A Feature Optimized Deep Learning Model for Clinical Data Mining

Wu, Tianshu; Chen, Shuyu; Tian, Yingming; Wu, Peng

doi:10.1049/cje.2020.03.004

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The frequency of the item set is the number of transactions that contain the item set, which is called the frequency of the item set. If the item set meets the minimum support, it is called frequent item set [1].…”

Section: Introductionmentioning

confidence: 99%

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Wang

2022

Wireless Communications and Mobile Computing

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Data mining technology can analyze and mine university management data, provide more data support for university management, and play an important role in optimizing teaching quality, but these technologies are rarely applied to the work of university counselors. Based on this, this paper studies the construction and research of college counselors’ innovative work mode based on data mining technology. Based on the simple analysis of the impact of big data on the traditional working mode of counselors, this paper introduces the application of data mining commonly used in colleges and universities and puts forward the existing shortcomings. The innovative working mode of college counselors is designed. In the analysis of students’ daily behavior, the advantages of cluster analysis and support vector machine are used to analyze students’ consumption behavior. The Apriori algorithm is applied to the student achievement early warning management to improve the Apriori algorithm. Simulation results show that the proposed algorithm can shorten the running time, reduce the number of frequent item sets, and improve the classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Wang

2022

Wireless Communications and Mobile Computing

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“…Furthermore, this type of pipeline represents a novel exploration in predictive modeling and has been gaining traction in related studies. Such approaches have been widely adopted across various domains including clinical data classification ( Kong & Yu, 2018 ; Wu et al, 2020 ), financial analysis ( Ma, Han & Fu, 2019 ; Pai & Ilango, 2020 ), and solar power prediction ( Wang et al, 2023 ). All these related studies acknowledge the effectiveness of combining RF for feature importance and LSTM networks for sequence modeling.…”

Section: Methodsmentioning

confidence: 99%

Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model

Khairuddin,

Sankaranarayanan,

Hasikin

et al. 2024

PeerJ Computer Science

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Background This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational injury severity assessment. Methods Natural language processing (NLP) techniques were harnessed to preprocess the occupational injury narratives obtained from the US Occupational Safety and Health Administration (OSHA) from January 2015 to June 2023. The methodology involved meticulous preprocessing of textual narratives to standardize text and eliminate noise, followed by the innovative integration of Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) word embeddings for effective text representation. The proposed predictive model adopts a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture and is further refined through model optimization, including random search hyperparameters and in-depth feature importance analysis. The optimized Bi-LSTM model has been compared and validated against other machine learning classifiers which are naïve Bayes, support vector machine, random forest, decision trees, and K-nearest neighbor. Results The proposed optimized Bi-LSTM models’ superior predictability, boasted an accuracy of 0.95 for hospitalization and 0.98 for amputation cases with faster model processing times. Interestingly, the feature importance analysis revealed predictive keywords related to the causal factors of occupational injuries thereby providing valuable insights to enhance model interpretability. Conclusion Our proposed optimized Bi-LSTM model offers safety and health practitioners an effective tool to empower workplace safety proactive measures, thereby contributing to business productivity and sustainability. This study lays the foundation for further exploration of predictive analytics in the occupational safety and health domain.

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“…17 Conducting clinical studies in patients with melasma using specific wavelengths or parameters poses challenges because of potential pigmentation issues. Our animal model of melasma closely resembled human skin, 18 enabling the evaluation of various wavelengths of picosecond lasers and parameters for both effectiveness and adverse reactions. The findings from these animal experiments provided insight into the impact of picosecond laser treatment on melanocytes as well as considerations for skin collagen, water molecules, blood vessels and other anatomical structures.…”

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confidence: 99%

Combining large‐spot low‐fluence 1064‐nm and fractional 1064‐nm picosecond lasers for promoting protective melanosome autophagy via the PI3K/Akt/mTOR signalling pathway for the treatment of melasma

Shen,

Jin,

Huang

et al. 2024

Experimental Dermatology

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Melasma is a common condition of hyperpigmented facial skin. Picosecond lasers are reported to be effective for the treatment of melasma. We aimed to identify the most effective therapeutic mode and elucidate the potential molecular mechanisms of picosecond lasers for the treatment of melasma. Female Kunming mice with melasma‐like conditions were treated using four different picosecond laser modes. Concurrently, in vitro experiments were conducted to assess changes in melanin and autophagy in mouse melanoma B16‐F10 cells treated with these laser modes. Changes in melanin in mouse skin were detected via Fontana–Masson staining, and melanin particles were evaluated in B16‐F10 cells. Real‐time polymerase chain reaction and western blotting were used to analyse the expression levels of melanosome and autophagy‐related messenger ribonucleic acid (mRNA) and proteins. A combination of large‐spot low‐fluence 1064‐nm and fractional 1064‐nm picosecond lasers resulted insignificant decreases in melanin as well as in mRNA and protein expression of melanin‐synthesizing enzymes (TYR, TRP‐1 and MITF). This combination also led to increased expression of the autophagy‐related proteins, Beclin1 and ATG5, with a marked decrease in p62 expression. Intervention with the PI3K activator, 740 Y‐P, increased TYR, TRP‐1, MITF, p‐PI3K, p‐AKT, p‐mTOR and p62 expression but decreased the expression of LC3, ATG5 and Beclin1. A combination of large‐spot low‐fluence 1064‐nm and fractional 1064‐nm picosecond lasers proved more effective and safer. It inhibits melanin production, downregulates the PI3K/AKT/mTOR pathway, enhances melanocyte autophagy and accelerates melanin metabolism, thereby reducing melanin content.

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A Feature Optimized Deep Learning Model for Clinical Data Mining

Cited by 8 publications

References 13 publications

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model

Combining large‐spot low‐fluence 1064‐nm and fractional 1064‐nm picosecond lasers for promoting protective melanosome autophagy via the PI3K/Akt/mTOR signalling pathway for the treatment of melasma

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