the intuitive appeal of semantic CAMs, traditionally colored CAMs consistently yielded higher diagnostic accuracy across all groups. Our results challenge some prevalent assumptions in the XAI field and emphasize the importance of adopting an evidence-based and human-centered approach to design and evaluate AI- and XAI-assisted diagnostic tools. To this aim, the paper also proposes a hierarchy of evidence framework to help designers and practitioners choose the XAI solutions that optimize performance and satisfaction on the basis of the strongest evidence available or to focus on the gaps in the literature that need to be filled to move from opinionated and eminence-based research to one more based on empirical evidence and end-user work and preferences.
MeSH term(s)	Humans ; Mental Processes ; Radiology ; Semantics ; Spine
Language	English
Publishing date	2024-02-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2024.108042
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Article ; Online: Never tell me the odds: Investigating pro-hoc explanations in medical decision making.

Cabitza, Federico / Natali, Chiara / Famiglini, Lorenzo / Campagner, Andrea / Caccavella, Valerio / Gallazzi, Enrico

Artificial intelligence in medicine

2024 Volume 150, Page(s) 102819

Abstract: This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations, that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation ... ...

Abstract	This paper examines a kind of explainable AI, centered around what we term pro-hoc explanations, that is a form of support that consists of offering alternative explanations (one for each possible outcome) instead of a specific post-hoc explanation following specific advice. Specifically, our support mechanism utilizes explanations by examples, featuring analogous cases for each category in a binary setting. Pro-hoc explanations are an instance of what we called frictional AI, a general class of decision support aimed at achieving a useful compromise between the increase of decision effectiveness and the mitigation of cognitive risks, such as over-reliance, automation bias and deskilling. To illustrate an instance of frictional AI, we conducted an empirical user study to investigate its impact on the task of radiological detection of vertebral fractures in x-rays. Our study engaged 16 orthopedists in a 'human-first, second-opinion' interaction protocol. In this protocol, clinicians first made initial assessments of the x-rays without AI assistance and then provided their final diagnosis after considering the pro-hoc explanations. Our findings indicate that physicians, particularly those with less experience, perceived pro-hoc XAI support as significantly beneficial, even though it did not notably enhance their diagnostic accuracy. However, their increased confidence in final diagnoses suggests a positive overall impact. Given the promisingly high effect size observed, our results advocate for further research into pro-hoc explanations specifically, and into the broader concept of frictional AI.
MeSH term(s)	Humans ; Clinical Decision-Making ; Automation ; Physicians ; Radiology
Language	English
Publishing date	2024-03-01
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	645179-2
ISSN	1873-2860 ; 0933-3657
ISSN (online)	1873-2860
ISSN	0933-3657
DOI	10.1016/j.artmed.2024.102819
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Zs.A 2547: Show issues

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Article ; Online: A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients.

Famiglini, Lorenzo / Campagner, Andrea / Carobene, Anna / Cabitza, Federico

Medical & biological engineering & computing

2022

Abstract: In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests ... ...

Abstract	In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created four ML models, by adopting a robust development procedure which was designed to minimize risks of bias and over-fitting, according to reference guidelines. The best model, a support vector machine, had an AUC of .85, a Brier score of .14, and a standardized net benefit of .69: these scores indicate that the model performed well over a variety of prediction criteria. We also conducted an interpretability study to back up our findings, showing that the data on which the developed model is based is consistent with the current medical literature. This also demonstrates that CBC data and ML methods can be used to predict COVID-19 patients' ICU admission at a relatively low cost: in particular, since CBC data can be quickly obtained by means of routine blood exams, our models could be used in resource-constrained settings and provide health practitioners with rapid and reliable indications.
Language	English
Publishing date	2022-03-30
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	282327-5
ISSN	1741-0444 ; 0025-696X ; 0140-0118
ISSN (online)	1741-0444
ISSN	0025-696X ; 0140-0118
DOI	10.1007/s11517-022-02543-x
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Zs.A 294: Show issues

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Article ; Online: Decisions are not all equal-Introducing a utility metric based on case-wise raters' perceptions.

Campagner, Andrea / Sternini, Federico / Cabitza, Federico

Computer methods and programs in biomedicine

2022 Volume 221, Page(s) 106930

Abstract: Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this article we ...

Abstract	Background and Objective Evaluation of AI-based decision support systems (AI-DSS) is of critical importance in practical applications, nonetheless common evaluation metrics fail to properly consider relevant and contextual information. In this article we discuss a novel utility metric, the weighted Utility (wU), for the evaluation of AI-DSS, which is based on the raters' perceptions of their annotation hesitation and of the relevance of the training cases. Methods We discuss the relationship between the proposed metric and other previous proposals; and we describe the application of the proposed metric for both model evaluation and optimization, through three realistic case studies. Results We show that our metric generalizes the well-known Net Benefit, as well as other common error-based and utility-based metrics. Through the empirical studies, we show that our metric can provide a more flexible tool for the evaluation of AI models. We also show that, compared to other optimization metrics, model optimization based on the wU can provide significantly better performance (AUC 0.862 vs 0.895, p-value <0.05), especially on cases judged to be more complex by the human annotators (AUC 0.85 vs 0.92, p-value <0.05). Conclusions We make the point for having utility as a primary concern in the evaluation and optimization of machine learning models in critical domains, like the medical one; and for the importance of a human-centred approach to assess the potential impact of AI models on human decision making also on the basis of further information that can be collected during the ground-truthing process.
MeSH term(s)	Benchmarking ; Humans ; Machine Learning
Language	English
Publishing date	2022-06-03
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2022.106930
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Article ; Online: Introducing New Measures of Inter- and Intra-Rater Agreement to Assess the Reliability of Medical Ground Truth.

Campagner, Andrea / Cabitza, Federico

Studies in health technology and informatics

2020 Volume 270, Page(s) 282–286

Abstract: In this paper, we present and discuss two new measures of inter- and intra-rater agreement to assess the reliability of the raters, and hence of their labeling, in multi-rater setings, which are common in the production of ground truth for machine ... ...

Abstract	In this paper, we present and discuss two new measures of inter- and intra-rater agreement to assess the reliability of the raters, and hence of their labeling, in multi-rater setings, which are common in the production of ground truth for machine learning models. Our proposal is more conservative of other existing agreement measures, as it considers a more articulated notion of agreement by chance, based on an empirical estimation of the precision (or reliability) of the single raters involved. We discuss the measures in light of a realistic annotation tasks that involved 13 expert radiologists in labeling the MRNet dataset.
MeSH term(s)	Electronic Health Records ; Humans ; Information Storage and Retrieval/methods ; Machine Learning ; Observer Variation ; Radiologists ; Radiology ; Reproducibility of Results
Language	English
Publishing date	2020-06-22
Publishing country	Netherlands
Document type	Journal Article
ISSN	1879-8365
ISSN (online)	1879-8365
DOI	10.3233/SHTI200167
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Article: Assessment of Fast-Track Pathway in Hip and Knee Replacement Surgery by Propensity Score Matching on Patient-Reported Outcomes.

Campagner, Andrea / Milella, Frida / Guida, Stefania / Bernareggi, Susan / Banfi, Giuseppe / Cabitza, Federico

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 6

Abstract: Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting a ... ...

Abstract	Total hip (THA) and total knee (TKA) arthroplasty procedures have steadily increased over the past few decades, and their use is expected to grow further, mainly due to an increasing number of elderly patients. Cost-containment strategies, supporting a rapid recovery with a positive functional outcomes, high patient satisfaction, and enhanced patient reported outcomes, are needed. A Fast Track surgical procedure (FT) is a coordinated perioperative approach aimed at expediting early mobilization and recovery following surgery and, accordingly, shortening the length of hospital stay (LOS), convalescence and costs. In this view, rapid rehabilitation surgery optimizes traditional rehabilitation methods by integrating evidence-based practices into the procedure. The aim of the present study was to compare the effectiveness of Fast Track versus Care-as-Usual surgical procedures and pathways (including rehabilitation) on a mid-term patient-reported outcome (PROs), the SF12 (with regard both to Physical and Mental Scores), 3 months after hip or knee replacement surgery, with the use of Propensity score-matching (PSM) analysis to address the issue of the comparability of the groups in a non-randomized study. We were interested in the evaluation of the entire pathways, including the postoperative rehabilitation stage, therefore, we only used early home discharge as a surrogate to differentiate between the Fast Track and Care-as-Usual rehabilitation pathways. Our study shows that the entire Fast Track pathway, which includes the post-operative rehabilitation stage, has a significantly positive impact on physical health-related status (SF12 Physical Scores), as perceived by patients 3 months after hip or knee replacement surgery, as opposed to the standardized program, both in terms of the PROs score and the relative improvements observed, as compared with the minimum clinically important difference. This result encourages additional research into the effects of Fast Track rehabilitation on the entire process of care for patients undergoing hip or knee arthroplasty, focusing only on patient-reported outcomes.
Language	English
Publishing date	2023-03-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13061189
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Article: External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count.

Campagner, Andrea / Carobene, Anna / Cabitza, Federico

Health information science and systems

2021 Volume 9, Issue 1, Page(s) 37

Abstract: Purpose: The rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, ... ...

Abstract	Purpose: The rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, Machine Learning (ML) has been applied to hematological parameters to develop diagnostic tools and help clinicians in promptly managing positive patients. However, few ML models have been externally validated, making their real-world applicability unclear. Methods: We externally validate 6 state-of-the-art diagnostic ML models, based on Complete Blood Count (CBC) and trained on a dataset encompassing 816 COVID-19 positive cases. The external validation was performed based on two datasets, collected at two different hospitals in northern Italy and encompassing 163 and 104 COVID-19 positive cases, in terms of both error rate and calibration. Results and conclusion: We report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings.
Language	English
Publishing date	2021-10-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2697647-X
ISSN	2047-2501
ISSN	2047-2501
DOI	10.1007/s13755-021-00167-3
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Article: Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading.

Cabitza, Federico / Campagner, Andrea / Sconfienza, Luca Maria

Health information science and systems

2021 Volume 9, Issue 1, Page(s) 8

Abstract: Purpose: The integration of Artificial Intelligence into medical practices has recently been advocated for the promise to bring increased efficiency and effectiveness to these practices. Nonetheless, little research has so far been aimed at ... ...

Abstract	Purpose: The integration of Artificial Intelligence into medical practices has recently been advocated for the promise to bring increased efficiency and effectiveness to these practices. Nonetheless, little research has so far been aimed at understanding the best human-AI interaction protocols in collaborative tasks, even in currently more viable settings, like independent double-reading screening tasks. Methods: To this aim, we report about a retrospective case-control study, involving 12 board-certified radiologists, in the detection of knee lesions by means of Magnetic Resonance Imaging, in which we simulated the serial combination of two Deep Learning models with humans in eight double-reading protocols. Inspired by the so-called Kasparov's Laws, we investigate whether the combination of humans and AI models could achieve better performance than AI models alone, and whether weak reader, when supported by fit-for-use interaction protocols, could out-perform stronger readers. Results: We discuss two main findings: groups of humans who perform significantly worse than a state-of-the-art AI can significantly outperform it if their judgements are aggregated by majority voting (in concordance with the first part of the Kasparov's law); small ensembles of significantly weaker readers can significantly outperform teams of stronger readers, supported by the same computational tool, when the judgments of the former ones are combined within "fit-for-use" protocols (in concordance with the second part of the Kasparov's law). Conclusion: Our study shows that good interaction protocols can guarantee improved decision performance that easily surpasses the performance of individual agents, even of realistic super-human AI systems. This finding highlights the importance of focusing on how to guarantee better co-operation within human-AI teams, so to enable safer and more human sustainable care practices.
Language	English
Publishing date	2021-02-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2697647-X
ISSN	2047-2501
ISSN	2047-2501
DOI	10.1007/s13755-021-00138-8
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