The measurement of meaning

The reliability and validity of traditional taxonomies are limited by arbitrary boundaries between psychopathology and normality, often unclear boundaries between disorders, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. These taxonomies went beyond evidence available on the structure of psychopathology and were shaped by a variety of other considerations, which may explain the aforementioned shortcomings. The Hierarchical Taxonomy Of Psychopathology (HiTOP) model has emerged as a research effort to address these problems. It constructs psychopathological syndromes and their components/subtypes based on the observed covariation of symptoms, grouping related symptoms together and thus reducing heterogeneity. It also combines co-occurring syndromes into spectra, thereby mapping out comorbidity. Moreover, it characterizes these phenomena dimensionally, which addresses boundary problems and diagnostic instability. Here, we review the development of the HiTOP and the relevant evidence. The new classification already covers most forms of psychopathology. Dimensional measures have been developed to assess many of the identified components, syndromes, and spectra. Several domains of this model are ready for clinical and research applications. The HiTOP promises to improve research and clinical practice by addressing the aforementioned shortcomings of traditional nosologies. It also provides an effective way to summarize and convey information on risk factors, etiology, pathophysiology, phenomenology, illness course, and treatment response. This can greatly improve the utility of the diagnosis of mental disorders. The new classification remains a work in progress. However, it is developing rapidly and is poised to advance mental health research and care significantly as the relevant science matures. (PsycINFO Database Record

/pdf/the-hierarchical-taxonomy-of-psychopathology-hitop-a-1wcosnqb5e.pdf

The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies

Complex immune dysregulation in covid-19 patients with severe respiratory failure

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

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Learning To Teach In Higher Education

Objectives: Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with : Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ). Methods: A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects A total of 151 outpatients and 73 normal subjects were asked to complete the LJQ. Seventy-three normal subjects were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the were additionally asked to complete the Chalder Fatigue Scale (CFS). Twelve clinicians determined whether the 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using 151 outpatients exhibited LJ or not. The internal consistency and construct validity for the LJQ were estimated using data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ data from the outpatient subjects. The CFS data were used to examine the concurrent validity of the LJQ. Total LJQ scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) scores and the clinicians' diagnoses of the outpatients were used to perform receiver operating characteristics (ROC) curve analyses and to defi ne an optimum cut-off score for the LJQ. curve analyses and to defi ne an optimum cut-off score for the LJQ. Results: The 19-item LJQ had satisfactory internal : The 19-item LJQ had satisfactory internal consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. consistency (α=0.828) and concurrent validity, with signifi cant correlations between the LJQ and the CFS subscales. In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall In the test of construct validity using principal component analysis, a total of six factors were extracted, and the overall variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the variance explained by all factors was 59.5%. In ROC curve analyses, the sensitivity, specifi city, and area under the curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. curve were 76.0%, 59.2%, and 0.709, respectively. The optimum cut-off score was defi ned as six points. Conclusions: Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. Our results suggest that the LJQ is a reliable and valid instrument for evaluating LJ. KEYWORDS Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern Chinese medicine, chronic fatigue syndrome, Chinese medicine-pattern

Development and Validation of A

Objectives: The present study was conducted to examine the reliability and validity of a Phlegm Pattern Questionnaire (PPQ) that was developed within the context of Traditional Chinese Medicine. Methods: A total of 292 normal subjects and 61 inpatients were asked to complete the PPQ. Among the 292 normal subjects, 73 college students were additionally asked to complete the Profile of Mood States (POMS) and the Chalder Fatigue Scale (CFS). Three (3) clinicians determined whether the 61 inpatients exhibited the Phlegm Pattern (PP). The authors estimated the internal consistency and construct validity for the PPQ using data from the normal and inpatient subjects. The POMS and CFS data were used to evaluate the concurrent validity of the PPQ. PPQ data and the clinicians' diagnoses of the inpatient subjects were used to estimate the sensitivity and specificity and to define the optimum cut-off point of the PPQ. Results: The PPQ had satisfactory internal consistency (α=0.902 in normal subjects, α=0.919...

Development of a valid and reliable phlegm pattern questionnaire.

A Study on Factors Associated with Weight Loss by ‘Gamitaeeumjowee-Tang’

Objectives
Lao Juan (LJ, 劳倦) is a syndrome described in Chinese medicine (CM) that manifests with fatigue, fever, spontaneous sweating, indigestion, work-induced pain, weakness of the limbs, and shortness of breath. The present study was conducted to examine the reliability and validity of a Lao Juan Questionnaire (LJQ).

Development and validation of a Lao Juan questionnaire.

Background: This was a study to analyze Sanghanron through network theory, as the first attempt to construct network models for systems biomedicine in traditional Korean medicine. For this purpose, we investigated the network structure with priority given to two-node connections between symptoms and herbs of Taeyangbyung compilation in Sanghanron. Purpose: We had three goals in carrying out this study. First, to establish the minimum clinical grouping data sets for symptoms and herbs of Taeyangbyung compilation in Sanghanron. Second, to make index files for the obtained data sets. Third, to generate a network structure for systems biomedicine in this part, and analyze its relationship. Methods: Using MS office Excel and Netminer software, we constructed the minimum clinical grouping data sets and the network for systems biomedicine about symptoms and herbs of Taeyangbyung compilation in Sanghanron, and analyzed its relationship. Results: We established the minimum clinical grouping data sets for symptoms and herbs of Taeyangbyung compilation in Sanghanron, using MS Excel. We constructed a network to structurize our database through two-node connections of Netminer program, and analyzed its relationships. Conclusions: Further research on network model for systems biomedicine between symptoms and herbs for three Yang and three Um(Taeyang, Soyang, Yangmyung, Taeum, Soum, Gualum) disease compilation is necessary.

/pdf/a-structural-analysis-of-sanghanron-by-network-model-4nvgsf34bi.pdf

Min-Yong Kim

Papers

Development and Validation of A

Development of a valid and reliable phlegm pattern questionnaire.

A Study on Factors Associated with Weight Loss by ‘Gamitaeeumjowee-Tang’

Development and validation of a Lao Juan questionnaire.

A Structural Analysis of Sanghanron by Network Model - Centered on Symptoms and Herbs of Taeyangbyung Compilation in Sanghanron -