Objective
This research aimed to describe several areas in which AI could play a role in the development of Personalized Medicine and Drug Screening, and the transformations it has created in the field of biology and therapy. It also addressed the l
imitations faced by the application of artificial intelligence techniques and make suggestions for further research.
Methods
We have conducted a comprehensive review of research and papers related to the role of AI in personalized medicine and drug screening, and filtered the list of works for those relevant to this review.
Results
Artificial Intelligence can play an important role in the development of personalized medicines and drug screening at all clinical phases related to development and implementation of new customized health products, starting with finding the appropriate medicines to testing their usefulness. In addition, expertise in the use of artificial intelligence techniques can play a special role in this regard.
Discussion
The capacity of AI to enhance decision-making in personalized medicine and drug screening will largely depend on the accuracy of the relevant tests and the ways in which the data produced is stored, aggregated, accessed, and ultimately integrated.
Conclusion
The review of the relevant literature has revealed that AI techniques can enhance the decision-making process in the field of personalized medicine and drug screening by improving the ways in which produced data is aggregated, accessed, and ultimately integrated. One of the major obstacles in this field is that most hospitals and healthcare centers do not employ AI solutions, due to healthcare professionals lacking the expertise to build successful models using AI techniques and integrating them with clinical workflows.
For interpreting the behavior of a probabilistic model, it is useful to measure a model's calibration---the extent to which it produces reliable confidence scores. We address the open problem of calibration for tagging models with sparse tagsets, and
recommend strategies to measure and reduce calibration error (CE) in such models. We show that several post-hoc recalibration techniques all reduce calibration error across the marginal distribution for two existing sequence taggers. Moreover, we propose tag frequency grouping (TFG) as a way to measure calibration error in different frequency bands. Further, recalibrating each group separately promotes a more equitable reduction of calibration error across the tag frequency spectrum.
Mining the causes of political decision-making is an active research area in the field of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on su
rveys as the main source of predictors. However, the recent COVID-19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic---the pandemic. In this paper, we focus on the question of how public opinion influences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and comparisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.
Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation. To bridge this gap, we propose an SQL intermediate representation (IR) called Natural SQL (NatSQL). Specif
ically, NatSQL preserves the core functionalities of SQL, while it simplifies the queries as follows: (1) dispensing with operators and keywords such as GROUP BY, HAVING, FROM, JOIN ON, which are usually hard to find counterparts in the text descriptions; (2) removing the need of nested subqueries and set operators; and (3) making the schema linking easier by reducing the required number of schema items. On Spider, a challenging text-to-SQL benchmark that contains complex and nested SQL queries, we demonstrate that NatSQL outperforms other IRs, and significantly improves the performance of several previous SOTA models. Furthermore, for existing models that do not support executable SQL generation, NatSQL easily enables them to generate executable SQL queries, and achieves the new state-of-the-art execution accuracy.
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based
approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explici
t in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.
This study aims to study the extent of applying the two dimensions of the empowerment
employees strategy(employees participation in decision-making and the justice and equity
of the top management) in the public industrial companies in Latakia. thi
s study was
applied in The General Organization of tobacco, textile Latakia Company and the General
Company of cotton threads of Latakia, To achieve the purpose of study a questionnaire has
been made and distributed to research sample which is(310) workers of these companies,
(265) of questionnaires out of total sample is returned and ready for analysis by the
statistical program (spss) in order to implementing the study goals.
In addition to questionnaire, a several interviews were made with the managers and
employees to aware precisely the work environment.
the main result of this study was, the public industrial companies lacks to minimum
required elements of the implementation of the strategy and workers of these companies
isn't contented and satisfied with the work environment and that is, doesn't encourage or
support workers to take part in decision-making process related to their job, as well as
there is injustice in of the work systems followed by top management.
The purpose of this study is to examine the effect of the use of accounting information
systems on improving the quality of administrative decisions in a sample of banks in the
Syrian coast. The researchers distributed (100) questionnaires to manag
ers, heads of
departments, supervisors and administrative personnel responsible for taking various types
of decisions adopted on the accounting information in the banks in question. The number
of returned and valid questionnaires that were discharged was (77). The researchers analyzed
the data using the Statistical Analysis Program (SPSS 20).
The study aimed at uncovering the effect of the use of brainstorming strategy in the
improving the decision-making skills of the fourth grade students through their answers to
a test that measures the decision-making skill in the social studies. It
also aims to know the
differences between the middle grades, the students who learned according to the
brainstorming strategy and the pupils Who were taught according to the usual method.
Accordingly, a semi-experimental approach was adopted.
Abrainstorming strategy was designed for a unit of the fourth grade social studies
book, and a test was designed to measure decision- (10) phrases, each of which included a
problem to be decided on, and the educational program was applied to a sample of the
fourth grade students in the first class of basic education (103) students and students in the
school of martyr Yasser Kasu in the city of Jblah, The study shows that there is a
statistically significant difference between the average scores of the students of the two
groups (experimental and control) in the post-implementation and deferred to test the skill
of decision making, and this difference is in the interest of the students of the experimental
group.
The study suggested that the brainstorming strategy should be applied in the teaching of
the new curriculum, and studies should be carried out to reveal the impact of this strategy
on the development of different thinking skills and in most subjects.
The purpose of the research is to identify the correlation between selfaffirmation
and decision-making, and to find out the differences between
the average grade of university students and the members of the research
sample on the scale of self-affirmation and decision making according to
the gender variable and the academic specialization.
