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      Study of the Relationship between ICU Patient Recovery and TCM Treatment in Acute Phase: A Retrospective Study Based on Python Data Mining Technology

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          Abstract

          Background

          Data was mined with the help of an artificial intelligence system based on Python, data was collected, and a database was established using a Python crawler, and the relationship between the outcome of neurosurgery ICU patients and treatment using traditional Chinese medicine was ascertained through data management and statistical processing.

          Method

          The source data cases ( n = 2237) were selected. By following the experimental design, data ( n = 739) were obtained through artificial intelligence processing, including n = 480 in the group with traditional Chinese medicine treatment and n = 259 in the group without traditional Chinese medicine treatment. An evaluation was carried out using characteristics of patents' ICU stays and summated rating scales.

          Results

          There were statistical differences in 5 evaluation items ( P < 0.05), and other comparison items also showed data with results favoring the outcomes in the intervention group using traditional Chinese medicine. Discussion. Traditional Chinese medicine as an alternative medical protocol effectively alleviates the stress and treatment fatigue brought about by modern medicine. Artificial intelligence data mining is a favorable medium to quantify this. Python will play a greater role in future clinical research because of its own characteristics.

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          Most cited references20

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          Support-vector networks

          Corinna Cortes, Vladimir Vapnik (1995)
          Article 0 comments Cited 2646 times – based on 0 reviews     Review now
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            First documented rhythm and clinical outcome from in-hospital cardiac arrest among children and adults.

            Vinay Nadkarni, Gregory Luke Larkin, Mary Ann Peberdy … (2006)
            Cardiac arrests in adults are often due to ventricular fibrillation (VF) or pulseless ventricular tachycardia (VT), which are associated with better outcomes than asystole or pulseless electrical activity (PEA). Cardiac arrests in children are typically asystole or PEA. To test the hypothesis that children have relatively fewer in-hospital cardiac arrests associated with VF or pulseless VT compared with adults and, therefore, worse survival outcomes. A prospective observational study from a multicenter registry (National Registry of Cardiopulmonary Resuscitation) of cardiac arrests in 253 US and Canadian hospitals between January 1, 2000, and March 30, 2004. A total of 36,902 adults (> or =18 years) and 880 children (<18 years) with pulseless cardiac arrests requiring chest compressions, defibrillation, or both were assessed. Cardiac arrests occurring in the delivery department, neonatal intensive care unit, and in the out-of-hospital setting were excluded. Survival to hospital discharge. The rate of survival to hospital discharge following pulseless cardiac arrest was higher in children than adults (27% [236/880] vs 18% [6485/36,902]; adjusted odds ratio [OR], 2.29; 95% confidence interval [CI], 1.95-2.68). Of these survivors, 65% (154/236) of children and 73% (4737/6485) of adults had good neurological outcome. The prevalence of VF or pulseless VT as the first documented pulseless rhythm was 14% (120/880) in children and 23% (8361/36,902) in adults (OR, 0.54; 95% CI, 0.44-0.65; P<.001). The prevalence of asystole was 40% (350) in children and 35% (13 024) in adults (OR, 1.20; 95% CI, 1.10-1.40; P = .006), whereas the prevalence of PEA was 24% (213) in children and 32% (11,963) in adults (OR, 0.67; 95% CI, 0.57-0.78; P<.001). After adjustment for differences in preexisting conditions, interventions in place at time of arrest, witnessed and/or monitored status, time to defibrillation of VF or pulseless VT, intensive care unit location of arrest, and duration of cardiopulmonary resuscitation, only first documented pulseless arrest rhythm remained significantly associated with differential survival to discharge (24% [135/563] in children vs 11% [2719/24,987] in adults with asystole and PEA; adjusted OR, 2.73; 95% CI, 2.23-3.32). In this multicenter registry of in-hospital cardiac arrest, the first documented pulseless arrest rhythm was typically asystole or PEA in both children and adults. Because of better survival after asystole and PEA, children had better outcomes than adults despite fewer cardiac arrests due to VF or pulseless VT.
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              Machine learning in chemoinformatics and drug discovery

              Yu-Chen Lo, Stefano E. Rensi, Wen Torng … (2018)
              Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
              Article 0 comments Cited 192 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Xue Wang:
                ORCID: https://orcid.org/0000-0002-2606-1536
                Journal
                Journal ID (nlm-ta): Evid Based Complement Alternat Med
                Journal ID (iso-abbrev): Evid Based Complement Alternat Med
                Journal ID (publisher-id): ECAM
                Title: Evidence-based Complementary and Alternative Medicine : eCAM
                Publisher: Hindawi
                ISSN (Print): 1741-427X
                ISSN (Electronic): 1741-4288
                Publication date Collection: 2021
                Publication date (Electronic): 2 March 2021
                Publication date PMC-release: 2 March 2021
                Volume: 2021
                Electronic Location Identifier: 5548157
                Affiliations
                1Department of Neurosurgery, Shanghai Blue Cross Brain Hospital, Qixin Road 2880, Shanghai 201100, China
                2Intensive Care Unit (ICU), Shanghai Tianyou Hospital, Zhen'nan Road 528, Shanghai 200333, China
                Author notes

                Academic Editor: Lei Jiang

                Author information
                https://orcid.org/0000-0001-5881-921X
                https://orcid.org/0000-0002-2606-1536
                Article
                DOI: 10.1155/2021/5548157
                PMC ID: 7943298
                SO-VID: bec4cbf9-bcad-4407-ba4a-ad187aca2a24
                Copyright © Copyright © 2021 Zhiqun Wu et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 7 January 2021
                Date revision received : 20 February 2021
                Date accepted : 23 February 2021
                Funding
                Funded by: Tongji University
                Award ID: 2020NS03
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Complementary & Alternative medicine
                Data availability:
                ScienceOpen disciplines: Complementary & Alternative medicine

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