319 lines
16 KiB
Ruby
319 lines
16 KiB
Ruby
class Cancerpredictfields
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require 'pathname'
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require 'json'
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include Mongoid::Document
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include Mongoid::Timestamps
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include OrbitModel::Status
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include OrbitModel::Impression
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# encoding: utf-8
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include OrbitTag::Taggable
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include OrbitCategory::Categorizable
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FIELDINFO = {"variable"=>"String","name"=>"String","is_num"=>"Fixnum","hint"=>"String","comment_text"=>"String","choice_fields"=>"Array","range"=>"Array","right"=>"Fixnum","is_float"=>"Fixnum","revert_value"=>"Fixnum","map_values"=>"Array","cancer_predict_mapping_file"=>"String"}
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NonLoclaized = ["variable","is_num","range","right","is_float","revert_value","map_values","cancer_predict_mapping_file"]
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field :title ,type:String ,default:""
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field :form_show , :type=> Hash ,default:{0=>{:variable=>"sex",:name=>{"zh_tw"=>"性別<br/>(Sex)","en"=>"Sex"},:is_num=>0, :hint=>{'zh_tw'=>'','en'=>''} , :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['男','女'],"en"=>['Male','Female']},:range=>[],:right=>0,:is_float=>0,:revert_value=>0,:map_values=>[],:cancer_predict_mapping_file=>""},
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1=>{:variable=>"age",:name=>{"zh_tw"=>"年齡<br/>(Age)","en"=>"Age"},:is_num=>1, :hint=>{'zh_tw'=>'從 20 歲(含)開始至 98 歲','en'=>'Age must be between 20 and 98'} , :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>[],"en"=>[]},:range=>[20,98],:right=>0,:is_float=>0,:revert_value=>0,:map_values=>[],:cancer_predict_mapping_file=>""},
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2=>{:variable=>"calcification_score",:name=>{"zh_tw"=>"鈣化指數<br/>(Calcification score)","en"=>"Calcification score"},:is_num=>1,:hint=>{'zh_tw'=>'請輸入0到5000的數字','en'=>'Please enter a number between 0 and 5000'}, :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>[],"en"=>[]},:range=>[0,5000],:right=>0,:is_float=>1,:revert_value=>0,:map_values=>[],:cancer_predict_mapping_file=>""}
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}
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field :form_show_in_result , :type=> Hash ,default:{}#{0=>{:variable=>"hormone_therapy",:name=>{"zh_tw"=>"賀爾蒙治療","en"=>"Hormone/Steroid therapy"},:is_num=>0, :hint=>{'zh_tw'=>'適用賀爾蒙受體陽性病人','en'=>'Hormone/ steroid therapy is available when ER status is positive'} , :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['否','是'],"en"=>['No','Yes']},:range=>[]},
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#1=>{:variable=>"Chemotherapy",:name=>{"zh_tw"=>"化學治療","en"=>"Chemotherapy"},:is_num=>0,:hint=>{'zh_tw'=>'','en'=>''}, :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['否','是'],"en"=>['No','Yes']},:range=>[]},
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#2=>{:variable=>"Radiotherapy",:name=>{"zh_tw"=>"放射治療","en"=>"Radiotherapy"},:is_num=>0,:hint=>{'zh_tw'=>'','en'=>''}, :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['否','是'],"en"=>['No','Yes']},:range=>[]},
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#3=>{:variable=>"Targeted_therapy",:name=>{"zh_tw"=>"標靶治療","en"=>"Targeted therapy"},:is_num=>0,:hint=>{'zh_tw'=>'抗HER2治療','en'=>''}, :comment_text=>{'zh_tw'=>'','en'=>''}, :choice_fields=> {"zh_tw"=>['否','是'],"en"=>['No','Yes']},:range=>[]}
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#}
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field :form_result_is_right , :type=> Integer ,default: 0
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field :text_descibe ,type:Hash ,default:{"zh_tw"=>"歡迎使用台灣心血管疾病預後預測系統<br />本預測系統由全民健保資料庫2017年~2020年間共1964位病人電腦斷層影像所建立之預測模型<br />請在下方填入相關資訊","en"=>"Welcome to the Taiwan Breast Cancer Prediction System!<br/>The prediction system is constructed using clinical data from 90,841 breast cancer patients in the Taiwan Cancer Registry database between 2011 to 2015, and validated using clinical data from 49,374 breast cancer patients in the U.S.-based Surveillance, Epidemiology and End Results (SEER) database. <br/>To start, please select the information below."}
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field :small ,type:Hash ,default:{'font_size'=>"0.825em",'active'=>0}
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field :medium ,type:Hash ,default:{'font_size'=>"1em",'active'=>1}
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field :large ,type:Hash ,default:{'font_size'=>"1.25em",'active'=>0}
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field :head_images_id ,type:Array , default: []
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field :title_images_id ,type:Array , default: []
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field :title_texts ,type:Hash ,default:{'zh_tw'=>'臺灣心血管疾病存活預測','en'=>'Cardiovascular Disease Survival Forecast in Taiwan'}
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field :table_above_texts ,type:Hash ,default:{'zh_tw'=>"下表之分析為針對手術後病人，根據選定的術後治療，分別估計在半年、一年及一年半的再住院或死亡機率。",'en'=>'The analysis is for women who had undergone surgery.The table shows the 0.5-, 1- and 1.5-year survival rates,based on the treatment you have selected.'}
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field :text_above_texts ,type:Hash ,default:{'zh_tw'=>"此研究分析來自於照射胸部電腦斷層所得之結果，根據您所輸入的資訊，在第{{years}}年內，有2.69%的機率可能再住院或死亡{{Surgery_only}}%。",'en'=>'此研究分析來自於照射胸部電腦斷層所得之結果，根據您所輸入的資訊，在第{{years}}年內，有2.69%的機率可能再住院或死亡{{Surgery_only}}%。'}
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field :surgery_only_texts ,type:Hash ,default:{'zh_tw'=>'','en'=>''}
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field :extra_texts ,type:Hash ,default:{'zh_tw'=>'，此外','en'=>''}
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field :extra_therapy_texts ,type:Hash ,default:{'zh_tw'=>'100 位在術後有接受{{extra_therapy}}的婦女中，有{{survival_num}}位婦女，術後{{surgery_year}}年仍為存活(多了{{Additional_Benefit}}位)','en'=>'{{survival_num}} out of 100 women treated with {{extra_therapy}} are alive (an extra {{Additional_Benefit}})'}
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field :danger_texts ,type:Hash ,default:{'zh_tw'=>'請注意紅框的輸入資料是否符合要求!','en'=>'Please check whether input data in red blocks are correct!'}
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field :years ,type:Array ,default:[1,1.5,2,2.5]
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field :texts_between_Result_and_result_block ,type:Hash ,default:{'zh_tw'=>'如果欲將預測結果應用於臨床上，請務必與您的主治醫師討論後再做最後決定。','en'=>'Please note that the patients need to consult with their medical doctors before making any decision.'}
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#field :image_uploader ,type:Object
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field :prediction_formula , type: String ,default: "A = 0.1327868* (sex_value- 0.4858824)
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+ 0.0371720* (age_test1 - 61.56000) -0.07447278* (age_test2 - 13.10152)
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+ 0.4315686* (age_test3 - 0.9844332)
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+ 0.0009163615*( calH_test1 - 182.9347)
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-0.0007536899*( calH_test2 - 124.8706) -0.00004697183*( calH_test3 -80.75636)
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+ 0.0001401325*( calAH_test1 - 700.7824)
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-0.001349783*( calAH_test2 - 634.2167) +0.001753832*( calAH_test3 -419.3361)
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+ 0.0001906046*( calDH_test1 -835.2894) -0.000251567*( calDH_test2 - 213.1630)
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-0.002173942*( fat_test1 -108.4149)+0.003066541*( fat_test2 - 28.33497)
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+0.6700708*(N4-0.3241176)
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+0.3336162*(O3-0.4994118)
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+0.1322476*(O20-0.1741176)
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+0.9084972*(O18-0.008823529)
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+0.2978388*(N12-0.1152941)
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+0.1777935*(N20-0.3582353)
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+1.588042*(N31-0.002352941)
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+0.2197419*(O6-0.07823529)
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+1.791159*(N34-0.001176471)
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+0.4305973*(N14-0.02176471)
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-0.4472885*(N29-0.02411765)
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+0.2601319*(N26-0.04941176)
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-0.2364269*(O11-0.1164706)
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+0.1784179*(N6-0.1070588)
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+0.6023170*(O14-0.01294118)
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-1.031959*(N43-0.007058824)
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+0.4257809*(O17-0.01823529)
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+0.2002546*(O9-0.06176471)"
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field :years_settings , type: Array , default: ["0.8095037^( exp(A) )","0.729158^( exp(A) )","0.6717211^( exp(A) )","0.6056773^( exp(A) )"]
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field :tmp_years_settings , type: Array , default: []
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field :tmp_years_settings_for_ruby , type: Array , default: []
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field :lpv_calc, type: Hash, default: {} #for js code
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field :tmp_lpv_ruby_code, type: String, default: ""
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field :tmp_lpv_variables, type: Array, default: []
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field :mapping_data_from_csv , type: String ,default: ""
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scope :can_display, ->{where(:is_hidden=>false,:is_preview => false).any_of({:postdate.lt=>Time.now, :deadline.gt=>Time.now},{:postdate.lt=>Time.now, :deadline=>nil}).order_by([:is_top, :desc],[:postdate, :desc])}
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scope :is_approved, ->{where(:approved => true)}
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#before_create :set_expire
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before_save do
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self.form_show.each do |num,property|
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property[:need_map_values] = (property[:map_values].class == Array && property[:choice_fields].class == Array && property[:map_values].length == property[:choice_fields].length) ? 1 : 0
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end
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result_keys = []
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self.form_show.each do |num,property|
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variable_name = property[:variable]
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if variable_name.present?
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result_keys << variable_name
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end
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end
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mapping_data = JSON.parse(self.mapping_data_from_csv) rescue {}
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if mapping_data.present?
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mapping_data.each do |k,v|
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result_keys += (v.keys rescue [])
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end
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end
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formula = self.prediction_formula.gsub("\r\n"," ").gsub("^","**")
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result_keys.each do |k|
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formula = formula.gsub(/#{k}?(-|\+|\*|\/|\s|\=)/){ "result[\"#{k}\"]#{$1}" }
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end
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formula = formula.split(/^([^=]+)=([^=])/).select{|s| s.present?}.each_slice(2).map do |a,b|
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if b
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("@"+ a + "=" + b)
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else
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"@"+ a
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end
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end.join("\n")
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self.tmp_lpv_ruby_code = formula
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formula_variables = formula.enum_for(:scan,/([^\=\(\)]+)?=[^=]/).map {|x| x[-1] }.compact.map{|s| s.strip}
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self.tmp_lpv_variables = formula_variables
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self.tmp_years_settings = self.years_settings.map do |s|
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s.gsub('^','**').gsub('exp','Math.exp')
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end
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self.tmp_years_settings_for_ruby = self.tmp_years_settings.clone
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formula_variables.each do |variable_name|
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self.tmp_years_settings_for_ruby = self.tmp_years_settings_for_ruby.map do |y|
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y.gsub(variable_name[1..-1],variable_name)
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end
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end
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self.lpv_calc = get_years_settings_dict
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self.generate_eval_formula
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end
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def generate_eval_formula
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eval_formula = "def eval_formula(result); #{self.tmp_lpv_ruby_code}; end"
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CancerpredictsController.module_eval(eval_formula)
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end
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def update_user
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User.find(update_user_id) rescue nil
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end
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def update_user=(user)
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self.update_user_id = user.id
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end
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def email_members
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MemberProfile.find(self.email_member_ids) rescue []
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end
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def email_addresses
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addresses = self.email_members.collect{|member| member.email} rescue []
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addresses = addresses +[self.other_mailaddress] if !self.other_mailaddress.blank?
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addresses.flatten
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end
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def email
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mail = Email.find(self.email_id) rescue nil
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end
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def expired?
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(self.deadline < Time.now) rescue false
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end
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def destroy_email
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mail = Email.find(self.email_id) rescue nil
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mail.destroy if !mail.nil?
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end
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def self.remove_expired_status
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self.where(:is_top => true, :top_end_date.ne => nil, :top_end_date.lt => Time.now).each do |b|
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b.is_top = false
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b.top_end_date = nil
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b.save
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end
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end
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def generate_jscode
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js_code = "var map_values , mapping_hash , temp_index ,temp_value , index , closest_value;\r\n"
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mapping_data_from_csv = YAML.load(self.mapping_data_from_csv) rescue {}
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variable_keys = []
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self.form_show.each do |num,property|
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@variable = property[:variable]
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if @variable.present?
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if property[:is_num] == 1
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js_code += "\t\t\t\tresult['#{@variable}'] = Number(result_json['#{@variable}']);\r\n"
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elsif property[:choice_fields].present?
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if property[:map_values].class == Array && property[:choice_fields].class == Array && property[:map_values].length == property[:choice_fields].length
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js_code += "\t\t\t\tmap_values = #{property[:map_values]};\r\n"
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js_code += "\t\t\t\tresult['#{@variable}'] = map_values[Number(result_json['#{@variable}'']) - 1];\r\n"
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else
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if property[:revert_value] != 1
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js_code += "\t\t\t\tresult['#{@variable}'] = Number(result_json['#{@variable}']) - 1;\r\n"
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else
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js_code += "\t\t\t\tresult['#{@variable}'] = (#{property[:choice_fields].length} - Number(result_json['#{@variable}']));\r\n"
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end
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end
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end
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variable_keys.push(@variable)
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if property[:cancer_predict_mapping_file].present?
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if (mapping_data_from_csv != {} && !mapping_data_from_csv[@variable].blank?)
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variable_keys.concat(mapping_data_from_csv[@variable].keys)
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js_code += "\t\t\t\tmapping_hash = mapping_data_from_csv['#{@variable}'];\r\n"
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js_code += "\t\t\t\ttemp_index = 0;\r\n"
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js_code += "\t\t\t\ttemp_value = result[#{@variable}];\r\n"
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js_code += "\t\t\t\tindex = 0;
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$.each(mapping_hash,function(k,v){
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if( i == 0 ){
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var index_val = v.indexOf(temp_value);
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if( index_val != -1 ){
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temp_index = index_val;
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}else{
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closest_value = v.get_nearest_value(temp_value);
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temp_index = v.indexOf(closest_value)
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}
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}
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result[k] = v[temp_index];
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index++;
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});\r\n"
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end
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end
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end
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end
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formula = self.prediction_formula.gsub("\r\n"," ").gsub("^","**")
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variable_keys.each do |k|
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formula = formula.gsub(/#{k}?(-|\+|\*|\s|\=)/){ "result[\"#{k}\"]#{$1}" }
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end
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formula_variables = formula.enum_for(:scan,/([^\=]*)?=/).map { ::Regexp.last_match[1] }.map{|s| s.strip.split(/(\s*|;\r\n)/).last}
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js_code = "function calculate_first_lpv(result_json){
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result = {};
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#{js_code}
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try{
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result['lpv'] = (#{formula});
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}catch(e){result['lpv'] = \"error\"};
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console.log(result['lpv']);
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#{formula_variables.map{|v| "result['lpv_variable']['#{v}'] = #{v};"}.join("\r\n\t\t\t\t") }
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return result;
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};
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function calculate_and_change_result_value(obj){
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obj.servive_ratio_arr = [];
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for(var i = 0;i<obj.active_treatment.length;i++){
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var servive_ratio = round((1-(calculate_servive_ratio(obj.year,obj.lpv_real[i])))*100,2);
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var benefit = servive_ratio - obj.servive_ratio_arr[obj.servive_ratio_arr.length-1];
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obj.servive_ratio_arr.push(servive_ratio);
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$('tr.'+obj.active_treatment[i]+' td.Overall_Survival').html(servive_ratio+'%');
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$('.'+obj.active_treatment[i]+'.Overall_Survival').html(servive_ratio);
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if(i != 0){
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$('tr.'+obj.active_treatment[i]+' td.Additional_Benefit').html(round(benefit,2)+'%');
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$('.'+obj.active_treatment[i]+'.Additional_Benefit').html(Math.round(benefit));
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}
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}
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//$('.'+obj.active_treatment[0]+'.Overall_Survival').html(Math.round(obj.servive_ratio_arr[0]));
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};"
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@years = self.years
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switch_texts = "
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#{formula_variables.map{|v| "var #{v} = obj['#{v}'];"}.join("\r\n\t\t\t\t")}
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switch(year) {"
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@years.each do |year|
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year_index = @years.index(year)
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switch_texts +=
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"
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case '#{year}':
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servive_ratio = #{self.tmp_years_settings[year_index]};
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break;
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"
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end
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switch_texts += "
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default:
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console.log('not found year.');
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}"
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js_code = js_code +"
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function calculate_servive_ratio(year,obj){
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var servive_ratio;
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#{switch_texts}
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return servive_ratio;
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}
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"
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return js_code
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end
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def auto_write_predict_js
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js_codes = generate_jscode
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module_app_path = Pathname.new(File.expand_path(__dir__)).dirname.dirname.to_s
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save_path = module_app_path + '/app/assets/javascripts/cancer_predict.js'
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file_texts = File.read(save_path)
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str1 = "/* auto add start */"
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index1 = file_texts.index(str1)
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str2 = "/* auto add end */"
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index2 = file_texts.index(str2)
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str3 = "/*lpv_calc_formula_start*/"
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index3 = file_texts.index(str3)
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str4 = "/*lpv_calc_formula_end*/"
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index4 = file_texts.index(str4)
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file_texts = file_texts.sub(file_texts[(index1+str1.length+2) .. (index2 - 1)] , js_codes)
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file_texts = file_texts.sub(file_texts[(index3+str3.length) .. (index4 - 1)] , self.lpv_calc.to_json.gsub("@","."))
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if (!index1.nil? && !index2.nil?) || (!index3.nil? && !index4.nil?)
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File.write(save_path,file_texts)
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end
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end
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def get_years_settings_dict
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lpv_variable_name = (prediction_formula.include?("=") ? prediction_formula.split("=")[0].strip : "" rescue "")
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res = self.years.map.with_index do |y, i|
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tmp_formula = self.tmp_years_settings[i]
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if lpv_variable_name.present?
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tmp_formula = tmp_formula.gsub(lpv_variable_name, "lpv_current")
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else
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tmp_formula = tmp_formula.gsub("lpv", "lpv_current")
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end
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[y.to_s.sub(".","@"), tmp_formula]
|
||
end
|
||
res.to_h
|
||
end
|
||
end |