Artificial Intelligence and Statistics: Just the Old Wine in New Wineskins?

Faes, Livia; Sim, Dawn A; Smeden, Maarten van; Held, Ulrike; Bossuyt, Patrick M.; Bachmann, Lucas M.

doi:10.3389/fdgth.2022.833912

Cited by 14 publications

(12 citation statements)

References 32 publications

(30 reference statements)

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“…The difference in mindset becomes apparent when reading each professional's published literature. For example, what is known in statistical literature as fitting or estimating is called learning or training in the ML literature [109]. Similarly, external validity is called transportability, and covariates are called features [110].…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, external validity is called transportability, and covariates are called features [110]. By developing a common language between these groups of researchers, we avoid complexities when aggregating results in systematic reviews and increase cross-contamination of ideas [109]. By converging these worlds, we probably will be able to extract more information from data, while avoiding harm by neglecting the need to address potential biases.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modern Learning from Big Data in Critical Care: Primum Non Nocere

Gravesteijn

Lingsma

2022

Neurocrit Care

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Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modern Learning from Big Data in Critical Care: Primum Non Nocere

Gravesteijn

Lingsma

2022

Neurocrit Care

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“…In this article, we hypothesize that the co-lighting of reciprocal reasoning and cross-teaching between IA and clinician reasoning could be fruitful ( 11 , 12 ). This interdisciplinary dialog would not only shed light on clinical practice but also create a dynamic for the challenges of the psychiatry of the future, bringing together digital technology and clinical practice ( 13 – 15 ).…”

Section: Introductionmentioning

confidence: 99%

How Does Comparison With Artificial Intelligence Shed Light on the Way Clinicians Reason? A Cross-Talk Perspective

et al. 2022

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In order to create a dynamic for the psychiatry of the future, bringing together digital technology and clinical practice, we propose in this paper a cross-teaching translational roadmap comparing clinical reasoning with computational reasoning. Based on the relevant literature on clinical ways of thinking, we differentiate the process of clinical judgment into four main stages: collection of variables, theoretical background, construction of the model, and use of the model. We detail, for each step, parallels between: i) clinical reasoning; ii) the ML engineer methodology to build a ML model; iii) and the ML model itself. Such analysis supports the understanding of the empirical practice of each of the disciplines (psychiatry and ML engineering). Thus, ML does not only bring methods to the clinician, but also supports educational issues for clinical practice. Psychiatry can rely on developments in ML reasoning to shed light on its own practice in a clever way. In return, this analysis highlights the importance of subjectivity of the ML engineers and their methodologies.

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“…We write to expand on Faes's et al recent publication “ Artificial intelligence and statistics: Just the old wine in new wineskins? ” ( 1 ). The authors rightly address a lack of consensus regarding terminology between the statistics and machine learning fields.…”

mentioning

confidence: 99%