The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.

在这项研究中，要求各种印度生物的听众倾听并认识到美国扬声器所说的速度话语。我们识别出一个话语时，我们有三种来自每个听众的回应：1。句子难度评级，2.扬声器难度评级，以及讲话的转录。从这些转录中，计算并用作标准以评估识别和原始句子之间的相似性。本研究中选择的句子分为三组：简单，中和硬，基于此研究它们中的单词的频率。我们观察到句子，扬声器难度评级和行动从易于难以句子的句子增加。我们还使用以下三种自动语音识别（ASR）进行人类语音识别性能，在声学模型（AM）和语言模型（LM）（LM）（LM）：ASR1）训练中，录制了印度源头和LM的录音Timit Text，ASR2）我正在使用来自Libli语音语料库的本地美国扬声器和LM的录音，以及ASR3）正在使用来自美国原住民扬声器和LM构建的录音在Libli语音和Timit文本上。我们观察到HSR性能类似于ASR1的性能，而ASR3则实现最佳性能。扬声器诞生明智的分析表明，与少数其他生命神相比，印度听众的扬声器的话语更难以识别