Pivot and Cluster: An Exercise in Clinical Reasoning

May, Jori E.; Blackburn, Reaford J.; Centor, Robert M.; Dhaliwal, Gurpreet

doi:10.1007/s11606-017-4216-6

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…In that case, it is expected to increase the accuracy of diagnosis through the collaboration between “inside” and “outside”. A pivot and cluster strategy (PCS) is an “inside” diagnostic thinking model that combines systems 1 and 2 to prevent diagnostic omissions by developing a differential diagnosis of clinical phenotypes similar to the first diagnosis that intuitively comes to mind [ 15 , 16 ]. This PCS has been applied to machine learning to develop an “outside” CDSS model [ 17 ].…”

Section: Redefining Systemmentioning

confidence: 99%

“…For example, if two diseases are similar clinical phenotypes, they will be considered differential diagnoses, and differentiation will be necessary. Recognizing what findings are common to the two diseases and what are different will affect making a precise differential diagnosis [ 15 , 16 ]. Furthermore, having a solid typological disease illness script, especially for diseases the physician has little or no experience with, will help rapidly calibrate the diagnostic process by metacognition when an atypical case is encountered.…”

Section: How To Nurture the Effective Application Of Systemmentioning

confidence: 99%

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System 2 Diagnostic Process for the Next Generation of Physicians: “Inside” and “Outside” Brain—The Interplay between Human and Machine

Shimizu

2022

Diagnostics

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Improving diagnosis has been one of the most critical issues in medicine for the last two decades. In the context of the rise of digital health and its augmentation and human diagnostic thinking, it has become necessary to integrate the concept of digital diagnosis into dual-process theory (DPT), which is the fundamental axis of the diagnostic thinking process physicians. Particularly, since the clinical decision support system (CDSS) corresponds to analytical thinking (system 2) in DPT, it is necessary to redefine system 2 to include the CDSS. However, to the best of my knowledge there has been no concrete conceptual model based on this need. The innovation and novelty of this paper are that it redefines system 2 to include new concepts and shows the relationship among the breakdown of system 2. In this definition, system 2 is divided into “inside” and “outside” brains, where “inside” includes symptomatologic, anatomical, biomechanical–physiological, and etiological thinking approaches, and “outside” includes CDSS. Moreover, this paper discusses the actual and possible future interplay between “inside” and “outside.” The author envisions that this paper will serve as a cornerstone for the future development of system 2 diagnostic thinking strategy.

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Section: Redefining Systemmentioning

confidence: 99%

Section: How To Nurture the Effective Application Of Systemmentioning

confidence: 99%

System 2 Diagnostic Process for the Next Generation of Physicians: “Inside” and “Outside” Brain—The Interplay between Human and Machine

Shimizu

2022

Diagnostics

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“…The process involved in PCS is as follows. [2, 3] First, a clinician designates the initial diagnosis (pivot) as the most likely hypothesis through an intuitive or analytical process based on history and physical examination, their knowledge, and experience. Second, the clinician forms a disease cluster around the pivot to obtain a collection of differential diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Validating the representation of distance between infarct diseases using Word2Vec word embedding

Yokokawa

Noda

Yanagita

et al. 2022

Preprint

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Objective: To determine if inter-disease distances between word embedding vectors using the picot-and-cluster strategy (PCS) are a valid quantitative representation of similar disease groups in a limited domain. Materials and Methods: Abstracts were extracted from the Ichushi-Web database and subjected to morphological analysis and training using the Word2Vec. From this, word embedding vectors were obtained. For words including "infarction", we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. Results: Seventy-one words included "infarction", of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. The CCC was most significant at 0.8690 (metric and method: euclidean and centroid), while the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). Discussion: The metric and method that maximized the internal validity measure were different from those that maximized the external validity measures; both produced different results. The Cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. Conclusion: The distributed representation, when trained by Word2Vec on the "infarction" domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS.

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“…In PCS, clinicians simultaneously recall a differential diagnosis list that approximates one of the recalled diagnoses based on intuition. The process involved in PCS is as follows [ 2 , 3 ]: First, a clinician designates the initial diagnosis (pivot) as the most probable hypothesis through an intuitive or analytical process based on history and physical examination as well as their knowledge and experience. Second, the clinician forms a disease cluster around the pivot to obtain a collection of differential diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Validating the representation of distance between infarct diseases using word embedding

Yokokawa

Noda

Yanagita

et al. 2022

BMC Med Inform Decis Mak

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Background The pivot and cluster strategy (PCS) is a diagnostic reasoning strategy that automatically elicits disease clusters similar to a differential diagnosis in a batch. Although physicians know empirically which disease clusters are similar, there has been no quantitative evaluation. This study aimed to determine whether inter-disease distances between word embedding vectors using the PCS are a valid quantitative representation of similar disease groups in a limited domain. Methods Abstracts were extracted from the Ichushi Web database and subjected to morphological analysis and training using Word2Vec, FastText, and GloVe. Consequently, word embedding vectors were obtained. For words including “infarction,” we calculated the cophenetic correlation coefficient (CCC) as an internal validity measure and the adjusted rand index (ARI), normalized mutual information (NMI), and adjusted mutual information (AMI) with ICD-10 codes as the external validity measures. This was performed for each combination of metric and hierarchical clustering method. Results Seventy-one words included “infarction,” of which 38 diseases matched the ICD-10 standard with the appearance of 21 unique ICD-10 codes. When using Word2Vec, the CCC was most significant at 0.8690 (metric and method: euclidean and centroid), whereas the AMI was maximal at 0.4109 (metric and method: cosine and correlation, and average and weighted). The NMI and ARI were maximal at 0.8463 and 0.3593, respectively (metric and method: cosine and complete). FastText and GloVe generally resulted in the same trend as Word2Vec, and the metric and method that maximized CCC differed from the ones that maximized the external validity measures. Conclusions The metric and method that maximized the internal validity measure differed from those that maximized the external validity measures; both produced different results. The cosine distance should be used when considering ICD-10, and the Euclidean distance when considering the frequency of word occurrence. The distributed representation, when trained by Word2Vec on the “infarction” domain from a Japanese academic corpus, provides an objective inter-disease distance used in PCS.

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Pivot and Cluster: An Exercise in Clinical Reasoning

Cited by 7 publications

References 6 publications

System 2 Diagnostic Process for the Next Generation of Physicians: “Inside” and “Outside” Brain—The Interplay between Human and Machine

System 2 Diagnostic Process for the Next Generation of Physicians: “Inside” and “Outside” Brain—The Interplay between Human and Machine

Validating the representation of distance between infarct diseases using Word2Vec word embedding

Validating the representation of distance between infarct diseases using word embedding

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