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ڈاکٹر قاسم علی منصوری
ڈاکٹر قاسم علی منصوری
مسلم یونیورسٹی کے شعبۂ کیمیا کے نوجوان لائق صدر ڈاکٹر قاسم علی منصوری ایم، اے۔ ایم، ایس، سی (کینٹب) پی، ایچ، ڈی، (گوٹنجن) جو ہماری قوم میں اس فن کے مستند ماہر اور یورپ کی درسگاہوں کی متعدد سندوں کے مالک تھے، ۱۰؍مارچ ۱۹۳۰ء کی صبح کو کسی بیماری میں دل کی حرکت بند ہوجانے سے وفات پاگئے، مرحوم کے دل کا یہ عارضہ کیمیائی تجربہ گاہ کے بعض خاص قسم کی گیس کے اثر سے شروع ہوا تھا، جس سے وہ بالآخر نجات نہ پاسکے، اس طرح ہم ان کو شہید علم کا درجہ دے سکتے ہیں، مرحوم کی اس غیر متوقع وفات سے ہمارے ملک کے حلقۂ علم و فن کو بڑا صدمہ پہنچا، خدا مغفرت فرمائے۔
(سید سلیمان ندوی، اپریل ۱۹۳۰ء)
Completeness Analysis of Completeness Filling and Time of Returning The Medical Record for Inpatient Patients at Regional General Hospital of Makassar City
At Makassar City Hospital, one of the service indicators that has not been achieved is in incomplete medical record files and medical record files that are returned more than 2x24 hours after service. This study aims to analyze the implementation of the completeness of filling in and the timeliness of returning inpatient medical record files at the Makassar City Hospital. This type of research is mixed methods research. The study design used a cross-sectional approach. The study was conducted in September - October 2020. The results showed that the implementation of completeness of filling in and the timeliness of returning medical record files was still low, this has led to the accumulation of medical record files in the treatment room and delays in returning the files of inpatients to the medical record installation of the City Hospital Makassar. Training on the implementation of medical records has not been comprehensive for all officers at the Makassar City Hospital. The result of the delay in returning the documents is the delay in payment of insurance claims to the hospital. Accumulation of medical records in the treatment room from incomplete medical records and returned to the treatment room. Health workers who forget to fill in complete medical records are only given a sanction in the form of a warning during a meeting with the medical committee. Availability of SOP on filling and returning medical record files at the hospital. The facilities and infrastructure in the implementation of medical records are still insufficient for medical record employees at Makassar City Hospital. It is recommended that the hospital improve the implementation of the completeness and timeliness of returning medical record files, provide incentives or rewards for completing filling in, increase the number of computers and expand the room in the medical record installation, and review the medical record format at Makassar City HospitalNovel Disease Named Entity Recognition Dner & Hybrid Relation Extraction Hre Frameworks for Biomedical Text
Biomedical knowledge is usually presented in the form of unstructured segments; making the extraction of such information a complex task. Although, manual information extraction often produces the best results, it is harder to manage biomedical data extraction manually, because its data size is rising exponentially. Thus, there is a need for automatic tools and techniques for information extraction and knowledge discovery in biomedical text mining. Named entity recognition and relation extraction are focused areas of research in biomedical information extraction systems. Relation Extraction hinders the known relationship between Named Entities and in some way these are dependent on each other yet research also takes both these steps in an independent manner also. A lot of work has been done on biomedical named entity recognition focusing mostly on supervised and semi supervised solutions but very less attention work is done on unsupervised methods. Due to limited availability of annotated corpora the researchers now directed their efforts towards achievement of unsupervised named entity recognition systems. Named Entity Recognition from annotated corpora has been matured and there is very less margin for performance optimization. The challenge is still alive for the named entity recognition from unannotated corpora in all domains generally and for biological and biomedical domain specifically. Biomedical text exhibits relationships between different entities which are important for practitioners and researchers. Relation extraction is a significant area in biomedical knowledge, which has gained much importance in the last two decades. A lot of work has been done on biomedical relation extraction and identification focusing on two major areas: 1) rule based technique and 2) machine learning technique. In the last decade, focus has changed to hybrid approaches which have shown better results. This research presents an unsupervised named entity recognition framework along with a hybrid feature set for classification of relations between biomedical entities. Our Named Entity Recognition uses UMLS concepts and creates signatures that automate signature vectors. The vectorization of UMLS concepts ensures application of the framework in a generic way. Our framework differs with previous un-supervised methods in a way that we rely on UMLS for vector space creation instead of corpus statistics. The Relation Extraction approach uses bag of word feature, along with Natural Language Processing (NLP) to identify the noun and verb phrases and semantic features based on UMLS concepts. This hybrid feature set is a better representation of the relation extraction task. The main contribution in this hybrid features is the addition of semantic feature xi | P a g e set where verb phrases are ranked using Unified Medical Language System (UMLS), and a ranking algorithm is designed to get the most suitable concepts as features for the classifier. For Named Entity Recognition, we used Arizona Disease Corpus (AZDC) a gold standard corpus for this task. Our framework achieved accuracy of 72.56% which is competitive with supervised techniques on the same corpus. Our Relation Extraction approach has been validated on standard biomedical text corpus obtained from MEDLINE 2001, an accuracy of 96.19%, 97.45%, 96.49% and F-measure of 98.05%, 93.55%, 88.89% has been achieved for the cure, prevent and side effect relations respectively.Journals by Discipline
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